research articles on logistics

• Browse All Articles
• Newsletter Sign-Up

Logistics →

• 25 Apr 2023

How SHEIN and Temu Conquered Fast Fashion—and Forged a New Business Model

The platforms SHEIN and Temu match consumer demand and factory output, bringing Chinese production to the rest of the world. The companies have remade fast fashion, but their pioneering approach has the potential to go far beyond retail, says John Deighton.

• 18 Oct 2022
• Cold Call Podcast

Chewy.com’s Make-or-Break Logistics Dilemma

In late 2013, Ryan Cohen, cofounder and then-CEO of online pet products retailer Chewy.com, was facing a decision that could determine his company’s future. Should he stay with a third-party logistics provider (3PL) for all of Chewy.com’s e-commerce fulfillment or take that function in house? Cohen was convinced that achieving scale would be essential to making the business work and he worried that the company’s current 3PL may not be able to scale with Chewy.com’s projected growth or maintain the company’s performance standards for service quality and fulfillment. But neither he nor his cofounders had any experience managing logistics, and the company’s board members were pressuring him to leave order fulfillment to the 3PL. They worried that any changes could destabilize the existing 3PL relationship and endanger the viability of the fast-growing business. What should Cohen do? Senior Lecturer Jeffrey Rayport discusses the options in his case, “Chewy.com (A).”

• 12 Jul 2022

Can the Foodservice Distribution Industry Recover from the Pandemic?

At the height of the pandemic in 2020, US Foods struggled, as restaurant and school closures reduced demand for foodservice distribution. The situation improved after the return of indoor dining and in-person learning, but an industry-wide shortage of truck drivers and warehouse staff hampered the foodservice distributor’s post-pandemic recovery. That left CEO Pietro Satriano to determine the best strategy to attract and retain essential workers, even as he was tasked with expanding the wholesale grocery store chain (CHEF’STORE) that US Foods launched during the pandemic lockdown. Harvard Business School Professor David E. Bell explores how post-pandemic supply chain challenges continue to affect the foodservice distribution industry in his case, “US Foods: Driving Post-Pandemic Success?”

• 05 Jul 2022
• What Do You Think?

Have We Seen the Peak of Just-in-Time Inventory Management?

Toyota and other companies have harnessed just-in-time inventory management to cut logistics costs and boost service. That is, until COVID-19 roiled global supply chains. Will we ever get back to the days of tighter inventory control? asks James Heskett. Open for comment; 0 Comments.

• 19 Oct 2021
• Research & Ideas

Fed Up Workers and Supply Woes: What's Next for Dollar Stores?

Willy Shih discusses how higher costs, shipping delays, and worker shortages are putting the dollar store business model to the test ahead of the critical holiday shopping season. Open for comment; 0 Comments.

• 26 Mar 2014

How Electronic Patient Records Can Slow Doctor Productivity

Electronic health records are sweeping through the medical field, but some doctors report a disturbing side effect. Instead of becoming more efficient, some practices are becoming less so. Robert Huckman's research explains why. Open for comment; 0 Comments.

• 11 Nov 2013
• Working Paper Summaries

Increased Speed Equals Increased Wait: The Impact of a Reduction in Emergency Department Ultrasound Order Processing Time

This study of ultrasound test orders in hospital emergency departments (EDs) shows that, paradoxically, increasing capacity in a service setting may not alleviate congestion, and can actually increase it due to increased resource use. Specifically, the study finds that reducing the time it takes to order an ultrasound counter intuitively increases patient throughput time as a result of increased ultrasound use without a corresponding increase in quality of care. Furthermore, the authors show that in the complex, interconnected system or hospitals, changes in resource capacity affects not only the patients who receive the additional resources, but also other patients who share the resource, in this case, radiology. These results highlight how demand can be influenced by capacity due to behavioral responses to changes in resource availability, and that this change in demand has far reaching effects on multiple types of patients. Interestingly, the increased ultrasound ordering capacity was achieved by removing what appeared to be a "wasteful" step in the process. However, the results suggest that the step may not have been wasteful as it reduced inefficient ultrasound orders. In healthcare, these results are very important as they provide an explanation for some of the ever-increasing costs: reducing congestion through increased capacity results in even more congestion due to higher resource use. Overall, the study suggests an operations-based solution of increasing the cost/difficulty of ordering discretionary but sometimes low-efficacy treatments to address the rise in healthcare spending. Therefore, to improve hospital performance it could be optimal to put into place "inefficiencies" to become more efficient. Key concepts include: A process improvement can inadvertently cause an increase in demand for a service as well as associated shared resources, which results in congestion, counter intuitively decreasing overall system performance. While individual patients and physicians may benefit from the reduced processing time, there can be unintended consequences for overall system performance. Closed for comment; 0 Comments.

• 25 Jan 2013

Why a Harvard Finance Instructor Went to the Kumbh Mela

Every 12 years, millions of Hindu pilgrims travel to the Indian city of Allahabad for the Kumbh Mela, the largest public gathering in the world. In this first-person account, Senior Lecturer John Macomber shares his first impressions and explains what he's doing there. Closed for comment; 0 Comments.

• 07 Aug 2012

Off and Running: Professors Comment on Olympics

The most difficult challenge at The Olympics is the behind-the-scenes efforts to actually get them up and running. Is it worth it? HBS professors Stephen A. Greyser, John D. Macomber, and John T. Gourville offer insights into the business behind the games. Open for comment; 0 Comments.

• 19 Oct 2010

The Impact of Supply Learning on Customer Demand: Model and Estimation Methodology

"Supply learning" is the process by which customers predict a company's ability to fulfill product orders in the future using information about how well the company fulfilled orders in the past. A new paper investigates how and whether a customer's assumptions about future supplier performance will affect the likelihood that the customer will order from that supplier in the future. Research, based on data from apparel manufacturer Hugo Boss, was conducted by Nathan Craig and Ananth Raman of Harvard Business School, and Nicole DeHoratius of the University of Portland. Key concepts include: Two key measures of supplier performance include "consistency", which is the likelihood that a company will continue to keep items in stock and meet demand, and "recovery", which is the likelihood that a company will deliver on time in spite of past stock-outs. Improvements in consistency and recovery are associated with increases in orders from retail customers. Increasing the level of service may lead to an increase in orders, even when the service level is already nearly perfect. Closed for comment; 0 Comments.

• 19 Jul 2010

How Mercadona Fixes Retail’s ’Last 10 Yards’ Problem

Spanish supermarket chain Mercadona offers aggressive pricing, yet high-touch customer service and above-average employee wages. What's its secret? The operations between loading dock and the customer's hands, says HBS professor Zeynep Ton. Key concepts include: The last 10 yards of the supply chain lies between the store's loading dock and the customer's hands. Poor operational decisions create unnecessary complications that lead to quality problems and lower labor productivity and, in general, make life hard for retail employees. Adopting Mercadona's approach requires a long-term view and a leader with a strong backbone. Closed for comment; 0 Comments.

• 12 Jul 2010

Rocket Science Retailing: A Practical Guide

How can retailers make the most of cutting-edge developments and emerging technologies? Book excerpt plus Q&A with HBS professor Ananth Raman, coauthor with Wharton professor Marshall Fisher of The New Science of Retailing: How Analytics Are Transforming the Supply Chain and Improving Performance. Key concepts include: Retailers can better identify and exploit hidden opportunities in the data they generate. Integrating new analytics within retail organizations is not easy. Raman outlines the typical barriers and a path to overcome them. Incentives must be aligned within organizations and in the supply chain. The first step is to identify the behavior you want to induce. To attract and retain the best employees, successful retailers empower them in specific ways. Closed for comment; 0 Comments.

• 05 Jul 2006

The Motion Picture Industry: Critical Issues in Practice, Current Research & New Research Directions

This paper reviews research and trends in three key areas of movie making: production, distribution, and exhibition. In the production process, the authors recommend risk management and portfolio management for studios, and explore talent compensation issues. Distribution trends show that box-office performance will increasingly depend on a small number of blockbusters, advertising spending will rise (but will cross different types of media), and the timing of releases (and DVDs) will become a bigger issue. As for exhibiting movies, trends show that more sophisticated exhibitors will emerge, contractual changes between distributor and exhibitors will change, and strategies for tickets prices may be reevaluated. Key concepts include: Business tools such as quantitative and qualitative research and market research should be applied to the decision-making process at earlier stages of development. Technological developments will continue to have unknown effects on every stage of the movie-making value chain (production, distribution, exhibition, consumption). Closed for comment; 0 Comments.

• 20 Dec 2004

How an Order Views Your Company

HBS Professors Benson Shapiro and Kash Rangan bring us up to date on their pioneering research that helped ignite today’s intense focus on the customer. The key? Know your order cycle management. Closed for comment; 0 Comments.

• 15 Apr 2002

In the Virtual Dressing Room Returns Are A Real Problem

That little red number looked smashing onscreen, but the puce caftan the delivery guy brought is just one more casualty of the online shopping battle. HBS professor Jan Hammond researches what the textile and apparel industries can do to curtail returns. Closed for comment; 0 Comments.

• 26 Nov 2001

How Toyota Turns Workers Into Problem Solvers

Toyota's reputation for sustaining high product quality is legendary. But the company's methods are not secret. So why can't other carmakers match Toyota's track record? HBS professor Steven Spear says it's all about problem solving. Closed for comment; 0 Comments.

• 19 Nov 2001

Wrapping Your Alliances In a World Wide Web

HBS professor Andrew McAfee researches how the Internet affects manufacturing and productivity and how business can team up to get the most out of technology. Closed for comment; 0 Comments.

• 22 Jan 2001

Control Your Inventory in a World of Lean Retailing

"Manufacturers of consumer goods are in the hot seat these days," the authors of this Harvard Business Review article remind readers. But there is no need to surrender to escalating costs of inventories. In this excerpt, they describe one new way to help lower inventory costs. Closed for comment; 0 Comments.

• 12 Oct 1999

Decoding the DNA of the Toyota Production System

How can one production operation be both rigidly scripted and enormously flexible? In this summary of an article from the Harvard Business Review, HBS Professors H. Kent Bowen and Steven Spear disclose the secret to Toyota's production success. The company's operations can be seen as a continuous series of controlled experiments: whenever Toyota defines a specification, it is establishing a hypothesis that is then tested through action. The workers, who have internalized this scientific-method approach, are stimulated to respond to problems as they appear; using data from the strictly defined experiment, they are able to adapt fluidly to changing circumstances. Closed for comment; 0 Comments.

Rapid Response: Inside the Retailing Revolution

A simple bar code scan at your local department store today launches a whirlwind of action: data is transmitted about the color, the size, and the style of the item to forecasters and production planners; distributors and suppliers are informed of the demand and the possible need to restock. All in the blink of an electronic eye. It wasn’t always this way, though. HBS Professor Janice Hammond has focused her recent research on the transformation of the apparel and textile industries from the classic, limited model to the new lean inventories and flexible manufacturing capabilities. Closed for comment; 0 Comments.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 10 August 2024

The impact of international logistics performance on import and export trade: an empirical case of the “Belt and Road” initiative countries

• Weixin Wang 1 ,
• Qiqi Wu 2 ,
• Jiafu Su   ORCID: orcid.org/0000-0002-6001-5744 3 &
• Bing li 2  

Humanities and Social Sciences Communications volume  11 , Article number:  1028 ( 2024 ) Cite this article

211 Accesses

Metrics details

• Business and management

As an important foundation of cargo transportation, logistics plays a vital role in developing international trade. Based on the international logistics performance index (LPI) and the sample data of the “Belt and Road” initiative countries from 2011 to 2022, this paper uses the extended trade gravity model to explore the impact of the logistics performance of the “Belt and Road” initiative countries on China’s import and export trade. The empirical results show that the improvement of the logistics performance level of the countries along the “Belt and Road” Initiative has a certain role in promoting the growth of China’s trade volume to the country, and the improvement of LPI has a more significant positive impact on China’s import and export to large-scale countries along the route. Finally, according to the analysis of empirical results, this paper puts forward specific suggestions to promote the development of logistics performance and import and export trade, which provides some reference value for implementing the “Belt and Road” initiative and improving national logistics and trade level.

Similar content being viewed by others

A study on the evaluation of competitiveness in the aviation logistics industry cluster in Zhengzhou

Ports’ criticality in international trade and global supply-chains

How business environment of countries along the Belt and Road impacts China’s OFDI efficiency: a stochastic frontier gravity model approach

Introduction.

In the context of 'globalization', China and the countries along the “Belt and Road” Initiative have ushered in a high-speed development stage of comprehensive cooperation (Khan et al. 2022a ), working together to promote a higher level of trade facilitation development trend and promote the further expansion of trade scale (Khan et al. 2022b ). The proportion of the trade scale between China and the “Belt and Road” initiative countries in China’s overall foreign trade continues to grow (Mena et al. 2022 ), reaching 34.7% by 2022. The trade between China and the “Belt and Road” initiative countries is mainly concentrated in Southeast Asia, focusing on West Asia and South Asia (Qin 2022 ). From an overall perspective, in 2022, China’s trade with ASEAN accounted for 50.3% of the trade of the “Belt and Road” initiative countries. And the trade imbalance between China and the “Belt and Road” initiative countries is becoming more and more serious (Wang and Liu 2022 ).

Since 2007, the World Bank has published the “Logistics Performance Index Report” every two years to measure the level of logistics in countries worldwide with the LPI (Hausman et al. 2013 ). The progress of the logistics industry drives the development of manufacturing, finance, and other industries, promotes the coordinated development of upstream and downstream enterprises, and promotes the formation of a complete industrial chain and supply chain (Göçer et al. 2022 ). The logistics performance of the “Belt and Road” initiative countries has become an important factor in promoting import and export between China and the “Belt and Road” initiative countries (Pan et al. 2022 ). With the continuous advancement of the 'economic integration' process, logistics connects the needs of trade between countries, is an essential guarantee for the smooth operation of the supply chain (Yang 2023 ), and plays a vital role in the country’s economic development (Yingfei et al. 2022 ). Therefore, the research on the relationship between international logistics performance and the development of import and export trade has certain practical significance.

Due to the incalculable impact of the 2008 financial crisis on the world economy, this paper selects 2011–2022 as the time interval for research to avoid the effect on the accuracy of empirical results. Combined with data availability, taking 61 countries along the Belt and Road Initiative as an example, this paper profoundly analyzes the relationship between international logistics performance and national import and export trade. It systematically analyzes the impact of different indicators of international logistics performance on international trade import and export and more accurately controls the adjustment direction of resource allocation to provide some reference value for the implementation of the “Belt and Road” initiative of China and provide some reference for the balance of national logistics system and trade relations.

Compared with previous studies, the innovation of this paper is mainly reflected in the following aspects: Firstly, the interaction mechanism between international logistics performance and import and export trade is analyzed. This paper analyzes the interaction mechanism between international logistics performance and import and export trade through previous data collection. Secondly, the fixed effect model is used to test the impact of the improvement of the logistics performance level of the countries along the “Belt and Road” on the trade volume, and to explore the impact of international logistics performance on the import and export of countries of different sizes and the differences. Thirdly, the relationship between international logistics performance and import and export trade is tested by using the methods of 'eliminating outlier samples', 'reducing sample interval', 'shrinking tail processing' and 'lagging one period of explained variables', and the conclusions are summarized, and specific and feasible development suggestions are put forward.

The research contributions of this paper can be summarized as follows: Firstly, it further enriches the research on international logistics performance and import and export trade. The research of related fields is mainly related to the relationship between logistics performance and export. This paper takes import and export as the research object, which has a specific reference value for the overall development of the country and the region. Secondly, this paper not only explores the relationship between international logistics performance and import and export trade but also divides different countries according to their scale and explores the various impacts on countries of different scales, which provides a specific reference for the direction of national logistics and trade investment. Thirdly, this paper discusses the relationship between international logistics performance and the development of import and export trade, and puts forward specific suggestions to promote the improvement of international logistics performance and trade volume, which helps provide some reference value for the development of related fields.

The section 2 reviews the literature on logistics performance and international trade, and provides some theoretical support for the article. Section 3 explains the construction of the gravity model data sources, the variables, and the division of the national scale. In section 4, descriptive statistics, LPI comprehensive index regression, and LPI sub-index regression are used, and systematic analysis is carried out according to the results of empirical data. Section 5 conducts a robustness test. Section 6 summarizes the conclusions and puts forward specific suggestions based on the empirical analysis.

Literature review

Logistics performance is the key to supporting trade growth and the main factor determining a country’s economic growth (Cui et al. 2022 ). Bhukiya and Patel ( 2023 ) and Huong et al. ( 2024 ) believed that logistics performance promotes international trade. Barakat et al. ( 2023 ) demonstrated that the improvement of logistics performance helps to increase national trade openness and reduce trade costs. Jayathilaka et al. ( 2022 ) analyzed the impact of gross domestic product (GDP) and LPI on international trade based on 142 countries, and verified the positive role of LPI in promoting international trade, which is more significant in Asia, Europe and Oceania. Çelebi ( 2019 ) believes that logistics performance will promote the development of trade, and the efficiency of logistics system is an important factor affecting bilateral trade. Based on the sample data of 10 countries along the China-Europe Express from 2015 to 2019, Zhong and Zhou ( 2022 ) demonstrated that the improvement of international logistics performance has promoted the increase of import and export trade in Guangdong Province. Liu ( 2022 ) selected the data of 12 provinces and regions in western China from 2015 to 2020 to explore the impact of cross-border logistics performance on the competitiveness of cross-border agricultural products trade, and found that the development of cross-border logistics is conducive to improving the development of cross-border agricultural products trade in western China. The above research shows that: in the context of economic integration, the development of international logistics performance has promoted the improvement of the international trade environment and improved the convenience of international trade. There is a positive correlation between international logistics performance and international trade.

Import and export trade has a certain feedback effect on the development of logistics industry (Yang 2010 ). Guo ( 2018 ) used the panel data of 31 provinces in China from 1997 to 2016 to empirically test the role of import and export trade in promoting the development of the logistics industry, and the impact of exports on the development of the logistics industry is significantly greater than the role of imports. At the same time, due to differences in geographical location and resource endowments, only imports in the central and eastern regions of China promote the development of the logistics industry, and the western region is not significant. Zhan et al. ( 2019 ) found that the scale effect, export efficiency effect and export structure effect of export trade in the core area of the “Belt and Road” have promoted the development of the logistics industry. Wang and Wang ( 2021 ) found that the trade in the core area of “Belt and Road” can promote the growth and agglomeration of the logistics industry, and the export scale effect is the main factor to promote the growth of the logistics industry. The expansion of international trade scale, the improvement of trade efficiency and the improvement of trade structure have also promoted the improvement of international logistics performance. Based on the co-integration model of time series data from 1989 to 2012, Wang ( 2015 ) found that there is a co-integration relationship between logistics development and energy consumption, foreign trade and urbanization level, and this co-integration relationship has a long stability. Yang et al. ( 2019 ) found that the logistics development between China and ASEAN countries is the reason that affects the development of each other’s trade through the Granger causality test. Guo et al. ( 2018 ) studied the development of China’s logistics industry and foreign trade in the past 40 years of reform and opening up, and found that there is a long-term and stable coordinated development relationship between the two. In order to promote the sustainable development of the two, it can be achieved by optimizing the business environment, promoting the support of the coordinated development of modern logistics and foreign trade, improving the quality of logistics infrastructure and customs operation efficiency, and accelerating the informatization and standardization of logistics industry.

There are some differences in the impact of international logistics performance on countries with different income levels, different trade facilitation levels, and different population sizes (Fan and Yu 2015 ). See et al. ( 2024 ) found that countries with higher income levels have better logistics performance. Çelebi ( 2019 ) believes that income level is an important factor in the impact of logistics performance on trade volume. Trade facilitation will have different effects according to per capita income level, and low-income economies with higher logistics level will gain more benefits than high-income economies. Compared with the increase of logistics level in low-income countries, the increase of trade volume will be promoted, and the import volume of middle and high-income countries will benefit more from the improvement of logistics performance. Kumari and Bharti ( 2021 ) studied the impact of country size on trade and logistics performance based on population size, and found that the degree of LPI to improve related trade growth is the highest in medium-sized countries, followed by small-scale countries. Among the sub-indicators of LPI, cargo tracking ability and timeliness have the greatest impact on the trade of small-scale countries, and the convenience and timeliness of arranging international freight transportation have the greatest impact on medium-sized countries.

In summary, with the deepening of the globalization of the supply chain and industrial chain, import and export trade are moving towards lower cost and higher efficiency. International logistics performance and import and export trade promote each other and jointly drive national economic development. The impact of international performance on the trade of different countries has certain differences. However, there are still few studies on the impact of logistics performance on the trade of countries of different sizes. Therefore, this paper divides the “Belt and Road” initiative countries according to population size, further explores the impact of international logistics performance on import and export trade, and provides a reference for the development of the “Belt and Road” initiative countries and the trade between nations.

Data selection and model construction

In order to improve the trade level of the “Belt and Road” initiative countries, this paper studies the impact of international logistics performance of the “Belt and Road” initiative countries on China’s import and export trade, constructs an extended gravity model, and introduces the LPI into the model. At the same time, according to the population size, the countries along the “Belt and Road” are divided into three categories: large, medium and small, to explore the impact of international logistics performance on the import and export of countries of different sizes and the differences.

Data processing and variable setting

This study takes 2011–2022 as the time interval of the study. The data mainly come from the World Bank WDI database. In view of the fact that the data published by the World Bank has been updated to 2022, but there are missing data in individual years, such as LPI, since the World Bank releases the logistics performance index every two years, in order to ensure the continuity of the data, the missing data of this part is filled by linear prediction using stata15 software. In order to avoid the impact of unit differences between indicators on the experimental results, the gross national product of the “Belt and Road” initiative countries, China’s imports and exports to the “Belt and Road” initiative countries, the distance from the “Belt and Road” initiative countries, China’s gross national product, the comprehensive index of international logistics performance, the score of cargo tracking ability, the score of logistics serviceability, the score of international freight transportation that is easy to arrange competitive prices, the score of customs clearance process efficiency, the score of the expected time of goods to reach the consignee frequency and the score of transportation-related infrastructure quality are standardized by stata15. Variables are set as follows:

Explained variable: China’s trade volume with the “Belt and Road” initiative countries (billions of dollars).

Explanatory variable: international logistics performance of the “Belt and Road” initiative countries. Sub-indicators: goods tracking ability score, logistics serviceability score, easy-to-arrange price competitive international freight score, customs clearance process efficiency score, goods expected time to reach the consignee frequency score, and transportation-related infrastructure quality score.

Control variables: distance from the “Belt and Road” initiative countries (kilometers), gross national product of the “Belt and Road” initiative countries (billions of dollars), gross national product of China (billions of dollars), the ratio of total imports and exports of goods and services to GDP of the sample countries, whether it is adjacent to China, and whether it has joined the WTO.

Data sources and processing instructions

According to the model setting and variable definition, the variable name, economic implications, variable value, data source and expected impact on trade volume of international logistics performance and its sub-indicators and control variables on trade volume is shown in Table 1 . If the expected impact on trade volume is positive, it is expressed as '+', and vice versa.

Since the fixed effect model is used for regression analysis while controlling the year and time, all variables should change with time. This paper uses the product of the distance between China and the “Belt and Road” initiative countries and the Brent crude oil price of the year to represent the distance, so that the distance can change with time, which enhances the feasibility of the model. There are 65 countries and regions along the “Belt and Road” marked by the 'China Belt and Road Network'. However, due to the lack of data in Brunei, Timor-Leste, Palestine and other countries, combined with the availability of data, this study selects the “Belt and Road” initiative countries: 40 countries in Asia, 20 countries in Europe and one country in Africa, a total of 61 countries from 2011 to 2022 sample data for empirical research.

According to the average population data of the “Belt and Road” initiative countries from 2011 to 2022, the countries with the top 25% of the population are classified as large-scale countries, the latter 25% are classified as small-scale countries, and 25–75% are classified as medium-scale countries. The specific division results are shown in Table 2 .

Model construction

The gravitational model is derived from the law of universal gravitation proposed by the British physicist Newton. It was originally used to explain the interaction between objects and was later cited in the field of international trade. It is used to measure the relationship between the trade volume between the two countries and their economic scale (Zhong and Zhou 2022 ). The formula can be expressed as:

Formula (1) is transformed into logarithmic form and the random error term can be expressed as:

The above equation \({{TRADE}}_{{ij}}\) represents the trade volume between country i and country j, \({X}_{i}\) and \({X}_{j}\) represent the economic aggregate of country i and country j respectively, \({{DIS}}_{{ij}}\) represents the geographical distance between the two economies of country i and country j, \({\beta }_{0}\) represents the parameters to be estimated in the model, and ε represents the random error term of the model.

In the gravity model setting in the field of international trade, the trade volume between the two countries is negatively correlated with the distance between the two countries, and positively correlated with the total economic volume of the two countries. On the basis of the basic gravity model, combined with the existing research, the international logistics performance index released by the World Bank is introduced into the gravity model, and the control variables are added to expand the model. The control variables include: DIS, GDPJ, GDPC, OPEN, BORDER and WTO, in which OPEN is an endogenous variable, BORDER and WTO are dummy variables.

The extended gravity model can be expressed as follows:

Each sub-index of LPI as an alternative index of LPI into the extended gravity model can be expressed as:

Empirical analyses

Analysis of statistical index results.

The descriptive statistical results are shown in Table 3 . According to the results in the table, there are great differences in the data results of the “Belt and Road” initiative countries. First of all, there is a big difference in China’s import and export to the “Belt and Road” initiative countries: China’s import and export to Bhutan, Maldives, Bosnia and Herzegovina, North Macedonia and other countries remained low from 2011 to 2022, with an average annual import and export value of no more than $300 million. Trade with Singapore, India, Russia and other countries have remained at more than USD 50 billion since 2011. Secondly, the population size of the “Belt and Road” initiative countries is significantly different: the population of Maldives, Bhutan, Montenegro and other countries is less than one million between 2011 and 2022, while India’s population remains above one billion. Third, the scores of various indicators related to logistics in countries along the route are not the same: from the perspective of the comprehensive index of logistics performance, Singapore and other countries have maintained a score of more than 4, and the comprehensive ranking is among the top ten in the world, while Mongolia, Myanmar, Laos, Tajikistan, Turkmenistan and other countries have a low logistics level ranking of 100. From the perspective of each sub-index, the differences between countries are obvious, and the development status of the “Belt and Road” initiative countries is uneven.

LPI comprehensive index regression

On the basis of descriptive statistics, this paper further uses Stata15.0 software to conduct regression analysis on the panel data of the “Belt and Road” initiative countries from 2011 to 2022. Since the data cross section (N) > time series (T), it is a short panel and does not require a unit root test. Through collinearity diagnosis, it was found that the VIF values of each index were less than 10, indicating that there was no multicollinearity in the data. The results of the Hausman test are shown in Table 4 , and the P value is 0.0017, which is less than 0.1. Therefore, the results are significant. The original hypothesis that the panel data model is a random effect model is rejected, and the fixed effect model is supported. The fixed effect model is used to analyze the data from 2011 to 2022 by controlling the country and time at the same time. The regression results are shown in Table 5 .

According to the regression results, the impact of international logistics performance of the “Belt and Road” initiative countries on China’s import and export trade is as follows:

According to the regression results in Table 5 , the impact of LPI on China’s import and export trade is significantly positive under the condition of 10%, indicating that the higher the LPI of the “Belt and Road” initiative countries, the more conducive to the trade between China and the country.

In the regression results of endogenous variables, the coefficient of DIS is −0.116, which is significant at the 1% level. Therefore, the distance between China and the “Belt and Road” initiative countries has a significant negative impact on China’s trade volume. The farther the distance is, the more unfavorable it is for China’s import and export to the Belt and Road Initiative countries. In China’s international trade, distance cost is still an important influencing factor. The economic volume coefficient of the countries along the route is 0.803, indicating that the economic volume of the countries along the route has a certain impact on China’s imports and exports to the country. The higher the GDP is, the higher the economic development level of the country is, and the higher the corresponding consumption level is, thus driving China’s import and export to the country. China’s economic volume coefficient is 0.1, which is significantly positive at the 1% level. Therefore, the improvement of China’s economic volume will significantly promote the growth of international trade volume. In addition, the degree of opening to the outside world has a significant role in promoting international trade, with a coefficient of 0.223, indicating that the higher the degree of opening to the outside world of countries along the route will be conducive to China’s import and export to the country.

Through the results of dummy variable data, it can be seen that the BORDER and WTO coefficients are-0.048 and 0.011, respectively, and the BORDER is significant at the level of 10%, indicating that the national border is not conducive to improving China’s import and export of goods. Because the climate environment of the bordering countries is close to China, the differences in resources and production factors may not be obvious enough, so that BORDER is negatively correlated with TRADE. The WTO performance is not significant, indicating that whether to join the WTO organization cannot be used as the strongest factor affecting trade between countries. To a certain extent, the differences in the types of goods traded between China and countries of different sizes will affect the volume of trade between countries. For example, some countries have parallel production with China, which leads to a decrease in trade between China and the country.

The comparison of the results in Table 5 shows that the impact of international logistics performance on China’s import and export to large-scale countries is significantly positive at the 1% level. In addition, the impact of international logistics performance on small and medium-sized countries is negative and insignificant at the 10% level, respectively. To a certain extent, it is due to the small total national demand of small and medium-sized countries. The improvement of international logistics performance has also led to the improvement of the national internal logistics system and promoted the better utilization of national internal resources. Therefore, continuing to invest resources to enhance the international logistics performance level of small and medium-sized countries is not conducive to the growth of import and export trade volume, which is more obvious in small-scale countries, and reasonable allocation of resources is particularly important.

LPI sub-index regression

In order to study the impact of LPI’s specific sub-indicators on China’s import and export to the “Belt and Road” initiative countries, this paper replaces LPI with six sub-indicators TRACE, SERVICE, SHIPMENTS, CLEARANCE, TIME, INFRASTRUCTURE for regression analysis. The results are shown in Table 6 .

From the regression results, it can be seen that TRACE, SHIPMENTS, CLEARANCE and TIME are not significant at the 10% level, indicating that logistics cargo tracking ability, international freight price competitiveness, customs clearance efficiency and logistics timeliness have little impact on China’s import and export to the “Belt and Road” initiative countries. The impact of SERVICE on import and export trade is positive, which is significant at the level of 10%, and INFRASTRUCTURE is positive at the level of 1%. That is, the logistics service capacity and logistics infrastructure quality of the Belt and Road Initiative countries have a greater impact on China’s import and export trade. Logistics service capacity includes inventory capacity, operation capacity and logistics reliability of the logistics system. The progress of logistics inventory capacity and operation capacity will be conducive to the supply of resources and business development of international trade enterprises (Wang 2023 ). The improvement of logistics reliability will increase consumers ‘ online purchase intention to a certain extent and promote the positive development of international trade (Yuan and Zhang 2023 ). The quality of logistics infrastructure is an important guarantee for efficient transportation of goods (Yuan et al. 2023 ). Therefore, China’s trade import and export have a certain dependence on the level of international logistics performance. The improvement of the relevant sub-indicators of the logistics performance index has a certain role in promoting China’s import and export trade.

Through the regression analysis of comprehensive indicators, it can be seen that the impact of international logistics performance on large-scale countries is the most significant. In order to deeply explore the impact of sub-indicators of international logistics performance on large-scale countries, this paper introduces the sub-indicators of international logistics performance indicators into large-scale countries, and replaces LPI for regression analysis. The results are shown in Table 7 .

From the regression results in Table 7 , it can be seen that TRACE and TIME have no significant impact on China’s import and export to large-scale countries. SHIPMENTS is significant at the 5% level. SERVICE, CLEARANCE and INFRASTRUCTURE are significant at the 1% level, and the coefficients are 0.254,0.532,0.485 and 0.449, respectively. The international freight price competitiveness, logistics service capacity, customs clearance efficiency and logistics-related infrastructure level of the “Belt and Road” initiative countries have a significant positive effect on China’s trade import and export.

Robustness test

In order to avoid the influence of extreme values on the empirical results of the selected samples, this paper removes individual outliers and conducts robustness test analysis. Among the 61 sample countries along the “Belt and Road”, China’s annual average import and export volume to Bhutan, North Macedonia, Bosnia and Herzegovina and Moldova from 2011 to 2022 is less than USD 100 million, which has a large gap with the average value of China’s import and export volume to the “Belt and Road” initiative countries. Therefore, in order to avoid the impact of such extreme data on the experimental results, this paper eliminates the sample data of four countries, including Bhutan, North Macedonia, Bosnia and Herzegovina and Moldova, and performs multiple regression analysis on the remaining sample data. The regression results are shown in Table 8 .

Combined with the regression results, it can be seen that the LPI coefficient passed the significance test under the condition of 5%, excluding the influence of sample selection bias on the empirical results of this paper. That is, the international logistics performance of the Belt and Road Initiative countries has a significant role in promoting the growth of trade volume between China and the country.

Since 2020, the new coronavirus epidemic has traumatized the economies of various countries to a certain extent and has had a certain impact on the country’s import and export trade. In order to avoid the interference of such factors on the regression results, the sample time interval is shortened to 2011–2020, and the regression is carried out again. The results are shown in Table 9 , and the estimated coefficient of LPI is significantly positive at the level of 10%, which proves that under the condition of weakening the interference of economic turbulence factors, the improvement of logistics performance level of the “Belt and Road” initiative countries has a significant role in promoting China’s trade import and export, and the conclusions of this paper are still robust.

The data are tailed in the range of 5–95%. The second column in Table 10 shows that the international logistics performance of the “Belt and Road” initiative countries has a positive impact on China’s import and export trade, which is significant at the level of 5%, the coefficient is small, and the main research conclusions have not changed.

Since there may be a certain time difference in the effect of international logistics performance level on import and export trade volume, this paper lags the explained variable TRADE by one period to explore the lag effect of LPI on TRADE, which helps to alleviate the possible two-way causality. The results in Table 11 show that the impact of LPI on TRADE lags one period is consistent with the benchmark results, so the benchmark regression is robust.

Conclusions

By adding the international logistics performance index to the trade gravity model, this paper analyzes the impact of the logistics performance of the “Belt and Road” initiative countries on China’s import and export trade. At the same time, the countries along the 'Belt and Road' are divided into three scales: large, medium and small, to explore the differences in the impact of logistics performance on the import and export of China and these three scale countries. According to the empirical analysis, the following conclusions can be drawn:

First, the improvement of the logistics performance level of the “Belt and Road” initiative countries has a certain role in promoting the increase of trade volume between China and the country. The international logistics performance index of the “Belt and Road” initiative countries has the most significant impact on China’s import and export to large-scale countries. The impact of LPI on China’s import and export trade is significantly positive under the condition of 10%. Therefore, the improvement of the logistics performance index of the 'Belt and Road' initiative countries is conducive to the increase of trade volume between China and the country. The impact of international logistics performance on China’s import and export to large-scale countries is significantly positive at the 1% level, small-scale countries are significantly negative at the 10% level, and medium-scale countries are not significant.

Second, the sub-indicators of the international logistics performance index of the countries along the “Belt and Road” have different degrees of influence on the import and export volume. Among them, logistics service capacity has a significant impact at the level of 10%, and the quality of logistics infrastructure is significant at the level of 1%, and the coefficient is positive. Therefore, the improvement of logistics service capacity and logistics infrastructure quality will help promote the growth of import and export volume. However, TRACE, SHIPMENTS, CLEARANCE and TIME have no significant impact on import and export volume. Therefore, logistics cargo tracking capability, international freight price competitiveness, customs clearance efficiency and logistics timeliness have little impact on China’s import and export to the Belt and Road Initiative countries.

Third, among the sub-indicators of international logistics performance of large-scale countries along the 'Belt and Road', international freight price competitiveness, logistics service capacity, customs clearance efficiency and logistics-related infrastructure level have a significant role in promoting import and export trade, and the impact of cargo tracking capacity and logistics timeliness is not significant. SERVICE, CLEARANCE and INFRASTRUCTURE are significant at the 1% level, with coefficients of 0.532,0.485 and 0.449, respectively, so the impact of logistics service capacity is the greatest.

Practical implications

Based on the relevant conclusions of this paper, it is concluded that the improvement of the international logistics performance of the 'Belt and Road' initiative countries is conducive to promoting the development of China’s international trade, and the factors that have a greater impact on the growth of import and export trade in the sub-indicators of international logistics performance are clarified, which provides a certain basis for the implementation of the 'Belt and Road' initiative. In addition, combined with the research conclusions, targeted suggestions are put forward to provide certain reference values for the improvement of national logistics and trade levels and the implementation direction of the “Belt and Road” initiative.

First, improve logistics performance and reduce trade costs. The regression results show that the international logistics performance of the “Belt and Road” initiative countries has a significant and positive impact on China’s import and export, indicating that the level of logistics performance will promote the economic and trade exchanges between China and the “Belt and Road” initiative countries. The implementation of China’s 'Belt and Road' initiative is of great significance to the deepening of international cooperation. However, due to the large differences in the level of logistics performance among the “Belt and Road” initiative countries, this difference will restrict the development of intra-regional trade to a certain extent, and then weaken the benefits of the “Belt and Road” initiative. Therefore, it is particularly important to give full play to the role of the Asian Infrastructure Investment Bank and the Silk Road Fund to ensure the financial support for the process of improving the logistics performance level of the “Belt and Road” initiative countries. In addition, make full use of advanced digital economy and technology to promote more efficient and lower-cost trade between the “Belt and Road” initiative countries.

Second, strengthen infrastructure facilities and reduce trade barriers between countries along the route. Whether from the LPI comprehensive index regression or the regression results of each sub-index, the coefficient of distance is negative, indicating that the geographical distance between China and the “Belt and Road” initiative countries will have a negative impact on China’s import and export, that is, distance is still an important factor affecting trade costs. However, there are still some problems and obstacles in the logistics facilities of various countries. Therefore, it is necessary to increase the capital investment and investment in various facilities related to logistics, improve the sub-indicators of logistics performance, and improve logistics competitiveness and reduce trade costs by improving infrastructure quality and logistics service capabilities.

Third, improve the logistics performance of large-scale countries and promote the overall development of countries along the “Belt and Road”. Comparing the regression results of the three models of large, medium and small, the LPI passed the significance test of China’s import and export to large-scale countries at the 1% level. International logistics performance has the most significant impact on China’s import and export to large-scale countries along the “Belt and Road”, and the impact of logistics service capacity, customs clearance efficiency and logistics-related infrastructure level in each sub-index is the most significant. That is to say, the improvement of logistics performance of large-scale countries along the route will promote China’s import and export trade to a greater extent. Therefore, in order to promote the high-quality development of the “Belt and Road”, the government can increase investment in infrastructure construction in large-scale countries, promote the development of their import and export trade, and enhance the overall development of the “Belt and Road”.

Limitation and future research

In the research, the “Belt and Road” initiative countries are used as research samples. The sample interval is not broad enough, and the data source has certain limitations. Future research can consider many countries in the world with trade. Secondly, according to the number of populations, this paper divides different countries into three categories: large-scale countries, medium-scale countries and small-scale countries, and explores the different effects of international logistics performance on import and export trade in different countries. In the future, it can be further studied according to other aspects such as national income level, national geographical location and national economic development level. Finally, the fixed effect model is adopted in this paper. The research method is relatively simple, and the selection of control variables and sub-indicators is limited. Future research can try different research methods, add different control variables and sub-indicators to improve the technicality and comprehensiveness of the research. Although there are some limitations in this study, this study has certain positive significance for enriching the literature in the field of international logistics performance and international trade development, and enriches the current knowledge.

Data availability

The data used in the paper were compiled by the authors according to the World Bank Database and Prospective Database. Requests to access these publicly available datasets should be directed to https://d.qianzhan.com/xdata/list/xCxpy5y5xr.html , https://data.worldbank.org.cn/indicator/LP.LPI.OVRL.XQ , https://data.worldbank.org.cn/indicator/LP.LPI.TRAC.XQ , https://data.worldbank.org.cn/indicator/LP.LPI.INFR.XQ , https://data.worldbank.org.cn/indicator/LP.LPI.ITRN.XQ , https://data.worldbank.org.cn/indicator/LP.LPI.LOGS.XQ , https://data.worldbank.org.cn/indicator/LP.LPI.CUST.XQ , https://data.worldbank.org.cn/indicator/LP.LPI.TIME.XQ , https://data.worldbank.org.cn/indicator/NE.TRD.GNFS.ZS .

Barakat M, Madkour T, Moussa AM (2023) The role of logistics performance index on trade openness in Europe. Int J Econ Bus Res 25(3):379–394

Article   Google Scholar  

Bhukiya S, Patel R (2023) The Relationship Between Logistics Performance Index and International Trade: An Empirical Analysis. J homepage 2582:7421

Google Scholar  

Çelebi D (2019) The role of logistics performance in promoting trade. Marit Econ Logist 21:307–323

Cui Z, Fu X, Wang J, Qiang Y, Jiang Y, Long Z (2022) How does COVID-19 pandemic impact cities’ logistics performance? An evidence from China’s highway freight transport. Transp Policy 120:11–22

Fan X, Yu S (2015) ‘ Maritime Silk Road ‘ logistics performance and its impact on China’s import and export trade. J Xi Jiaotong Univ 35(3):13–20

Göçer A, Özpeynirci Ö, Semiz M (2022) Logistics performance index-driven policy development: An application to Turkey. Transp policy 124:20–32

Guo H, Qi Y, Zou Z (2018) An empirical analysis of the coordinated development of China’s logistics industry and foreign trade in the 40 years of reform and opening up. Enterprise Econ 12:56–63

Guo Q (2018) An analysis of the impact of international trade on the development of the logistics industry. Bus Econ Res 23:92–94

Hausman WH, Lee HL, Subramanian U (2013) The impact of logistics performance on trade. Prod Oper Manag 22(2):236–252

Huong VTT, Van Kiem P, Thuy NT, Trang VTH, Ha HTT (2024) Assessing the impact of green logistics performance on vietnam’s export trade to regional comprehensive economic partnership countries. Int J Innovative Res Sci Stud 7(2):742–754

Jayathilaka R, Jayawardhana C, Embogama N, Jayasooriya S, Karunarathna N, Gamage T, Kuruppu N (2022) Gross domestic product and logistics performance index drive the world trade: A study based on all continents. PloS one 17(3):e0264474

Article   PubMed   PubMed Central   CAS   Google Scholar  

Khan SAR, Godil DI, Yu Z, Abbas F, Shamim MA (2022a) Adoption of renewable energy sources, low‐carbon initiatives, and advanced logistical infrastructure—a step toward integrated global progress. Sustain Dev 30(1):275–288

Khan SAR, Yu Z, Umar M, Zia‐ul‐haq HM, Tanveer M, Janjua LR (2022b) Renewable energy and advanced logistical infrastructure: Carbon‐free economic development. Sustain Dev 30(4):693–702

Kumari M, Bharti N (2021) Trade and logistics performance: does country size matter? Marit Econ Logist 23(3):401–423

Liu L (2022) Cultivation of competitive advantage of cross-border agricultural products trade in western China-Based on the perspective of cross-border logistics. Res Business Econ 1:135–138

Mena C, Karatzas A, Hansen C (2022) International trade resilience and the Covid-19 pandemic. J Bus Res 138:77–91

Article   PubMed   Google Scholar  

Pan WT, Jiang B, Wang Y, Cai Y, Ji X (2022) Comparison and Suggestions of Logistics Performance Index of Main Countries of Belt and Road Strategy Based on Bootstrap DEA Model. Comput Intell Neurosci 2022:2159578

Article   PubMed   PubMed Central   Google Scholar  

Qin N (2022) Industrial co-agglomeration, logistics performance and trade openness - Evidence from countries along the “Belt and Road”. Business Econ Res 5:151–153

See KF, Guo Y, Yu MM (2024) Enhancing logistics performance measurement: an effectiveness-based hierarchical data envelopment analysis approach. Inf Syst Oper Res, 1–31, https://doi.org/10.1080/03155986.2024.2309420

Wang F (2015) Research on Logistics Development and Energy Consumption from the Perspective of Urbanization Development. Techno Econ Manag Res 3:100–103

Wang H (2023) Effect of information technology capability on business performance of logistics enterprises-based on the mediating role of logistics service quality. Bus Econ Res 24:104–108

Wang R, Wang Q (2021) The role of export trade in the “ Belt and Road “ core region on the growth and agglomeration of the logistics industry. Bus Econ Res 17:101–104

Wang S, Liu X (2022) Research on the impact of international logistics performance on agricultural trade between China and ASEAN. Mall Modernization 1:34–37

Yang M (2023) Analysis of the Impact of International Logistics Development Level on China’s Import and Export Trade of Agricultural Products. Logist Eng Manag 45(7):146–148

Yang S (2010) Research on the Interactive Relationship between International Trade and Modern Logistics Development. China Market 28:11

Yang Z, Hu Z, Liu C (2019) Analysis of the potential of trade and cross-border logistics cooperation between China and ASEAN countries in the context of the “Belt and Road”. Comput Appl Softw 36(2):1–6

ADS   Google Scholar  

Yingfei Y, Mengze Z, Zeyu L, Ki-Hyung B, Avotra AARN, Nawaz A (2022) Green logistics performance and infrastructure on service trade and environment-measuring firm’s performance and service quality. J King Saud Univ Sci 34(1):101683

Yuan C, Li J, Wei Y (2023) Rural logistics construction and farmers ‘ income increase effect. World Econ 46(4):111–139

Yuan X, Zhang X (2023) Research on logistics service strategy of e-commerce supply chain network platform under different sales channels. Supply Chain Manag 4(11):74–86

Zhan J, Feng X, Song Dynasty (2019) Research on the driving effect of export trade on the development of logistics industry in the core area of the “Belt and Road” -Based on the LMDI model. Price Monthly 4:44–53

Zhong D, Zhou N (2022) Analysis of the impact of logistics performance of countries along the China-Europe train on Guangdong’s import and export trade. Foreign Trade 6:63–68

Download references

Acknowledgements

This research was funded by the Science and Technology Innovation Project of Chongqing Education Commission “Chengdu Chongging Double City Economic Circle Construction”, Grant Number KJCX2020039.

Author information

Authors and affiliations.

School of International Business and Management, Sichuan International Studies University, Chongqing, 400031, China

Weixin Wang

College of Finance and Economics, Sichuan International Studies University, Chongqing, 400031, China

Qiqi Wu & Bing li

International College, Krirk University, Bangkok, 10220, Thailand

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization, Weixin Wang and Jiafu Su; methodology, Qiqi Wu.; software, Jiafu Su.; formal analysis, Bing Li; resources, Weixin Wang; writing—original draft preparation, Weixin Wang; writing—review and editing, Qiqi Wu. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Jiafu Su .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Informed consent

This research does not involve issues of human privacy or risk. Hence, informed consent is not applicable.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Wang, W., Wu, Q., Su, J. et al. The impact of international logistics performance on import and export trade: an empirical case of the “Belt and Road” initiative countries. Humanit Soc Sci Commun 11 , 1028 (2024). https://doi.org/10.1057/s41599-024-03541-0

Download citation

Received : 18 January 2024

Accepted : 31 July 2024

Published : 10 August 2024

DOI : https://doi.org/10.1057/s41599-024-03541-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

• SUGGESTED TOPICS
• The Magazine
• Newsletters
• Managing Yourself
• Managing Teams
• Work-life Balance
• The Big Idea
• Data & Visuals
• Reading Lists
• Case Selections
• HBR Learning
• Topic Feeds
• Account Settings
• Email Preferences

It’s Time to Rethink Your Global Logistics

• Willy C. Shih
• Adrien Foucault

The pandemic has overloaded companies’ usual shipping networks.

The initial supply and demand shocks caused by the pandemic were followed by an import surge as suppliers tried to replenish inventories, which threw normal transportation operations into turmoil. In the United States, this has included a lack of freight-handling capacity at Los Angeles and Long Beach ports, overloaded U.S. intermodal rail networks, and a lack of containers. But alternatives to established logistics networks exist. It’s time for companies to take advantage of them.

Over the last three decades, companies have established wide-ranging global supply chains that have taken advantage of steadily improving scale economies in global logistics. Efficient and reliable ocean and air cargo have linked low-cost manufacturing hubs across Asia with major markets in the United States and Europe. Much of this global sourcing was driven by the cost savings reaped through labor arbitrage, cost savings that were so dramatic that it more than covered the expense associated with moving products across vast distances to markets, or the extra cost of carrying inventory in long pipelines.

• Willy C. Shih is a Baker Foundation Professor of Management Practice at Harvard Business School.
• Adrien Foucault is an MBA student at Harvard Business School and has worked at maritime transport company CMA CGM.

Partner Center

Advertisement

Strategies to overcome challenges to smart sustainable logistics: a Bayesian-based group decision-making approach

• Published: 27 June 2023
• Volume 26 , pages 11743–11770, ( 2024 )

Cite this article

• Himanshu Gupta   ORCID: orcid.org/0000-0001-9202-4728 1 ,
• Kumar Shreshth 1 ,
• Manjeet Kharub 2 &
• Ashwani Kumar 3  

703 Accesses

3 Citations

Explore all metrics

A Correction to this article was published on 17 July 2023

This article has been updated

The logistics sector has seen rapid growth in the past few years due to globalization and the rise in demand for goods and commodities. With the exponential growth, managing logistics is becoming complex and challenging, often due to a lack of traceability. Also, its negative impacts on the environment have increased due to increased footprints, thus causing a threat to sustainability. Incorporating smart systems in the logistics sector is a possible solution to overcome these issues. But the incorporation of smart technologies in the logistics sector of a developing economy is often marred by various challenges. This study aims to identify and prioritize the challenges to smart sustainable logistics (SSL) and the multiple strategies that can help overcome these challenges. A framework comprised of 19 barriers to SSL and seven strategies for overcoming these barriers is established via a comprehensive literature study and practitioner discussions. The Bayesian best–worst method is implemented to examine the barriers to SSL, while the additive value function is used to rank the strategies. The results indicate that businesses must develop internet infrastructure and R&D and innovation competencies for the logistics sector to be smart and sustainable. They also need to build institutional structures for technology development. Also, reducing technological uncertainties, enhancing research & development capabilities, and nurturing human resources in smart technologies can help logistics companies overcome these challenges.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Connection Between Sustainable Process Innovation and Digital Transformation in Logistics – Multiple Case Study Research

Sustainable Logistics and Operations Design: Sustainable Practices for Emerging Countries - Perspective of the Situation in Bolivia

Toward Sustainable Logistics

Explore related subjects.

• Artificial Intelligence

Data availability

The data used in this study are already mentioned in the manuscript and annexures. Any other request can be made to authors regarding the data.

Change history

17 july 2023.

A Correction to this paper has been published: https://doi.org/10.1007/s10668-023-03611-4

Abbasi, M., & Nilsson, F. (2016). Developing environmentally sustainable logistics: Exploring themes and challenges from a logistics service providers’ perspective. Transportation Research Part d: Transport and Environment, 46 , 273–283.

Google Scholar  

Ahalt, S., & Kelly, K. (2013). The big data talent gap.  UNC Kenan-Flagler Business School White Paper , 1–15.

Ahmad, S., & Wong, K. Y. (2019). Development of weighted triple-bottom line sustainability indicators for the Malaysian food manufacturing industry using the Delphi method. Journal of Cleaner Production, 229 , 1167–1182.

Alam, S., Shuaib, M., Khan, W. Z., Garg, S., Kaddoum, G., Hossain, M. S., & Zikria, Y. B. (2021). Blockchain-based Initiatives: Current state and challenges. Computer Networks, 198 , 108395.

Al-Qaseemi, S. A., Almulhim, H. A., Almulhim, M. F., & Chaudhry, S. R. (2016). IoT architecture challenges and issues: Lack of standardization. In  2016 Future Technologies Conference (FTC)  (pp. 731–738). IEEE.

Amiri, M., Hashemi-Tabatabaei, M., Ghahremanloo, M., Keshavarz-Ghorabaee, M., Zavadskas, E. K., & Banaitis, A. (2021). A new fuzzy BWM approach for evaluating and selecting a sustainable supplier in supply chain management. International Journal of Sustainable Development & World Ecology, 28 (2), 125–142.

Annosi, M. C., Brunetta, F., Bimbo, F., & Kostoula, M. (2021). Digitalization within food supply chains to prevent food waste. Drivers, barriers and collaboration practices. Industrial Marketing Management, 93 , 208–220.

Barnes, S. J., & Mattsson, J. (2016). Understanding current and future issues in collaborative consumption: A four-stage Delphi study. Technological Forecasting and Social Change, 104 , 200–211.

Bertels, S., Papania, L., & Papania, D. (2010). Embedding sustainability in organizational culture: A systematic review of the body of knowledge.  Network for Business Sustainability , 25.

Botta, A., De Donato, W., Persico, V., & Pescapé, A. (2016). Integration of cloud computing and internet of things: A survey. Future Generation Computer Systems, 56 , 684–700.

Brender, N., & Markov, I. (2013). Risk perception and risk management in cloud computing: Results from a case study of Swiss companies. International Journal of Information Management, 33 (5), 726–733.

Brundtland, G. H. (1987). What is sustainable development. Our common future, 8 (9).

Carpentieri, G. (2015). Smart and sustainable logistics for a competitive Europe.

Dey, A., LaGuardia, P., & Srinivasan, M. (2011). Building sustainability in logistics operations: A research agenda. Management Research Review, 34 (11), 1237–1259.

Ding, Y., Jin, M., Li, S., & Feng, D. (2021). Smart logistics based on the internet of things technology: An overview. International Journal of Logistics Research and Applications, 24 (4), 323–345.

Esmaeilian, B., Sarkis, J., Lewis, K., & Behdad, S. (2020). Blockchain for the future of sustainable supply chain management in Industry 4.0. resources. Conservation and Recycling, 163 , 105064.

Ghadir, A. H., Vandchali, H. R., Fallah, M., & Tirkolaee, E. B. (2022). Evaluating the impacts of COVID-19 outbreak on supply chain risks by modified failure mode and effects analysis: A case study in an automotive company. Annals of Operations Research . https://doi.org/10.1007/s10479-022-04651-1

Article   Google Scholar  

Global Logistics Markets, 2011–2018 & 2019–2024. (2019). GlobeNewswire News Room. Retrieved 7 March 2022, from https://www.globenewswire.com/news-release/2019/07/19/1885194/0/en/Global-Logistics-Markets-2011-2018-2019-2024.html .

Goh, S. H. (2019). Barriers to low-carbon warehousing and the link to carbon abatement. International Journal of Physical Distribution & Logistics Management., 49 (6), 679–704.

Good, N., Ellis, K. A., & Mancarella, P. (2017). Review and classification of barriers and enablers of demand response in the smart grid. Renewable and Sustainable Energy Reviews, 72 , 57–72.

Gottschlich, F. (2018). Benefits and challenges of factors influencing the integration of blockchain technology. Journal of Applied Leadership and Management, 6 , 1–17.

Govindan, K., Shankar, K. M., & Kannan, D. (2020). Achieving sustainable development goals through identifying and analyzing barriers to industrial sharing economy: A framework development. International Journal of Production Economics, 227 , 107575.

Gupta, H., & Barua, M. K. (2017). Supplier selection among SMEs on the basis of their green innovation ability using BWM and fuzzy TOPSIS. Journal of Cleaner Production, 152 , 242–258.

Gupta, H., Kusi-Sarpong, S., & Rezaei, J. (2020). Barriers and overcoming strategies to supply chain sustainability innovation. Resources, Conservation and Recycling, 161 , 104819.

Gupta, H., Yadav, A. K., Kusi-Sarpong, S., Khan, S. A., & Sharma, S. C. (2022). Strategies to overcome barriers to innovative digitalization technologies for supply chain logistics resilience during pandemic. Technology in Society, 69 , 101970.

Gutierrez, A., Boukrami, E., & Lumsden, R. (2015). Technological, organizational and environmental factors influencing managers’ decision to adopt cloud computing in the UK. Journal of Enterprise Information Management, 28 (6), 788–807.

Hackius, N., & Petersen, M. (2017). Blockchain in logistics and supply chain: Trick or treat?. In  Digitalization in supply chain management and logistics: Smart and digital solutions for an industry 4.0 environment. Proceedings of the hamburg international conference of logistics (HICL), 23 (pp. 3–18). Berlin: epubli GmbH.

Haddud, A., DeSouza, A., Khare, A., & Lee, H. (2017). Examining potential benefits and challenges associated with the Internet of Things integration in supply chains. Journal of Manufacturing Technology Management., 28 (8), 1055–1085.

Haque, M. M., Chin, H. C., & Debnath, A. K. (2013). Sustainable, safe, smart—three key elements of Singapore’s evolving transport policies. Transport Policy, 27 , 20–31.

Heidary Dahooie, J., Zamani Babgohari, A., Meidutė-Kavaliauskienė, I., & Govindan, K. (2021). Prioritizing sustainable supply chain management practices by their impact on multiple interacting barriers. International Journal of Sustainable Development & World Ecology, 28 (3), 267–290.

Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21 (2), 155–172.

Issaoui, Y., Khiat, A., Bahnasse, A., & Ouajji, H. (2020). Smart logistics: Blockchain trends and applications. Journal of Ubiquitous Systems & Pervasive Networks, 12 (2), 9–15.

Kamble, S. S., Gunasekaran, A., & Sharma, R. (2018). Analysis of the driving and dependence power of barriers to adopt industry 4.0 in Indian manufacturing industry. Computers in Industry, 101 , 107–119.

Karakas, S., Acar, A. Z., & Kucukaltan, B. (2021). Blockchain adoption in logistics and supply chain: A literature review and research agenda. International Journal of Production Research . https://doi.org/10.1080/00207543.2021.2012613

Kaushik, V., Kumar, A., Gupta, H., & Dixit, G. (2022). Modelling and prioritizing the factors for online apparel return using BWM approach.  Electronic Commerce Research, 22 , 843–873.

Keeney, R. L., & Raiffa, H. (1993). Decisions with multiple objectives: Preferences and value trade-offs . Cambridge University Press.

Kheybari, S., Kazemi, M., & Rezaei, J. (2019). Bioethanol facility location selection using best-worst method. Applied Energy, 242 , 612–623.

Kongbuamai, N., Bui, Q., Adedoyin, F. F., & Bekun, F. V. (2022). Developing environmental policy framework for sustainable development in Next-11 countries: the impacts of information and communication technology and urbanization on the ecological footprint. Environment, Development and Sustainability , 1–29.

Kumar, A., & Anbanandam, R. (2020). Assessment of environmental and social sustainability performance of the freight transportation industry: An index-based approach. Transport Policy .

Kumar, S., Raut, R. D., Narwane, V. S., Narkhede, B. E., & Muduli, K. (2022). Implementation barriers of smart technology in Indian sustainable warehouse by using a Delphi-ISM-ANP approach. International Journal of Productivity and Performance Management, 71 (3), 696–721.

Kusi-Sarpong, S., Orji, I. J., Gupta, H., & Kunc, M. (2021). Risks associated with the implementation of big data analytics in sustainable supply chains. Omega, 105 , 102502.

Kuteyi, D., & Winkler, H. (2022). Logistics challenges in sub-saharan africa and opportunities for digitalization. Sustainability, 14 (4), 2399.

Lee, C. K., Yeung, C. L., & Cheng, M. N. (2015). Research on IoT based cyber physical system for industrial big data analytics. In  2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)  (pp. 1855–1859). IEEE.

Lee, C. K., Lv, Y., Ng, K. K. H., Ho, W., & Choy, K. L. (2018). Design and application of Internet of things-based warehouse management system for smart logistics. International Journal of Production Research, 56 (8), 2753–2768.

Lee, S., Kang, Y., & Prabhu, V. V. (2016). Smart logistics: Distributed control of green crowdsourced parcel services. International Journal of Production Research, 54 (23), 6956–6968.

Luthra, S., Garg, D., Mangla, S. K., & Berwal, Y. P. S. (2018). Analyzing challenges to Internet of Things (IoT) adoption and diffusion: An Indian context. Procedia Computer Science, 125 , 733–739.

Ma, L. (2022). Application of fuzzy particle swarm optimization algorithm in optimizing logistics distribution management system. In 2022 IEEE 2nd international conference on electronic technology, communication and information (ICETCI) (pp. 1189–1192). IEEE.

Malaka, I., & Brown, I. (2015). Challenges to the organizational adoption of big data analytics: A case study in the South African telecommunications industry. In  Proceedings of the 2015 annual research conference on South African institute of computer scientists and information technologists  (pp. 1–9).

Malek, J., & Desai, T. N. (2019). Interpretive structural modelling based analysis of sustainable manufacturing enablers. Journal of Cleaner Production, 238 , 117996.

Mohammadi, M., & Rezaei, J. (2020). Bayesian best-worst method: A probabilistic group decision making model. Omega, 96 , 102075.

Moktadir, M. A., Ali, S. M., Paul, S. K., & Shukla, N. (2019). Barriers to big data analytics in manufacturing supply chains: A case study from Bangladesh. Computers & Industrial Engineering, 128 , 1063–1075.

Moktadir, M. A., Kumar, A., Ali, S. M., Paul, S. K., Sultana, R., & Rezaei, J. (2020). Critical success factors for a circular economy: Implications for business strategy and the environment. Business Strategy and the Environment, 29 (8), 3611–3635.

Nagy, J., Oláh, J., Erdei, E., Máté, D., & Popp, J. (2018). The role and impact of Industry 4.0 and the internet of things on the business strategy of the value chain—the case of Hungary. Sustainability, 10 (10), 3491.

Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data, 2 (1), 1.

Neto, J. Q. F., Bloemhof-Ruwaard, J. M., van Nunen, J. A., & van Heck, E. (2008). Designing and evaluating sustainable logistics networks. International Journal of Production Economics, 111 (2), 195–208.

Neto, J. Q. F., Walther, G., Bloemhof, J., Van Nunen, J. A. E. E., & Spengler, T. (2009). A methodology for assessing eco-efficiency in logistics networks. European Journal of Operational Research, 193 (3), 670–682.

Nica, E., Manole, C., & Stan, C. I. (2018). A laborless society? How highly automated environments and breakthroughs in artificial intelligence bring about innovative kinds of skills and employment disruptions, altering the nature of business process and affecting the path of economic growth.  Journal of Self-Governance & Management Economics ,  6 (4).

Norese, M. F., Corazza, L., Bruschi, F., & Cisi, M. (2021). A multiple criteria approach to map ecological-inclusive business models for sustainable development. International Journal of Sustainable Development & World Ecology, 28 (1), 75–91.

Noy, I. Y., Shinar, D., & Horrey, W. J. (2018). Automated driving: Safety blind spots. Safety Science, 102 , 68–78.

Orji, I. J., Kusi-Sarpong, S., & Gupta, H. (2020). The critical success factors of using social media for supply chain social sustainability in the freight logistics industry. International Journal of Production Research, 58 (5), 1522–1539.

Pathak, S. K., Sharma, V., Chougule, S. S., & Goel, V. (2022). Prioritization of barriers to the development of renewable energy technologies in India using integrated Modified Delphi and AHP method. Sustainable Energy Technologies and Assessments, 50 , 101818.

Prakash, C., & Barua, M. K. (2015). Integration of AHP-TOPSIS method for prioritizing the solutions of reverse logistics adoption to overcome its barriers under fuzzy environment. Journal of Manufacturing Systems, 37 , 599–615.

Qiu, T., Chi, J., Zhou, X., Ning, Z., Atiquzzaman, M., & Wu, D. O. (2020). Edge computing in industrial internet of things: Architecture, advances and challenges. IEEE Communications Surveys & Tutorials, 22 (4), 2462–2488.

Rahimi, M., Kumar, P., Moazzamigodarzi, M., & Mishra, A. R. (2022). Digital transformation challenges in sustainable financial service systems using novel interval-valued Pythagorean fuzzy double normalization-based multiple aggregation approach. Environment, Development and Sustainability , pp.1–33.

Raj, A., Dwivedi, G., Sharma, A., de Sousa Jabbour, A. B. L., & Rajak, S. (2020). Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective. International Journal of Production Economics, 224 , 107546.

Ramachandran, S., Nakhava, M. S., & Pratik, K. (2015). Logistics in India: Challenges and scope. International Review of Research in Emerging Markets and the Global Economy (IRREM), 1 (2), 364–377.

Ranieri, L., Digiesi, S., Silvestri, B., & Roccotelli, M. (2018). A review of last mile logistics innovations in an externalities cost reduction vision. Sustainability, 10 (3), 782.

Rashidi, K., & Cullinane, K. (2019). Evaluating the sustainability of national logistics performance using data envelopment analysis. Transport Policy, 74 , 35–46.

Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53 , 49–57.

Rugeviciute, A., & Mehrpouya, A. (2019). Blockchain, a panacea for development accountability? A study of the barriers and enablers for Blockchain’s adoption by development aid organizations. Frontiers in Blockchain, 2 , 15.

Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019). Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research, 57 (7), 2117–2135.

Schneier, B. (2017). The internet of things will upend our industry. IEEE Security & Privacy, 15 (02), 108–108.

Schoenherr, T., & Speier-Pero, C. (2015). Data science, predictive analytics, and big data in supply chain management: Current state and future potential. Journal of Business Logistics, 36 (1), 120–132.

Logistics Sector. (2019). Retrieved from https://www.makeinindia.com/logistics-sector .

Shekhar, S., & Kesharwani, A. (2021). Analyzing the barriers to international trade logistics from indian perspective: An ism modelling approach. Academy of Marketing Studies Journal, 25 (6), 1–6.

Singh, P. K., & Maheswaran, R. (2023). Analysis of social barriers to sustainable innovation and digitization in supply chain. Environment, Development and Sustainability , 1–26.

Singh, R., & Bhanot, N. (2020). An integrated DEMATEL-MMDE-ISM based approach for analyzing the barriers of IoT implementation in the manufacturing industry. International Journal of Production Research, 58 (8), 2454–2476.

Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of big data challenges and analytical methods. Journal of Business Research, 70 , 263–286.

Stergiou, C., Psannis, K. E., Kim, B. G., & Gupta, B. (2018). Secure integration of IoT and cloud computing. Future Generation Computer Systems, 78 , 964–975.

Stewart, R., Bey, N., & Boks, C. (2016). Exploration of the barriers to implementing different types of sustainability approaches. Procedia CIRP, 48 , 22–27.

Talavera, J. M., Tobón, L. E., Gómez, J. A., Culman, M. A., Aranda, J. M., Parra, D. T., & Garreta, L. E. (2017). Review of IoT applications in agro-industrial and environmental fields. Computers and Electronics in Agriculture, 142 , 283–297.

Taskinsoy, J. (2019). Blockchain: A misunderstood digital revolution. Things you need to know about blockchain.  Things You Need to Know about Blockchain (October 8, 2019) .

Vaidya, S., Ambad, P., & Bhosle, S. (2018). Industry 4.0–a glimpse. Procedia Manufacturing, 20 , 233–238.

Wei, F., Alias, C., & Noche, B. (2019). Applications of digital technologies in sustainable logistics and supply chain management. Innovative Logistics Services and Sustainable Lifestyles: Interdependencies, Transformation Strategies and Decision Making , 235–263.

Yadav, G., Mangla, S. K., Luthra, S., & Rai, D. P. (2019). Developing a sustainable smart city framework for developing economies: An Indian context. Sustainable Cities and Society, 47 , 101462.

Yan, Y., Shang, W. L., Yan, J., Liao, Q., Wang, B., Song, H., & Liu, Y. (2022). Logistic and scheduling optimization of the mobilized and distributed battery in urban energy systems. Resources, Conservation and Recycling, 187 , 106608.

Zhang, Y., Zhao, H., Li, B., Zhao, Y., & Qi, Z. (2022). Research on credit rating and risk measurement of electricity retailers based on Bayesian best worst method-cloud model and improved credit metrics model in China’s power market. Energy, 252 , 124088.

Download references

Author information

Authors and affiliations.

Department of Management Studies and Industrial Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, India

Himanshu Gupta & Kumar Shreshth

Faculty of Operations Management, Institute of Management Technology, Ghaziabad, Delhi NCR, Ghaziabad, India

Manjeet Kharub

Indian Institute of Management, Rohtak, India

Ashwani Kumar

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Himanshu Gupta .

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 65 kb)

Rights and permissions.

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Gupta, H., Shreshth, K., Kharub, M. et al. Strategies to overcome challenges to smart sustainable logistics: a Bayesian-based group decision-making approach. Environ Dev Sustain 26 , 11743–11770 (2024). https://doi.org/10.1007/s10668-023-03477-6

Download citation

Received : 14 April 2022

Accepted : 06 June 2023

Published : 27 June 2023

Issue Date : May 2024

DOI : https://doi.org/10.1007/s10668-023-03477-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Smart logistics
• Sustainable logistics
• Bayesian best–worst method (BBWM)
• Bibliometric analysis
• Big data analytics
• Find a journal
• Publish with us
• Track your research

• Search Menu
• Sign in through your institution
• Advance articles
• Special issues
• Virtual issues
• Author Guidelines
• Submission Site
• Open Access
• Reasons to submit
• About IMA Journal of Management Mathematics
• About the Institute of Mathematics and its Applications
• Editorial Board
• Area Editors
• Advertising and Corporate Services
• Journals Career Network
• Self-Archiving Policy
• Dispatch Dates
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

1. introduction, 2. green logistics and sustainability, 3. city logistics, 4. vehicle routing problems, 5. current trends and business and social innovations, 6. conclusion and recommendations, acknowledgements, last mile logistics: research trends and needs.

• Article contents
• Figures & tables
• Supplementary Data

Emrah Demir, Aris Syntetos, Tom van Woensel, Last mile logistics: Research trends and needs, IMA Journal of Management Mathematics , Volume 33, Issue 4, October 2022, Pages 549–561, https://doi.org/10.1093/imaman/dpac006

• Permissions Icon Permissions

Aspiring green agendas in conjunction with tremendous economic pressures are resulting in an increased attention to the environment and technological innovations for improving existing logistics systems. Last mile logistics, in particular, are becoming much more than a consumer convenience necessity and a transportation optimization exercise. Rather, this area presents a true opportunity to foster both financial and environmental sustainability. This paper investigates recent technological advancements and pending needs related to business and social innovations, emphasizing green logistics and city logistics concepts. We discuss various pertinent aspects, including drones, delivery robots, truck platooning, collection and pickup points, collaborative logistics, integrated transportation, decarbonization and advanced transport analytics. From a mathematical perspective, we focus on the basic features of the vehicle routing problem and some of its variants. We provide recommendations around strategies that may facilitate the adoption of new effective technologies and innovations.

In recent years, the vulnerability of supply chains and transportation networks was exposed at a time when the demand for last mile logistics services soared. While COVID-19 has been a significant threat to almost everything as part of modern life, relevant operational responses have been almost exclusively reactive than proactive. Similarly, the logistics networks connecting us to goods have been under immense pressure due to increased online shopping. The value of public and private partnerships for the environment and technology integration has never been more crucial for the transport industry. As customers ask for fast and reliable last mile delivery, bringing technological innovation into sustainable transportation systems is urgently needed. Before the pandemic, the logistics industry was under pressure to improve their operations for cost reduction and profit making in a highly competitive market while dealing with unending requirements of their customers. For example, in their research, Gevaers et al. (2014) investigated the cost characteristics of last mile delivery services. In order to quantify the costs, their proposed simulation model considered the level of customer service, type of delivery, geographical area, market, density, fleet and the environment. It is highlighted that the last mile-related costs can differ greatly depending on these factors.

Environmental sustainability has never attracted equal focus compared with the economic priorities of retailers and logistics service providers (LSPs). It is now time to consider both financial and environmental sustainability in an attempt to escape from the disastrous impact of the pandemic and be ready for the future. There is an excellent opportunity to make changes and improve the design and operations of freight transportation soon as discussed in Meersman & Van de Voorde (2019) . Looking at the vulnerability of the logistics systems, the logistics industry needs to make the best use of available resources to ensure a sustainable future for all. Green logistics has been one of the most studied topics in the last decade, and it has brought various ideas and algorithms for tackling emissions, particularly greenhouse gases (GHGs) ( Dekker et al. , 2012 ; Demir et al. , 2014 ; Marrekchi et al. , 2021 ; Moghdani et al. , 2021 ). We can extend this area of research by looking at the latest technological, social and business innovations as a remedy to last mile problem.

Freight transportation manages the complete operation of the movement of freight and related resources from a starting location to a final destination by paying particular attention to customers’ requirements ( Ghiani et al. , 2013 ; Toth & Vigo, 2014 ). In practice, traditional LSPs aim to manage these activities at the lowest possible logistics cost and risk to be a preferable option for shippers and customers. Therefore, it is essential to optimize the whole logistics network, considering the characteristics of each component used in freight transportation. As noted in the literature, there are two main areas in freight transportation based on the coverage area of distribution/collection services. These two types of transportation are called long-haul and short-haul transportation. In long-haul transportation, freight is transported over a long distance (i.e. minimum hundreds of kilometres). Short-haul transportation is referred to as a small distance delivery within a city or region. This paper focuses on short-haul transportation as it is the most crucial part of the supply networks and the most relevant one for last mile logistics.

Due to the unprecedented increase in e-commerce and accessibility of goods via the Internet, the role of LSPs has become more critical in the supply network. The Swiss Reinsurance company estimates that the population living in areas classified as urban will increase by approximately 1.4 billion to 5 billion from 2011 to 2030 ( DHL, 2014 ). This will make the logistics systems more complicated than before. The cheapest delivery to satisfy customers’ needs has been the top priority for the logistics industry. Nowadays, the commitments for on-time delivery and reduced or net-zero emission (GHGs and air pollutants) are also becoming very important targets in a competitive and cost-driven logistics market ( Savelsbergh & Van Woensel, 2016 ).

As the most crucial part of the supply network, the road transportation mode is the most used and preferred option by the logistics industry. The whole process in road freight needs to deal with several decision-making stages. At the lowest level (operational-level) of planning, the Vehicle Routing Problem (VRP) has been extensively studied since the original work by Dantzig & Ramser (1959) . The main objective in this problem is to obtain a set of routes for vehicles starting and ending at a depot to visit customers’ locations. The problem also considers several practical operational constraints. These may include vehicle capacity or compartment volume, distance or duration, customers’ time windows (i.e. hard or soft), and other related customer, product, resource or LSP-related specific requirements.

Traditionally, the minimization of the travelled distance was considered as the main objective in the VRP literature. With the increasing emphasis on the environment, the interaction of operational research with automotive engineering highlighted various factors to accurately estimate fuel consumption. This interaction has to lead to the development of green logistics (and green vehicle routing as a sub-category) topic in the operational research literature ( Demir et al. , 2014 ; Moghdani et al. , 2021 ).

Another positive impact on freight transportation from the effects of increased e-commerce sales is the acceleration of the adoption of technological innovation for the industry. Seamless delivery and the use of new alternative resources, such as drones, delivery robots and truck platooning have led to new opportunities for the logistics industry. This paper presents a brief discussion on how the last mile logistics have evolved around green logistics (or sustainability) and technological innovations in recent years. This discussion will highlight the current achievements and the outlook of future needs on last mile logistics. We note that our focus is mostly on vehicle routing optimization and related developments in the context of last mile logistics. Other aspects of last mile logistics, such as the location problems and humanitarian logistics, are not covered in this paper.

The scientific and visionary contributions of this paper is threefold: (i) to discuss the importance of green vehicle routing and city logistics for the last mile delivery, (ii) to briefly introduce the VRP and some of its variants, (iii) to review the latest technological developments in last mile logistics. The remainder of this ‘positioning’ paper is organized into five sections. Section 2 presents a brief review on green vehicle routing, whereas section 3 discusses recent research in city logistics. Section 4 provides relevant VRPs along with an example of VRP mathematical formulation. In section 5 , we discuss contemporary topics related to last mile logistics. Conclusions and the outlook of future research needs on last mile logistics are provided in section 6 .

This section discusses how green logistics (and sustainability) is shaping the planning of vehicle routing activities from the last mile perspective.

Green logistics is an area that focuses on manufacturing and delivering freight to avoid the depletion of scarce natural resources. We focus only on the distribution part of green logistics in this paper. From this standpoint, green vehicle routing is a specific research domain in green logistics that studies VRPs and related negative externalities. In this research domain, vehicles running on petroleum-based fuels (petrol or diesel) or alternative cleaner fuels are explicitly considered for a better and more efficient route planning.

The most studied negative externalities are GHG emissions. They are primarily generated from power stations, transportation and industrial processes. As the primary reference metric, the CO |$_2$| -equivalent (CO |$_2$| e) is used to compare emissions based on their global warming potential by translating other gases to the equivalent amount of CO |$_2$|⁠ . More specifically, all gaseous emissions from transportation can be converted to the amount of CO |$_2$| needed to create the same effect as CO |$_2$| e. The reduction of emissions is an essential topic for obvious reasons, and governments are trying to tackle this problem. Since 2016, transportation has become the largest emitting sector in the UK. The UK’s transportation sector was accountable for 27% of the total-generated emissions in 2019. Of the total emissions, a large share of emissions (91%) came from road transport vehicles in the same year ( BEIS, 2021 ). With regards to freight transportation, heavy goods vehicles were responsible for 18% of road transport emissions (equivalent to 19.5 MtCO |$_2$| e), and delivery vans were responsible for 17% of emissions (equivalent to 19 MtCO |$_2$| e). While road transportation was one of the sectors most affected by the pandemic, emissions are likely to increase as transport demand increases. Next to the generation of GHGs, the logistics industry also generates large amounts of air pollutants. These include particulate matter, CO (carbon monoxide), ozone (O |$_3$|⁠ ) and hazardous air pollutants.

As CO |$_2$| or CO |$_2$| e is directly proportional to fuel consumption, the generated (on-road) emissions can be calculated by looking at the fuel consumption rate. The ultimate goal in green vehicle routing is to produce greener transportation plans (or routes) based on fuel consumption estimation. However, the methodology for calculating emissions can be in different forms than each other. For example, vehicle-generated emissions depend on various factors, including vehicle occupancy and age, fuel type, engine temperature, vehicle speed and load. However, from an operational planning perspective, vehicle payload and speed are the more relevant and controllable factors in routing. Such discussions have started the green vehicle routing domain as various factors and methodologies are available in the literature. Significantly, the interest in fuel consumption modelling within routing domain has created a great deal of research in the operational research domain.

Next to emissions, the literature has also focused on other types of negative externalities. The other negative externalities of freight transportation include noise pollution, traffic congestion, road accidents and excessive land use. We refer the interested readers to literature on (see, e.g. Brons & Christidis, 2012 ; McAuley, 2010 ) for more details. Later, Demir et al. (2015) has also developed a comprehensive framework for negative externalities of road freight transportation as shown in Figure 1 .

The most common negative externalities of road transportation. Source: Demir et al. (2015) .

Figure 1 presents the details of negative externalities of road transportation. As highlighted in the figure, the focus should be on emissions, and all other externalities of transportation should be carefully considered through better and more efficient transport planning. We note that there is good progress on GHGs-related studies in the literature, but more research is needed for other types of negative externalities. From the supply chain management perspective, there is also good progress on sustainability. For example, Luis et al. (2021) developed an optimization model for a sustainable closed-loop supply chain network with conflicting objectives (i.e. the minimization of the total logistic costs and the total amount of carbon emissions). The authors provided a mathematical model and matheuristic algorithm to investigate the trade-offs between conflicting objectives.

The birth of green VRPs in the operational research domain has created various analytical methods for making better decisions in last mile logistics. Various authors have proposed mathematical formulations and solution algorithms tailored specifically for the reduction of emissions. Next to distance-minimization in routing problems, authors in this domain have proposed more comprehensive objective functions and dealt with more practical constraints. For example, vehicle speed and payload have become the most important decision variables for reducing emissions. Using different type of emissions modelling for the calculation of emissions required more complex and advanced analytical techniques. In their study, Leenders et al. (2017) investigated the allocation of emissions to a specific shipment in routing by considering more advanced fuel consumption formulae. The authors looked at terrain, distance, payload and the fuel consumption rates of empty and loaded vehicle. Their research highlights the importance of considering more holistic approach for estimating emissions and fuel consumption. Considering the complexity of fuel consumption modelling, there is still need for in-depth research for developing advanced methodologies, including exact and approximations methods.

This section briefly discusses how city logistics became an essential area of research in the logistics literature.

Logistics management is a complex but crucial activity. It includes supply, distribution, production and reverse logistics. Each of these dimensions looks at a different aspect of the supply network. The focus of our paper is the distribution of goods to customers. The e-commerce hype in the last decade has fundamentally changed the way customers purchase and consume products, and the expectations for delivery has also similarly changed over the years. Before the pandemic, 35% of industrial leasing could be attributed to the e-commerce business. In 2020, the e-commerce logistics market had grown more than 27%. To sustain profitable and environmental last mile delivery in urban areas, the topic of city logistics has gained more popularity in the transport industry. In simple terms, city logistics is considered the delivery and/or collection of parcels in cities. It also promotes cleaner transportation modes (i.e. rail, maritime), new handling and storage processes, reduced inventories and waste, reverse logistics, attended delivery, next-day, same-day and instant delivery services. From an operational perspective, the performance of city logistics requires seamless planning of vehicle routes to reduce empty miles, unnecessary driving and idling. In addition, city logistics operations require more efficient, light and modular vehicles that run on alternative or cleaner energy.

Similar to green vehicle routing, city logistics also pay attention to the environmental impact of all logistical operations in an urban environment. Savelsbergh & Van Woensel (2016) discuss the importance of city logistics for urban development. The authors also pointed out the requirements of city logistics, such as connectivity, big data and analytics, automation and automotive technology. Other aspects of city logistics are discussed by Taniguchi & Thompson (2018) , who particularly look at the impacts of city logistics on the environment.

One of the main tasks in city logistics is to establish coordination and consolidation opportunities between different stakeholders and it is a crucial success factor for the city logistics. Next to finding the right location decision, there is also need for zero and low-emission zones within urban areas (see, e.g. Lurkin et al. , 2021 ). The classic approach of running smooth city logistics activities is to consolidate freight volumes outside the city without creating unnecessary trips. Normally, the term urban distribution centres is used to refer these specific locations outside the city. From these locations, the handled freight is then moved into the cities using cleaner and alternative vehicle technologies or services. This two-level problem is also known as two-echelon distribution problem in the literature. By adding more distribution centres closer the cities, the supply chain can be extended to improve efficiency of both upstream and downstream ( Savelsbergh & Van Woensel, 2016 ). For a recent review paper we refer to Sluijk et al. (2022) . In the next section, we define the most applicable VRP formulation for the last mile logistics.

A fundamental last mile problem is to find a set of routes to serve a set of customers located in a geographical region. As the problem has many dimensions, such as a vehicle, operation, driver and fuel type, many studies focus on various dimensions of routing.

The VRP deals with designing vehicle routes subject to various constraints. The basic assumptions of the VRP can be listed as follows: (i) vehicle(s) must start and end at the same depot; (ii) each customer must be visited only once by a vehicle and (iii) the total payload in a vehicle must not surpass the available vehicle capacity. These assumptions are the basic features of the standard VRP. Due to customers’ requirements and operational challenges in last mile logistics, various VRPs and mathematical formulations have been proposed in the literature. We refer to studies on VRP and its variants for more details, see e.g. Toth & Vigo (2014) ; Vidal et al. (2020) . There are also other studies that look at more several practical constraints. For example, Derigs & Pullmann (2016) studied different strategies for the solution of a variety of rich VRPs with regards to solution quality and speed. The authors proposed variable neighbourhood search algorithm by considering several modules for different types of VRP features.

The standard VRP with distance minimization is known as the capacitated VRP (CVRP) and it can be defined mathematically as follows. We assume that a complete graph |${G} = ({N}, {A})$| includes node set |${N} = \{0, 1, 2,..., n\}$| and arc set |$\in{A} = \{{i,j}: {i,j} \in{N}, i \neq j\}$|⁠ . Each node (customer) |$i \in{N} \backslash \{0\}$| is defined with a demand q |$_i$|⁠ . The depot is considered as node |$0$|⁠ . All homogeneous vehicles ( m ) are located and available at the depot. Each arc ( i , j ) |$\in{A}$| is quantified with a distance d |$_{ij}$| between nodes |$i$| and |$j$|⁠ . Moreover, the vehicle capacity is denoted with |${Q}$|⁠ . The objective in the CVRP is to obtain a set of vehicle routes with the lowest total travelling distance. The closest CVRP variant is the distance constrained VRP (DVRP). In the DVRP, capacity-related constraints are changed with other constraints such that the length of a route must not surpass the defined distance range.

Another practical VRP variant is known as the VRP with pickup and delivery (VRPPD). This problem is finding a set of vehicle routes for a group of requests. This can be very relevant for LSPs who wish to simultaneously or subsequently serve pickup and delivery customers in the same route. There are also other variants of the VRPPD available in the literature. In the case of real-time vehicle routing optimization, dynamic VRP formulations can be used for dispatching vehicles to serve customers. Some parts of the transport plan must be decided beforehand, and the plans may need to be revised regularly in practice. This makes the routing problem more complex but practical for the logistics industry.

Another important variant is known as the production routing problem in the literature. This problem considers a more complex but practical planning problem that jointly optimizes production, inventory, distribution and routing. In the study of Shahrabi et al. (2021) , the authors studied the same problem with time windows, deterioration and split delivery. The authors specifically looked at the bi-objective (i.e. economic and social sustainability) model for a single product. They also proposed an interval robust approach and extensive analysis are conducted on a real-life case on a food factory.

The most relevant extension of the VRP in last mile logistics is the VRP with time windows (VRPTW). Next to customer’s demand, each customer should also be served within predefined time intervals. For all locations (a set of customers and depot) |$i (i \in{N}_0)$|⁠ , a time window |$[{a}_i, {b}_i]$| is defined. In this delivery problem, each customer has to be served within this interval. The delivery should begin at customer |$i (i \in{N}_0)$| just after the lower bound of time window a |$_i$| but not later than the upper bound of time window b |$_i$|⁠ . Also, if the vehicle arrives at customer |$i$| location before the start a |$_i$|⁠ , the vehicle should wait the time a |$_i$| to commence delivery.

As an example VRP model formulation, a mixed-integer linear programming model for the VRPTW is presented below. The following decision variables are used for the model.

The objective function ( 4.1 ) is the minimization of the total distance. Constraints ( 4.2 ) ensure that a vehicle must departure from the depot. Constraints ( 4.3 ) and ( 4.4 ) are the degree constraints to ensure each customer is visited one time only. Constraints ( 4.5 ) and ( 4.6 ) state the flows of payload on each arc chosen in a solution. Constraints ( 4.7 )–( 4.9 ), where |$K$| and |$L$| are large numbers. They also ensure the time window features of the problem. Constraints ( 4.10 )–( 4.12 ) define non-negativity conditions.

This section provides a discussion on recent trends and developments in the last mile logistics. More specifically, we discuss how these contemporary topics affect last mile logistics practices.

When considering new technological instruments for adoption, one may consider the ‘Law of Disruption’ model, which is proposed by Downes (2009) . The author explains how digital life has changed and how technology develops exponentially while social, economic and legal systems change incrementally. This law presents a pattern of how different types of change manifest themselves. The author also points out that technological innovations are generally ahead of social and political change. As in other industries, we can also expect regulatory barriers or negative public perception to remain in effect in the next 5–10 years for the logistics sector. This is more or less the case for all technologies and innovations discussed here. Especially, there is a need for mathematical proofs and evidence before the actual implementation. Mathematical modelling and optimization can help promoting these technologies and innovations by providing quantitative justification. More research can aid policy makers and governments to take action for greener transportation, especially within populated urban areas. We will now discuss some of these latest developments to attract more attention to current technologies and environmental concerns.

5.1 Unmanned aerial vehicles (drone)

An unmanned aerial vehicle (UAV) is an aircraft without any pilot. It can be fully or partially autonomous. This new technology is available for use in freight transportation, and a wide range of research is available in the literature. Interested readers are referred to original review papers on UAVs by Macrina et al. (2020) ; Rojas Viloria et al. (2021) and Rovira-Sugranes et al. (2022) .

In a recent study, Kundu et al. (2021) studied a variant of the travelling salesman problem (TSP) as denoted flying sidekick TSP. In this variant, the authors consider a single vehicle case using only one drone to serve customers. In this problem setting, drone can be launched from the vehicle at customer location. The driver and drone can simultaneously deliver packages. The authors propose a novel split algorithm and heuristic method to the studied problem. Freight transportation can benefit from UAVs as they can be used to deliver goods in the last mile ( DHL, 2014 ). Primarily, customers are interested in receiving their orders with the use of UAVs. Even though there are several advantages, it will not be easy to replace traditional road vehicle-only transportation soon. However, we have seen various small applications or trials of UAVs used in recent years. During the pandemic, companies have successfully deployed UAVs for last mile delivery. UAV technologies can be a sustainable option in the context of the last mile. These resources are already utilized by logistics and retailer companies, such as DHL International, United Parcel Service and Amazon.

From an operational perspective, UAVs can play a vital role in last mile logistics as they are fast and capable of carrying multiple packages in different weights. However, legal challenges and public perception need to be addressed before utilizing them in urban areas.

5.2 Unmanned ground vehicles (delivery robot)

An unmanned ground vehicle (UGV) is a type of vehicle that is operated on the ground without an onboard human presence. They can be used for transportation in urban areas to minimize delivery times. As a practical solution, the integration of UGVs with delivery vans can offer greener solutions than using only delivery vans. As UGVs are powered by clean electricity, they do not produce emissions themselves. As a successful trial, Starship Technologies had been experimenting with the delivery system with UGVs in London in 2020. Chen et al. (2021) studied an urban delivery problem using robots as assistants. In their delivery system, the traditional delivery van serves the customer and acts as a mothership for its robots in the meantime. When the van is parked, robots can be dispatched to their target customer(s) and return to the same place where they depart from to rendezvous with the mothership van. This is a very realistic example of UGVs’ use in practice.

From an operational perspective, UGVs have particular advantages over UAVs. Since most UAVs are powered by small-capacity batteries that last less than half an hour (on average), their capacities and flying ranges are quite limited. However, UGVs have more loading capacity, and their range is much more than UAVs. With an integrated delivery van and UGVs, drivers can also supervise UGVs in certain areas, which is not the case for UAVs.

5.3 Collection and delivery points

As an alternative solution in urban areas, collection and delivery points can improve the logistics efficiency and reduce emissions. Especially, in populated city centres or in the proximity of heavy footfall areas, these points can be preferred by customers. In the study of Janjevic et al. (2019) , the authors proposed a new method for the integration of collection and delivery points in the design of multi-echelon logistics systems based on a real-life case study. The benefits of using these systems are quantified by showing significant cost benefits for companies involved in last mile logistics.

Weltevreden (2008) studied collection and delivery points in the Netherlands and its consequences for other stakeholders. The author showed that these locations are most used for returning online orders. For retailers operating a service point may lead to additional revenues. In recent study, Kedia et al. (2020) looked at to identify the optimal density and locations for establishing collection and delivery points in New Zealand. The authors modelled the problem as a set covering problem by considering city demographics and travel distance between population centres and potential facility locations. New type of points such as dairies and supermarkets were found to be more accessible than traditional post shops.

5.4 Truck platooning

The arrival of autonomous vehicles is an opportunity to improve people’s lives and protect the environment. These vehicles also contribute to advancing the sustainable development agenda. One of the application areas of autonomous vehicles is platooning, which links two or more vehicles (trucks) together to create a form of train. Generally, LSPs aim to make their operations more efficient by utilizing their resources (i.e. fleet, labour etc.) ( Ghiani et al. , 2013 ). These companies are also paying close attention to their environmental footprint. Early adopters of truck platooning can bring a competitive advantage amongst LSPs. Countries are also interested in automation and, more particularly, truck platooning. Most of the autonomous vehicle projects in Europe are done by collaborating with different organizations and countries. Cooperation of actors, especially in the European Union (EU), is progressing well since EU countries have similar legislation.

Truck platooning will contribute to the transport industry, including improved traffic management, reduced operational costs and operations ( Tavasszy & Janssen, 2016 ). Next to these advantages, truck platooning will also make the logistic operations more efficient and optimize the labour market. Platooning will also optimize the supply network from a higher perspective. This will eventually reduce CO |$_2$| e emissions and minimize congestion by improving traffic flows with reduced tailbacks. Truck platooning can be more efficient for longer distances and heavy good vehicles. The possibility to platoon with different trucks or multi-brand platooning is also needed to form vehicles in a platoon successfully.

5.5 Collaborative logistics

Generally, last mile delivery solutions are individually managed by retailers and LSPs. Due to competitiveness of the last mile delivery market, there is little room for joint and synchronized solutions. Collaborative logistics can address the challenges of last mile by increasing cost efficiency and utilization. The major challenge in last mile logistics is that the demand points are often located in highly congested urban areas and they are quite far from distribution centres. In the study of De Souza et al. (2014) , the authors looked at industry alignment through a synchronized marketplace concept by using clusters of customers, suppliers and service providers in Singapore.

Park et al. (2016) studied the collaborative delivery problem to measure the effects of collaboration for apartment complexes in Korea. Potential benefits are also quantified in this study and the role of the public sector is considered to be essential.

5.6 Integrated transport

As a promising business model, integrating freight flows with public scheduled transportation can be a viable option for freight transportation. A successful synchronization of delivery vehicles with scheduled public transport is directly related to coordination, which is the critical factor for seamless movement of freight in the last mile ( Ghilas et al. , 2016 ).

As public transportation systems have particular coverage, specific delivery trips of delivery vans may overlap with the scheduled line services. Using public transportation instead of delivery vans may reduce transportation cost and create environmental benefits. Due to the shorter driving time of their delivery vans, LSPs may reduce their operational costs. Less travel time also leads to reduced amount of CO |$_{2}$| e emissions. It is not an easy task to coordinate both delivery vans and public scheduled lines from an operational perspective. However, this system can be a viable option for the industry, especially in rural areas.

5.7 Decarbonization

By definition, decarbonization in road freight reduces transportation-related activities’ carbon footprint (GHGs). Reducing emissions in every industry is essential to ensure global temperature standards set by the Paris Agreement and governments. As the share of last mile increases due to e-commerce sales, more research and green thinking are needed for the industry.

In 2021, the Department for Transport of the UK published a policy plan on decarbonizing transport to meet the UK’s net-zero targets ( Department for Transport, 2021 ). Some of the proposed initiatives include: phasing out the sale of all new non-zero emission HGVs; demonstrating zero emission HGV technology on UK roads; stimulating demand for zero-emission trucks with financial and other incentives; supporting efficiency improvements and emission reductions in the current fleet; and also taking new measures to transform last mile deliveries. From this perspective, two technologies look prominent for last mile logistics. These include electric vehicles and green hydrogen, and these options could help reduce the environmental impact of last mile logistics.

5.8 Towards transport analytics: the role of data and information

The Internet of Things is known as the network of physical objects to enable data and information exchange between different physical and virtual objects. Last mile logistics and transportation can also benefit from information sharing on inventory, supply chain, resources and people. However, although promising, it is a great challenge to change the logistics systems and its related overwhelming daily operations. It requires the involvement of various stakeholders to act together for all types of operations.

It is important to consider different analytical approaches with information sharing capability for the last mile logistics. In the study of Krushynskyi et al. (2021) , the authors investigate two policies to improve the efficiency of the LSP by allowing more flexibility in choosing the delivery locations. The considered policies include roaming vehicle routing and the second policy allows the possibility of aggregating certain locations. The problem is modelled as TSP real-life parcel delivery data are analyzed. The authors points out that the two proposed policies can lead to significant improvements in the route length.

In order to improve the efficiency of last mile logistics, all processes during the transportation should be improved. Such improvement can be achieved by using advanced analytics, artificial intelligence (AI) and blockchain systems. Historical logistics data can be utilized to proactively reduce the vulnerability of traffic networks and improve the communication between transport users with real-time data. For example, AI-enhanced decision-making capabilities can provide real-time information and actionable suggestions for the planning of vehicle routes. In a related study, Ozarik et al. (2021) studied VRP in which customer presence probability data are explicitly considered in the planning of routes. As the unavailability of customers is a major problem for the logistics industry, the real-time location information of customers can improve the delivery service and reduce the unnecessarily generated emissions.

The last mile delivery is the most complicated part of the supply network. It deals with the movement of goods from a hub to their final destination. This is normally the customer’s doorstep. It is essential to make the delivery as efficient as possible while minimizing all operational costs. Due to urbanization and population growth, this final step of transportation is becoming increasingly important. Customers prefer to have on-time delivery, and this might be a challenge for the industry because of various uncertainties. Because of these challenges, there is a growing need to provide LSPs with relevant evidence, strategies and decision-making tools to help them plan better.

Academic research in last mile logistics has successfully considered new trends and technological developments in scientific investigations. However, there is a need for more research focusing on more operational and tactical issues related to routing optimization. Our short positioning paper has looked at various dimensions of last mile logistics and discussed the outlook of future research needs by the industry.

The future of last mile logistics will be shaped by technology, innovation and customer requirements. There is already good progress for using advanced technology in logistics. Digitization, automation and robotic systems will help LSPs to handle last mile operations more efficiently. The industry will also pay more attention to sustainability and decarbonization as the share of emissions from transportation must be reduced sharply in the next 10 years in many countries.

Building upon findings of our research, we can make the following recommendations for the adoption of the latest technologies and innovations in the last mile logistics.

The unending customer requirements must be addressed by promoting greener last mile delivery services through the use of advanced mathematical optimization techniques. In particular, there is a need for developing proactive and robust algorithms specifically designed for dynamic traffic environments.

The negative externalities of freight transportation and social indicators must also be considered within route optimization along with economic indicators. There is good progress on the environmental sustainability, but more research is needed to tackle social sustainability.

The barriers influencing the adoption of the latest technological solutions and innovations must be dealt with using quantitative data generated with the help of operational research techniques.

AI-enhanced decision-making approaches should be used based on the available data for creating vehicle routes and schedules. The algorithms should be suitable for processing large amounts of data within reasonable solution times.

Sincere thanks are due to the Operations Area Editor of IMAMAN for the opportunity to organize this special issue. We also thank two anonymous reviewers for their useful comments and for raising interesting points for discussion.

BEIS ( 2021 ) 2019 UK Greenhouse Gas Emissions, Technical report . Department of Business, Energy & Industrial Strategy , London: United Kingdom.

Google Scholar

Google Preview

Brons , M. & Christidis , P. ( 2012 ) External cost calculator for marco polo freight transport project proposals . Technical Report . Call 2013 Version. Joint Research Centre, Seville, Spain.

Chen , C. , Demir , E. & Huang , Y. ( 2021 ) An adaptive large neighborhood search heuristic for the vehicle routing problem with time windows and delivery robots . Eur. J. Oper. Res. , 294 , 1164 – 1180 .

Dantzig , G. B. & Ramser , J. H. ( 1959 ) The truck dispatching problem . Manage. Sci. , 6 , 80 – 91 .

De Souza , R. , Goh , M. , Lau , H.-C. , Ng , W.-S. & Tan , P.-S. ( 2014 ) Collaborative urban logistics–synchronizing the last mile a Singapore research perspective . Procedia Soc. Behav. Sci. , 125 , 422 – 431 .

Dekker , R. , Bloemhof , J. & Mallidis , I. ( 2012 ) Operations research for green logistics–an overview of aspects, issues, contributions and challenges . Eur. J. Oper. Res. , 219 , 671 – 679 .

Demir , E. , Bektaş , T. & Laporte , G. ( 2014 ) A review of recent research on green road freight transportation . Eur. J. Oper. Res. , 237 , 775 – 793 .

Demir , E. , Huang , Y. , Scholts , S. & Van Woensel , T. ( 2015 ) A selected review on the negative externalities of the freight transportation: modeling and pricing . Transp. Res. E Logist. Transp. Rev. , 77 , 95 – 114 .

Department for Transport ( 2021 ) Decarbonising Transport: A Better, Greener Britain . London, UK : Report .

Derigs , U. & Pullmann , M. ( 2016 ) A computational study comparing different multiple neighbourhood strategies for solving rich vehicle routing problems . IMA J. Manage. Math. , 27 , 3 – 23 .

DHL ( 2014 ) Unmanned Aeiral Vehicle in Logistics: A DHL perspective on implications and use cases for the logistics industry . Technical Report, DHL Trend Research. Toisdorf: Germany.

Downes , L. ( 2009 ) The Laws of Disruption: Harnessing the New Forces That Govern Life and Business in the Digital Age . Basic Books (AZ) .

Gevaers , R. , Van de Voorde , E. & Vanelslander , T. ( 2014 ) Cost modelling and simulation of last-mile characteristics in an innovative b2c supply chain environment with implications on urban areas and cities . Procedia Soc. Behav. Sci. , 125 , 398 – 411 .

Ghiani , G. , Laporte , G. & Musmanno , R. ( 2013 ) Introduction to Logistics Systems Management . John Wiley & Sons . West Sussex: United Kingdom.

Ghilas , V. , Demir , E. & Van Woensel , T. ( 2016 ) An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows and scheduled lines . Comput. Oper. Res. , 72 , 12 – 30 .

Irawan , A. C. , Abdulrahman , M. D-A. , Salhi , S. & Luis , M. ( 2022 ) An efficient matheuristic algorithm for bi-objective sustainable closed-loop supply chain networks . IMA J. Manage. Math ., 00 , 1–34.

Janjevic , M. , Winkenbach , M. & Merchán , D. ( 2019 ) Integrating collection-and-delivery points in the strategic design of urban last-mile e-commerce distribution networks . Transp. Res. E Logist. Transp. Rev. , 131 , 37 – 67 .

Kedia , A. , Kusumastuti , D. & Nicholson , A. ( 2020 ) Locating collection and delivery points for goods’ last-mile travel: a case study in New Zealand . Transp. Res. Procedia , 46 , 85 – 92 .

Krushynskyi , D. , Guo , X. & Claassen , G. ( 2021 ) Location flexibility in parcel delivery operations: framework and empirical analysis . IMA J. Manage. Math ., 00 , 1–19.

Kundu , A. , Escobar , R. G. & Matis , T. I. ( 2021 ) An efficient routing heuristic for a drone-assisted delivery problem . IMA J. Manage. Math ., 00 , 1–19.

Leenders , B. P. , Velázquez-Martínez , J. C. & Fransoo , J. C. ( 2017 ) Emissions allocation in transportation routes . Transp. Res. Part D , 57 , 39 – 51 .

Lurkin , V. , Hambuckers , J. & Van Woensel , T. ( 2021 ) Urban low emissions zones: a behavioral operations management perspective . Transp. Res. Part A Policy Pract , 144 , 222 – 240 .

Macrina , G. , Pugliese , L. D. P. , Guerriero , F. & Laporte , G. ( 2020 ) Drone-aided routing: a literature review . Transp. Res. Part C Emerg Technol , 120 , 102762.

Marrekchi , E. , Besbes , W. , Dhouib , D. & Demir , E. ( 2021 ) A review of recent advances in the operations research literature on the green routing problem and its variants . Ann. Oper. Res. , 304 , 529 – 574 .

McAuley , J. ( 2010 ) External costs of inter-capital freight in Australia. Australasian Transport Research Final Report: Actions to Promote Intermodal Transport (ATRF) . Australasian Transport Research Forum. Canberra: Australia.

Meersman , H. & Van de Voorde , E. ( 2019 ) Freight transport models: ready to support transport policy of the future?   Transp. Policy , 83 , 97 – 101 .

Moghdani , R. , Salimifard , K. , Demir , E. & Benyettou , A. ( 2021 ) The green vehicle routing problem: a systematic literature review . J. Cleaner Prod. , 279 , 123691.

Ozarik , S. S. , Veelenturf , L. P. , Van Woensel , T. & Laporte , G. ( 2021 ) Optimizing e-commerce last-mile vehicle routing and scheduling under uncertain customer presence . Transp. Res. E Logist. Transp. Rev. , 148 , 102263.

Park , H. , Park , D. & Jeong , I.-J. ( 2016 ) An effects analysis of logistics collaboration in last-mile networks for cep delivery services . Trans. Policy , 50 , 115 – 125 .

Rojas Viloria , D. , Solano-Charris , E. L. , Muñoz-Villamizar , A. & Montoya-Torres , J. R. ( 2021 ) Unmanned aerial vehicles/drones in vehicle routing problems: a literature review . Int. Trans. Oper. Res. , 28 , 1626 – 1657 .

Rovira-Sugranes , A. , Razi , A. , Afghah , F. & Chakareski , J. ( 2022 ) A review of AI-enabled routing protocols for UAV networks: trends, challenges, and future outlook . Ad Hoc Netw. , 130 , 1–27.

Savelsbergh , M. & Van Woensel , T. ( 2016 ) 50th anniversary invited article-city logistics: challenges and opportunities . Transp. Sci. , 50 , 579 – 590 .

Shahrabi , F. , Tavakkoli-Moghaddam , R. , Triki , C. , Pahlevani , M. & Rahimi , Y. ( 2021 ) Modelling and solving the bi-objective production–transportation problem with time windows and social sustainability . IMA J. Manage. Math ., (2021), 00 , 1–26.

Sluijk , N. , Florio , A. M. , Kinable , J. , Dellaert , N. & Van Woensel , T. ( 2022 ) Two-echelon vehicle routing problems: a literature review . Eur. J. Oper. Res. , 1.

Taniguchi , E. & Thompson , R. G. ( 2018 ) City Logistics 3: Towards Sustainable and Liveable Cities . John Wiley & Sons . New Jersey: USA.

Tavasszy , L. & Janssen , R. ( 2016 ) On the Value Case for Truck Platooning in Europe, in ‘ITS European Congress’ . Glasgow : UK .

Toth , P. & Vigo , D. ( 2014 ) Vehicle Routing: Problems, Methods, and Applications . SIAM . Philadelphia: USA.

Vidal , T. , Laporte , G. & Matl , P. ( 2020 ) A concise guide to existing and emerging vehicle routing problem variants . Eur. J. Oper. Res. , 286 , 401 – 416 .

Weltevreden , J. W. ( 2008 ) B2c e-commerce logistics: the rise of collection-and-delivery points in the Netherlands . Int. J. Retail Distrib. Manage. , 36 , 638 – 660 .

Email alerts

Citing articles via.

• Recommend to your Library

Affiliations

• Online ISSN 1471-6798
• Copyright © 2024 Institute of Mathematics and its Applications
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

• Social Media

• Publications
• Journal LOGISTICS RESEARCH

LOGISTICS RESEARCH Journal Submit Online

The international journal LOGISTICS RESEARCH - certified C-Journal - is published by BVL. The first print edition appeared in March 2009.

The mission of the journal is to create an interdisciplinary, cross-sector platform for the publication of scientific articles of a high standard. These articles should be geared towards increasing and expanding the knowledge of the worldwide research community in the fields of logistics and supply chain management. Manuscripts in English are double-blind reviewed by the international Editorial Board of the journal and editorially supervised through to actual publication.

One of the core goals of LOGISTICS RESEARCH is to underpin scientific communication and the status of relevant research both within the logistics community and in the public arena - as well as to promote the recognition and profile of this field and its research work to the "outside world", vis-à-vis the more established sciences and in countries where German and other European activities in this area have been under-publicised to date.

Scientists and researchers, postdocs and PhD candidates are invited to submit their manuscripts from the fields of business management science, other areas of economic and social science, engineering, IT or operations research for review. Via the online portal, they can also view rough drafts and "workshop" notes before formally submitting contributions and request comments regarding their suitability for inclusion in LOGISTICS RESEARCH. We do not charge any fee for publication or submission to the authors. 

At irregular intervals, special issues are introduced and published. 

Volume 17 January 2024

• January 2024, Issue 1, Articles 1-x

Volume 16 January 2023

• January 2023, Issue 1, Articles 1-12

Volume 15 December 2022

• December 2022, Issue 1, Articles 1-12

Volume 14 December 2021

• December 2021, Issue 1, Articles 1-6

Volume 13 December 2020

• December 2020, Issue 1, Articles 1-10

Volume 12 December 2019

• December 2019, Issue 1, Articles 1-10

Volume 11 December 2018

• December 2018, Issue 1, Articles 1-10

Volume 10 December 2017

• December 2017, Issue 1, Articles 1-12

Volume 9 December 2016

• December 2016, Issue 1, Articles 1-25

Volume 8 December 2015

• December 2015, Issue 1, Articles 1-8

Volume 7 December 2014

• December 2014, Issue 1, Articles 112-119

Volume 6 March 2013 - December 2013

• December 2013, Issue 4, Pages 119-261
• June 2013, Issue 2-3, Pages 45-118
• March 2013, Issue 1, Pages 1-43

Volume 5 August 2012 - November 2012

• November 2012, Issue 3-4, Pages 77-139
• August 2012, Issue 1-2, Pages 1-76

Volume 4 March 2012 - May 2012

• May 2012, Issue 3-4, Pages 83-156
• March 2012, Issue 1-2, Pages 1-81

Volume 3 April 2011 - October 2011

• October 2011, Issue 4, Pages 177-248
• May 2011, Issue 2-3, Pages 67-175
• April 2011, Issue 1, Pages 1-65

Volume 2 June 2010 - December 2010

• December 2010, Issue 3-4, Pages 121-176
• September 2010, Issue 2, Pages 55-120
• June 2010, Issue 1, Pages 1-53

Volume 1 March 2009 - December 2009

• December 2009, Issue 3-4, Pages 129-196
• September 2009, Issue 2, Pages 69-127
• March 2009, Issue 1, Pages 1-68

Robust Manufacturing Control: Robustness and Resilience in Global Manufacturing Networks

Dynamics in logistics: digital technologies and related management methods, ‘‘logistics in the networked industry’’ based on the bvl’s 7th international scientific symposium on logistics in cologne in 2014, ‘‘understanding future logistics ’’ based on the bvl’s 9th international scientific symposium on logistics in magdeburg in 2018, s.i.: logistics management 2021: the german perspective, s.i.: 10th international scientific symposium on logistics, dynamics in logistics – models and algorithms for optimisation, planning, and control, supply chain analytics in the 2020s, issl21: logistics for a sustainable future-contributions from science, unterkategorien.

Logistics Research

Logistics Research is a peer-reviewed open access journal published by BVL. The Journal features fundamental and applied research in the fields of logistics and supply chain management from an ...

Latest Articles

The international journal LOGISTICS RESEARCH - certified C-Journal - is published by BVL. The first print edition appeared in March 2009. The mission of the journal is to create an interdisciplinary ...

All Volumes and Issues

Instructions for Authors

Aims and Scope

Logistics Research is the international medium for the publication of academic research in the field of logistics from an interdisciplinary perspective spanning from logistics management to logistics ...

LOGISTICS RESEARCH Journal

Copyright and License Agreement

Articles in Logistics Reaearch do not require transfer of copyright as the copyright remains with the author. In confirming the publication of your article with open access you agree to the Creative ...

Editorial Board

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

A study on the interaction between logistics industry and manufacturing industry from the perspective of integration field

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation School of Economics and Management, Chang’an University, Xi’an, Shaanxi, China

Roles Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing

Roles Funding acquisition, Validation, Writing – review & editing

Roles Software, Validation, Writing – review & editing

* E-mail: [email protected]

Affiliation College of Transportation Engineering, Chang’an University, Xi’an, Shaanxi, China

Roles Visualization

• Borui Yan, 
• Qianli Dong, 
• Qian Li, 
• Lei Yang, 
• Fahim U. I. Amin

• Published: March 3, 2022
• https://doi.org/10.1371/journal.pone.0264585
• Reader Comments

Studying the linkage between manufacturing industry and logistics industry is conducive to explore and improve the efficiency of the common development of them. In order to study the interaction of logistics industry on the development of manufacturing industry and the development of two-industry-linkage, it first calculates the high-quality development level of logistics industry and manufacturing industry, then uses the coupling coordination model to theoretically analyze and empirically test the coupling and coordinated development level of high-quality development of logistics industry and manufacturing industry from three aspects: coupling degree, coordination degree and coupling coordination degree, and based on the perspective of integration field theory, it takes the three basic synthetic fields of logistics integrator, logistics base-nuclear and logistics connection-key as the analysis dimension, PVAR model was introduced for in-depth analysis the impact of logistics industry on manufacturing industry and the level of the two-industry-linkage. It was found that the high-quality development of China’s logistics industry and manufacturing industry is close on the whole, and the development trend is consistent, the high-quality development of them is mainly caused by the change of scale, but there is no obvious change in technical efficiency, which also provides a way for the high-quality development of the two-industry-linkage in the future. The two-industry-linkage mostly belongs to the situation of low-level mutual restriction, which has not yet reached a high level of mutual promotion, resulting in the overall coupling coordination degree basically in a state of barely coordination. The development of logistics industry and manufacturing industry need to go through certain practice and running in, when there is an error matching between the two, the logistics industry will inhibit the two-industry-linkage. When the economy develops to a certain extent, the expansion of the logistics system scale to the level of the two-industry-linkage is not necessarily beneficial, blindly exceeding the demand for logistics investment will cause a waste of resources, which is not conducive to the high-quality development of the logistics industry and the coupling and coordinated development of the two industries. In the long run, the change of the logistics basic-nuclear capacity, the logistics integrator scale and logistics connection-key level will have a positive impact on the change of green total factor productivity in manufacturing industry.

Citation: Yan B, Dong Q, Li Q, Yang L, Amin FUI (2022) A study on the interaction between logistics industry and manufacturing industry from the perspective of integration field. PLoS ONE 17(3): e0264585. https://doi.org/10.1371/journal.pone.0264585

Editor: J E. Trinidad Segovia, University of Almeria, SPAIN

Received: October 15, 2021; Accepted: February 13, 2022; Published: March 3, 2022

Copyright: © 2022 Yan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data underlying our findings can be found at: ( https://osf.io/hk2mv/ ).

Funding: This research was funded by National Social Science Foundation of China, grant number 20AJY015. The funder had role in decision to publish the manuscript. ROLES: Conceptualization, Resources, Supervision, Project administration, Funding acquisition.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Economic development needs the optimization of industrial structure, the upgrading and value of manufacturing industry. Manufacturing and logistics belong to the core supply category of market economy and are important components of the real economy. In the environment of fierce global competition, great innovation pressure and short response time to customer needs, enterprises are facing the problem of improving their logistics system. The increasing trend of logistics digitization and autonomy in Industry 4.0 is changing the existing logistics process [ 1 , 2 ]. The manufacturing industry needs to be upgraded, not only to realize the benefits of commodity production, but also to upgrade and change the functions of planning, supply chain logistics and even product development [ 3 ]. The logistics industry and related industries interact with each other, focusing on the core interests of their respective industries and forming the relationship of industrial cooperation and interaction under the principle of complementarity. Studying the linkage between manufacturing and logistics is conducive to exploring and improving the efficiency of the common development of them, and promoting the optimization of regional economic structure and industrial upgrading.

The concept of the two-industry-linkage was first put forward at the First Joint Development Conference of China’s Manufacturing Industry and Logistics Industry in September 2007. It means that the logistics industry and manufacturing industry form a reasonable division of labor and cooperation system through strategic adjustment to realize complementary advantages and coordinated development, so as to optimize the industrial structure, enhance the industrial energy level and the industrial competitiveness [ 4 ]. The two-industry-linkage has attracted the attention of more and more scholars and government departments, and the empirical research on the interaction between the two industries has gradually increased. For example, Fan et al. [ 5 ] proposed that integrating innovative resources and promoting linkage development have a certain positive effect on improving the efficiency of logistics enterprises in urban agglomeration. Liu et al. [ 6 ] proposed that the interactive development of manufacturing industry and logistics industry affects the price level by improving product supply capacity and consumption capacity, reducing trade costs. Su et al. [ 7 ] proposed that the coupling of the logistics industry and manufacturing industry has a positive impact on the productivity of the manufacturing industry. Some scholars studied the symbiotic evolution relationship between industries and even industrial integration [ 8 , 9 ], and some scholars used the system coupling theory to describe the process of industrial integration [ 10 ]. Especially with the concepts of Industry 4.0 and Made in China 2025 put forward, there are more and more studies on the integrated development of the two industries. At the same time, environmental awareness generally promotes the development of sustainable supply chain management. Manufacturing enterprises try to seek sustainable business strategies to deal with the pressure of the market on corporate social responsibility, and sustainable logistics service has become one of the most practical strategies [ 11 ]. In the context of high-quality development, how the logistics industry affects the manufacturing industry and the level of two-industry-linkage needs to be studied.

The rest of this paper is arranged as follows. The second section is the collation of relevant literature, the third section is the methodology, and the fourth section is the analysis results, mainly including the interactive impact of logistics industry and the comprehensive technical efficiency of manufacturing industry, the interactive impact of logistics industry and the change rate of green total factor productivity of manufacturing industry, and the impact of logistics industry on the level of two-industry-linkage, the last section is the conclusion and future research direction.

Literature review

In recent years, economic globalization and manufacturing resource globalization, as two key factors, have prompted enterprises to change their business processes in order to survive in a competitive environment. Logistics is the key factor of modern networked manufacturing [ 12 ], effective logistics control is the main factor determining the competitiveness of manufacturing industry [ 13 ], logistics performance has become the key factor for the success of modern manufacturing enterprises [ 14 , 15 ], logistics management has also become an important part of many manufacturers’ competitive strategies [ 16 , 17 ]. Logistics can help manufacturing and distribution meet the increasingly stringent requirements of the global market [ 18 ], while manufacturing control more directly affects logistics objects [ 19 ], and logistics performance can be improved by taking advantage of the flexibility potential in manufacturing and assembly [ 20 ]. Logistics engineering is a broader part of manufacturing engineering, which can create the most competitive manufacturing process in the whole supply chain.

The early research on logistics and manufacturing mainly focused on the impact of logistics on the cost of manufacturing enterprises and the role of improving market response ability [ 21 – 23 ]. Some studies show that when the strategy and structure of manufacturing enterprises are consistent with the inherent advantages in enterprise logistics selection, the performance will be higher [ 24 ], and some studies explore the best combination of manufacturing and logistics services [ 25 ]. Some studies began to explore how to optimize the selected intertwined system of manufacturing and logistics, so as to achieve the expected level of manufacturing system [ 26 – 28 ]. Some studies have also begun to explore the mode of the two-industry-linkage. For example, Yang [ 29 ] proposed that the industrial linkage mode between the logistics industry and the manufacturing industry can be divided into six types according to the degree of logistics self-support and outsourcing: complete logistics self-support, partial outsourcing of logistics business, establishment of logistics professional companies, logistics strategic alliance, takeover of logistics system, and complete outsourcing of logistics business.

Some studies discussed the relationship between the two industries, mainly using Logistic model, DEA, grey correlation analysis, and composite system coordination model, coupled coordination model, input-output analysis, VAR model, PVAR model, etc. The measurement indicators of the level of two-industry-linkage mainly include: influence coefficient and induction coefficient, symbiosis degree and symbiosis coefficient, grey correlation degree, grey grid correlation degree, comprehensive validity of collaborative development (DEA), order degree and coordination degree, coupling coordination degree. This paper will use PVAR model [ 30 ] and the coupling coordination degree [ 31 ] to analysis the level of two-industry-linkage.

Manufacturing industry and logistics industry are highly related, deeply complementary, and interactive development has become the inevitable trend of their development [ 32 ], and will even develop into the integration of logistics chain and supply chain [ 33 ], it even affects the manufacturing industry and international logistics operation [ 34 ]. At the same time, as more and more manufacturing enterprises realize the importance of environment, they also begin to pay attention to the cost, carbon emission and energy consumption in the logistics process related to manufacturing enterprises [ 35 , 36 ], and begin to explore how to improve the environmental performance of logistics process [ 37 ]. Through the implementation of green efforts in the logistics system, manufacturing enterprises can improve their efficiency, in addition to realizing the basic organizational objectives of manufacturing enterprises, they can also obtain some other long-term benefits [ 38 ], such as green supply chain management practices are much useful to improve environmental sustainability through a reduction in carbon emissions and PM2.5, it spur economic growth in terms of providing trade opportunities around the globe [ 39 ]. Although the green logistics related to manufacturing industry has received a certain degree of attention [ 40 ], it is still worth studying how the logistics industry will affect the manufacturing industry and the two-industry-linkage under the background of green development.

The research on the high-quality development of manufacturing industry was born with the research on the high-quality development of China’s economy. The connotation of high-quality development of manufacturing industry is closely related to evaluation, which mainly includes three views. The first view is that high-quality development of manufacturing industry mainly includes qualitative development (such as export technology complexity) and quantitative development (total output value of manufacturing industry) [ 41 ]; The second view is that it is mainly the adoption of advanced technology and the optimal allocation of resources [ 42 ]; The third view is that it should be considered from multiple dimensions, which has been recognized by many scholars [ 43 ]. The dimensions recognized by most scholars include economic benefits, structural optimization, innovative development, green development, opening up and social effects, which are similar to the five dimensions of high-quality economic development "innovation, coordination, green, opening and sharing", it is reflected from the side that most of these dimensions of high-quality development of manufacturing industry are derived from the connotation of high-quality economic development, but this approach also leads to a hidden danger, that is, some measurement index systems of high-quality development of manufacturing industry established on this basis overlap with the index system of high-quality economic development. For example, when investigating the dimension of opening up, some studies used the index of foreign capital dependence, which was widely used in the measurement system of high-quality economic development. Although the high-quality development of manufacturing industry is an important representation of high-quality economic development, there are still many differences between the two. Moreover, if many indicators coincide, it is doubtful whether it will lead to the error of the measurement results in the model with both high-quality manufacturing development and high-quality economic development. In fact, when investigating the high-quality development of manufacturing industry under the background of high-quality economic development, manufacturing total factor productivity can be used to measure the high-quality development level of manufacturing industry, which has also been recognized by many scholars [ 44 ]. The core of high-quality development of manufacturing industry is the improvement of manufacturing productivity (efficiency), which is the basis for sustainable economic development and the transformation of economic growth mode from extensive to intensive [ 45 ]. There are usually three methods to measure the efficiency of manufacturing industry, the first method is labor productivity (total industry output / total employment) [ 46 ], the second method is output rate, and the third method is to estimate the efficiency by using data envelopment analysis or stochastic frontier production function. The third method is the one used more, but scholars selected different input-output indicators. Investment indicators include: number of employees [ 47 ], and capital stock [ 47 ], number of business units [ 48 ], net value of fixed assets [ 49 ], total energy consumption [ 47 , 50 ], etc.; Output indicators include industrial sales value [ 51 ], main business income [ 48 ], total output value [ 50 ], industrial added value [ 52 ], pollution emission [ 47 , 49 ], etc.

High-quality development of logistics is an integral part of high-quality economic development [ 53 ]. It includes at least two meanings, first, the logistics industry has high development quality, which is reflected in high efficiency, high service level, strong endogenous power, sound industry, green environmental protection, etc., with the characteristics of "innovation, coordination, green, opening and sharing"; Second, the logistics industry can serve the social economy and people’s life with high-quality, strongly support the national economic development and meet the people’s growing needs for a better life.

As for the evaluation system of logistics high-quality development, some studies have considered the internal and external environment of the logistics industry for evaluation. For example, the index system established by Mu et al. [ 54 ] includes the economic environment of the logistics industry, the scale level of it, the input level of it, the output effect of it, etc.; The index system established by Cheng et al. [ 55 ] includes economic development level, logistics demand, logistics industry scale, informatization level and infrastructure construction; Li [ 56 ] proposed that the development quality of logistics industry can be measured from three aspects: development efficiency, development structure and development environment; Li et al. [ 57 ] established evaluation indicators including low-carbon logistics environment, low-carbon logistics strength, low-carbon logistics potential and low-carbon logistics level from a low-carbon perspective. It is more evaluated from the perspective of input-output. For example, Cao et al. [ 58 ] proposed that relevant indicators include input (capital input of logistics industry, labor input of logistics industry) and output (scale of logistics industry, quality of logistics industry); The index system established by Lu [ 59 ] includes input (labor, capital), output (added value, goods turnover); In the index system established by Li [ 60 ], inputs include capital input of logistics industry (fixed asset investment of logistics industry), labor input (the number of logistics industry employees), energy input (energy consumption of logistics industry), and outputs include expected output (output value of logistics industry) and unexpected output (CO 2 emission of logistics industry).

Generally, the impact of the logistics industry on the manufacturing industry and the two-industry-linkage only takes the efficiency of the logistics industry as the inspection index. However, the cooperation between logistics and manufacturing industry and even the two-industry-linkage are actually the integration and optimization process of logistics system and manufacturing system, the whole integration and optimization activities need to be considered. The integration field theory proposed by Professor Dong [ 61 ] regards all manual integration systems as integration fields, it holds that the integrated optimization is the general law of the artificial system, the integration field is the elaboration of the integrated optimization activities and general laws of the artificial integrated system, and it is the spatiotemporal distribution state of the synthetic field element under the action of the integrated force and integrated gravity in the field. The main basic units which is worth investigating separately in the integration field include integrator, base-nuclear, connection-key, field-line, field-boundary, etc., of which the first three basic units constitute the basic structure of the network chain [ 33 ]. The integrator is an active optimization adaptive integration subsystem, which dominates the integrated optimization process and has the nature of strategic subject, behavior subject and interest subject; The base-nuclear is the base carrying the field source, agglomeration and radiation field lines, and is an important node related to the integrated transfer connection and value gain; The connection-key constructs a stable structure of interaction, cooperation and coordination, and is the contact channel for the integrator and base-nuclear to gather and integrate resources; Field line is the track and performance of the composite action of multiple synthetic field elements dominated by the integrator. When using the integration field theory to analyze the development process of the two-industry-linkage, the integrator, base-nuclear (field source), connection-key and field line can be taken as the basic units. The integrator can be divided into manufacturing integrator and logistics integrator, they are usually the leading enterprises in the supply chain and logistics chain, they usually master the base-nuclear (field source), dominate the formation of supply chain or logistics chain, have strong integrated attraction and form joint forces, and affect or determine the field line performance; Base-nuclear is the carrier of field source, which can be divided into manufacturing base-nuclear and logistics base-nuclear, which mainly plays the role of attracting manufacturing integrator and logistics integrator; The connection-key can connect two or more synthetic field elements into a synthetic field, which usually include information type, resource type, function type, technology type, etc. it is the connection relationship between various business entities; In form, the field line is the integrated service track between various business entities, which can be reflected in business performance [ 4 ]. Therefore, we define the basic unit of the logistics system as the logistics integrator, logistics base-nuclear and logistics connection-key, the representative indicators defined from the macro perspective are the number of logistics industry employees, the freight turnover and the density of the grade highway network, which were used to investigate the impact of the logistics industry on the comprehensive technical efficiency of manufacturing industry, the change rate of green total factor productivity (MI) in manufacturing industry and even the two-industry-linkage. The logic framework diagram of the full text design from the perspective of integration field as shown in Fig 1 .

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0264585.g001

Methodology

Research method, super-sbm model..

Malmquist index.

Coupling coordination model.

https://doi.org/10.1371/journal.pone.0264585.t001

PVAR model.

According to the theory of integration field, the basic structure of logistics network chain is composed of logistics base-nuclear, logistics integrator and logistics connection-key. The logistics base-nuclear is the carrier of the field source, which mainly plays the role of attracting the integrator, and the freight turnover represents the operation capacity of the base-nuclear, so the freight turnover was used to represent its capacity; Logistics integrator is usually the leading logistics enterprise in the supply chain and logistics chain, which dominates the formation of supply chain or logistics chain, therefore, the number of logistics industry employees was used to represent the scale of logistics integrator. There are many types of logistics connection-keys, and a very important role of it is to connect various business entities in the logistics chain and supply chain, therefore, the level of logistics connection-keys can be represented by the density of grade highway network. Relevant indicators of PVAR model are shown in Table 2 .

https://doi.org/10.1371/journal.pone.0264585.t002

Data source and description

Because the data of Hong Kong, Macao, Taiwan and Tibet are partially missing, this paper selected the data of the other 30 provinces, autonomous regions, municipalities and cities in China as the research sample, with a time span of 2001–2019. The data are mainly from China Statistical Yearbook, China Energy Statistical Yearbook and China Industrial Statistical Yearbook. The original data of carbon emission mainly comes from the carbon emission inventories from 2001 to 2018 provided by CEADs database, which is the most authoritative statistical data for accounting China’s carbon emission level at present. In particular, due to the lack of manufacturing data, but the actual manufacturing accounts for more than 70% of the industry, replace manufacturing data with industrial data. If some original data are missing, they shall be supplemented by interpolation method, and finally 13 indicators were selected. The description and statistics of core variables are shown in Table 3 .

https://doi.org/10.1371/journal.pone.0264585.t003

Firstly, MAXDEA ultra 7.10 software was used to obtain the comprehensive technical efficiency of logistics industry and manufacturing industry, and the change rate of green total factor productivity (MI) of them, then SPSSAU was used to analyze the coupling coordination degree of the two industries, and then stata16.0 was used to analyze the interaction between the three elements of logistics industry and the comprehensive technical efficiency of manufacturing industry (gyzhxl), and the interaction with the green total factor productivity change rate of manufacturing industry (gymi), finally, the impact of the three elements of logistics on the two-industry-linkage was analyzed.

Interaction between logistics industry and the comprehensive technical efficiency of manufacturing industry from the perspective of integration field

Unit root test of variables..

In order to avoid the violent fluctuation of data and eliminate the possible heteroscedasticity, the relevant time series data were logarithmicized. The processed data are the logarithm of the comprehensive technical efficiency of manufacturing industry(lngyzhxl), the logarithm of freight turnover (lnzzl), the logarithm of the number of logistics industry employees (lnwlry) and the logarithm of the density of grade highway network (lndjglmd). The unit root stationarity test was carried out for the variable sequence, and HT and IPS were selected as the test standards [ 65 ], the test results using stata16.0 software are shown in Table 4 .

https://doi.org/10.1371/journal.pone.0264585.t004

The test results show that some sequences are non-stationary at the significance level of 10%. Then, the first-order difference sequence was tested and the results shown in Table 5 were obtained.

https://doi.org/10.1371/journal.pone.0264585.t005

The test results in Table 5 show that under the significance level of 1%, the first-order difference sequences are stable, i.e. I(1). Cointegration theory [ 66 ] shows that although variables have their own long-term fluctuation laws, if they are cointegrated of order (d, d), there is a long-term stable proportional relationship between them. Some variables in the original data series are unstable, according to the above variable unit root test, all variables (lngyzlxl, lnzzl, lnwlry, lndjglmd) belong to first-order cointegration. According to cointegration theory, the comprehensive technical efficiency of manufacturing industry (lngyzhxl) and logistics variables (lnzzl, lnwlry, lndjglmd) constitute a long-term equilibrium relationship, Kao test, Pedroni test and Westerlund test were used to test the cointegration relationship between variables to investigate whether there is a long-term equilibrium cointegration relationship between variables. The test results are shown in Table 6 , most of the tests reject the original test without cointegration relationship, that is, it can be judged that there is cointegration relationship between variables.

https://doi.org/10.1371/journal.pone.0264585.t006

Determine the optimal lag order.

The PVAR model for logistics industry and the comprehensive technical efficiency of manufacturing industry was established, first determine the lag order, select it by using Lian Yujun’s stata16.0 software package pvar2, and determine the lag period according to the minimum criteria such as AIC and SC, the results are shown in Table 7 .

https://doi.org/10.1371/journal.pone.0264585.t007

It can be seen from Table 7 that the lag period of the PVAR model for the interaction between the comprehensive technical efficiency of logistics industry and manufacturing industry is 5, the PVAR(5) model was established by stata16.0 software, and the estimation results are shown in Table 8 . However, some scholars believed that PVAR model is a pan theoretical model, and the positive and negative, magnitude and significance of its parameter estimates lack practical economic significance [ 67 ], it cannot describe the long-term impact mechanism, evolution path and impact degree of one variable on other variables [ 68 ], therefore, this paper only gives the estimation results ( Table 8 ), and the analysis focuses on the subsequent impulse response and variance decomposition.

https://doi.org/10.1371/journal.pone.0264585.t008

In the results shown in Table 8 , when the explanatory variable is the comprehensive technical efficiency of the manufacturing industry, its own lag first-order and fifth-order has insignificant negative effects, lag second-order and fourth-order have significant positive effects, and lag third-order has insignificant positive effects, which is manifested as inhibition–significant promotion–promotion–significant promotion–inhibition; The first, second, third and fifth order lag of freight turnover has insignificant negative effect, and the fourth-order lag has insignificant positive effect, which is manifested as inhibition–inhibition–inhibition–promotion–inhibition; The first-order and fifth-order lag of the number of logistics industry employees has insignificant negative effect, the second-order and third-order lag have insignificant positive effect, and the fourth-order lag has significant negative effect, which is manifested as inhibition–promotion–promotion–significant inhibition–inhibition; The first, second and fifth order lag of the density of the grade highway network has insignificant negative effect, the third-order lag has insignificant positive effect, and the fourth-order lag has significant negative effect, which is manifested as inhibition–inhibition–promotion–significant inhibition–inhibition. When the explained variable is freight turnover, its first-order to fifth-order lag is inhibition–promotion–promotion–promotion–significant promotion; The comprehensive technical efficiency of manufacturing industry is represented by promotion–promotion–significant promotion–promotion–inhibition; The number of logistics industry employees shows inhibition–significant inhibition–inhibition–promotion–promotion; The density of the grade highway network shows significant promotion–significant promotion–inhibition–significant promotion–significant promotion.

When the explained variable is the number of logistics industry employees, the first-order to fifth-order lag is inhibition–promotion–promotion–significant promotion–promotion; The comprehensive technical efficiency of manufacturing industry shows promotion–inhibition–significant inhibition–promotion–promotion; The freight turnover shows significant promotion–promotion–significant promotion–promotion–significant promotion; The density of the grade highway network shows promotion–significant inhibition–significant inhibition–inhibition–inhibition. When the explained variable is the density of the grade highway network, its own lag from first-order to fifth-order is significant promotion–significant promotion–significant inhibition–inhibition–inhibition; The comprehensive technical efficiency of manufacturing industry shows significant promotion–significant promotion–significant promotion–significant promotion–inhibition; The freight turnover shows promotion–significant promotion–significant promotion–significant promotion–significant promotion; The number of logistics industry employees shows inhibition–promotion–inhibition–significant inhibition–inhibition.

Stability of the model and Granger causality test.

The optimal lag order was set as 5 to further verify the model’s stability of logistics industry and the comprehensive technical efficiency of manufacturing industry and the Granger causality between variables, all characteristic roots (including real roots and virtual roots) are less than 1. Therefore, the PVAR model is stable and passes the stability test. Then, Granger causality test was carried out for each variable with a lag of 5 periods [ 69 ], and the results shown in Table 9 were obtained.

https://doi.org/10.1371/journal.pone.0264585.t009

It can be seen from Table 9 that the freight turnover, the number of logistics industry employees and the density of the grade highway network are not Granger reasons for the comprehensive technical efficiency of manufacturing industry, and the comprehensive technical efficiency of manufacturing industry is not Granger reasons for the freight turnover. The number of logistics industry employees and the density of the grade highway network are Granger reasons for the freight turnover. The comprehensive technical efficiency of manufacturing industry, the freight turnover, and the density of the grade highway network are Granger reasons of the number of logistics industry employees. The comprehensive technical efficiency of manufacturing industry, freight turnover and the number of logistics industry employees are the Granger reasons of the density of the grade highway network. It shows that the current scale of major logistics integrator, the level of logistics connection-key and the capacity of logistics base-nuclear have no obvious impact on the comprehensive technical efficiency of manufacturing industry, or their early changes cannot effectively explain the changes in the comprehensive technical efficiency of manufacturing industry (the main reason is that Granger causality test focuses on whether one variable has the ability to predict another variable, the availability of relevant data may weaken the test results, and the development of logistics is relatively slow, resulting in the absence of Granger causality), and the comprehensive technical efficiency of manufacturing industry mainly affects the scale of logistics integrator and the level of logistics connection-key. Within the logistics industry, the interaction effect of the capacity of logistics base-nuclear, the scale of logistics integrator and the level of logistics connection-key are obvious.

PVAR impulse response analysis.

In order to fully describe the long-term dynamic impact effect between the logistics industry and the comprehensive technical efficiency of manufacturing industry, the impulse response function was further analyzed, and the 95% confidence interval impulse response diagram of other endogenous variables was calculated 200 times by Monte Carlo random simulation, as shown in Fig 2 . The horizontal axis represents the number of response periods, the vertical axis represents the magnitude of impulse response value, the upper and lower lines represent the confidence interval of 5% ~ 95%, and the middle line represents the estimation curve of impulse response function.

https://doi.org/10.1371/journal.pone.0264585.g002

For a standard deviation impact from the comprehensive technical efficiency of manufacturing industry itself, it immediately produces a strong positive response, reaches the peak, then decreases rapidly, becomes negative in the first lag period and begins to converge to 0, indicating that the comprehensive technical efficiency of manufacturing industry is highly dependent on itself, but this inertia gradually weakens. For a standard deviation impact from the freight turnover, the comprehensive technical efficiency of manufacturing industry shows a negative response at the beginning, becomes positive in the first lag period, and then reaches the peak in the fourth lag period, after that, the lag begins to weaken and converge to 0. This also shows that in the process of increasing the freight turnover, the increase of some invalid transportation distance and transportation times is not conducive to the high-quality development of industry and even economy, considering the capacity of logistics base-nuclear, blindly exceeding the demand of the capacity of logistics base-nuclear will cause a waste of resources, but not conducive to the high-quality development of industry, it is also not conducive to high-quality economic development. The matching between the development of the capability of logistics base-nuclear and the high-quality development of industry needs to go through a certain stage of practice and running in, when there is an error matching between the two industries, the capability of logistics base-nuclear will inhibit the high-quality development of industry. For a standard deviation impact from the number of logistics industry employees, the comprehensive technical efficiency of manufacturing industry has a positive response at the beginning, and then gradually decreases, it reaches a negative bottom in the third lag period, and is positive in the fourth lag period, and then converges to 0 in the fluctuation. This shows that the increase of the number of logistics industry employees has a positive impact on the comprehensive technical efficiency of manufacturing industry in the short term, but excessive increase will cause a waste of human resources, the emergence or expansion of the scale of logistics integrator is more to meet the needs of the market, it takes a period of market running in to show a positive promotion of high-quality industrial development. The impact of a standard deviation impact from the density of the grade highway network on the comprehensive technical efficiency of manufacturing industry is reverse at first, then increases, reaches the peak in the fourth lag period, and then gradually weakens and converges to 0. In the early stage, the increase of the density of the grade highway network and even the improvement of the level of logistics connection-key have driven the respective development of the two industries, and even the high-quality development of the industry, with the gradual improvement of the high-quality development of the two industries in the later stage, the optimization of the linkage system of the two industries has been gradually realized.

For the freight turnover, a standard deviation impact from the comprehensive technical efficiency of manufacturing industry fluctuates slightly below 0 and converges to 0 in the lag period 1 ~ 5, which also confirms that the improvement of the comprehensive technical efficiency of manufacturing industry may require more efficient logistics turnover. The impact of freight turnover on its own standard deviation reached a positive peak at the beginning, and then weakened rapidly and converged to 0. This also shows that the rapid expansion of the capacity of logistics base-nuclear depends on itself, but the long-term development also depends on the influence of the outside world. A standard deviation impact of the number of logistics industry employees on the freight turnover was negative at first, then increased to positive in the second lag period, reached the peak in the fifth lag period, and the lag gradually converged to 0, the overall performance is positive, which also shows the important role of the number of logistics industry employees in completing the freight turnover, at the same time, it further explains the further demand impact of the expansion of the scale of logistics integrator on the logistics base-nuclear capability. A standard deviation impact of the density of the highway network on the freight turnover is positive at the beginning, and maintains a relatively high positive value, it reaches the peak in the fourth lag period, and then gradually becomes negative and converges to 0, indicating that when the density of the highway network increases, the freight turnover is more convenient. The impact of logistics connection-key on the capability of logistics base-nuclear is also positive in an appropriate range, but the continuous development of logistics connection-key is not necessarily conducive to the expansion of the capability of logistics base-nuclear, such as the increase of the density of the grade highway network may lead to more fierce competition between logistics base-nuclear.

For the number of logistics industry employees, the impact of a standard deviation impact from the comprehensive technical efficiency of manufacturing industry mainly fluctuates up and down at 0, reaching the peak in the third lag period, reaching the trough in the fourth lag period, and then rising and converging to 0. The increase of the number of logistics industry employees is reflected in the increase of investment in the high-quality development of the logistics industry, thus reducing the efficiency of high-quality logistics, the impact of the increase of the scale of logistics integrator on the comprehensive technical efficiency of manufacturing industry is positive in a certain range, but beyond the normal range, it may have negative effects. A standard deviation impact of freight turnover on the number of logistics industry employees was negative at first, reached the trough in the second period, and then gradually converged to 0. The increase of freight turnover may lead to the demand for mechanization, thus reducing the demand for the number of logistics industry employees, and the increase of the capacity of logistics base-nuclear requires a corresponding logistics integrator. The impact of a standard deviation of the number of logistics industry employees on themselves reached the peak at the beginning, and then rapidly decreased and converged to 0, which also shows that the number of logistics industry employees are more dependent on themselves, and the development of logistics integrator is more dependent on their own ability and even resources. A standard deviation impact of the density of the grade highway network on the number of logistics industry employees was negative at first, then basically fluctuated below 0, reached the trough after lagging behind phase 5, and gradually converged to 0. There is no doubt that the increase of the density of the grade highway network is conducive to improving the efficiency of logistics industry, but efficient logistics industry means the improvement of mechanization level and the reduction of labor demand rate. The development of logistics connection-key will also be conducive to the development of logistics integrator, such as expanding business scope and improving service level.

For the density of the grade highway network, the impact of a standard deviation impact from the comprehensive technical efficiency of manufacturing industry mainly fluctuates below 0, which shows that the positive impact of the comprehensive technical efficiency of manufacturing industry on the density of the grade highway network is not obvious. A standard deviation impact of freight turnover on the density of the grade highway network is positive, and reaches the peak in the second lag period, and then converges to 0 in the fluctuation, the increase of freight turnover is a main reason for the increase of the density of the grade highway network. This also shows that the increase of the capacity of logistics base-nuclear is a main reason for the further development of logistics connection-key. A standard deviation impact of the number of logistics industry employees on the density of the grade highway network is not obvious at the beginning, it reaches the trough in the second lag period, then gradually rises, reaches the peak in the seventh lag period, and then slowly decreases and converges to 0. The increase of the number of logistics industry employees means the increase of the scale of logistics integrator, thus, the increase of the density of the grade highway network is accompanied by the improvement of the level of logistics connection-key. The impact of the density of the grade highway network on its own standard deviation is positive at the beginning, and then rapidly converges to 0 in the first lag period, which also shows that the density of the grade highway network needs to rely more on the development of external factors.

Variance decomposition.

The variance decomposition of PVAR is a further supplement to impulse response analysis, it mainly describes the firmness of each structural impact on the variables in the system, and can specifically analyze the contribution of each variable to more accurately measure the degree of interaction between variables. The variance decomposition results of the interaction between the three elements of logistics industry and the comprehensive technical efficiency of manufacturing industry are shown in Table 10 .

https://doi.org/10.1371/journal.pone.0264585.t010

The results in Table 10 show that the variance contribution rate of the comprehensive technical efficiency of manufacturing industry to itself tends to be stable from 100% lagging behind in phase 1 to 97.7% in phase 15, indicating that the comprehensive technical efficiency of manufacturing industry is its biggest contribution factor. The contribution of freight turnover, the number of logistics industry employees and the density of the grade highway network to the comprehensive technical efficiency of manufacturing industry was relatively small at the beginning and then increased slightly, the freight turnover increased to 1.1% in the 15th lag period, the number of logistics industry employees increased to 0.6% in the 5th lag period, and the density of the grade highway network increased to 0.6% in the 10th lag period. It can be seen from the perspective of logistics industry, from large to small, the contribution to the comprehensive technical efficiency of manufacturing industry is the capacity of logistics base-nuclear, the scale of logistics integrator and the level of logistics connection-key, but the role is relatively small.

The variance contribution rate of freight turnover to itself tends to be stable from 95.2% in phase 1 to 88.2% in phase 10, indicating that freight turnover is its biggest contribution factor. However, the contribution of the comprehensive technical efficiency of manufacturing industry, the number of logistics industry employees and the density of the grade highway network to the development of freight turnover is relatively small, but their contributions are gradually increasing. The contribution of the comprehensive technical efficiency of manufacturing industry reached 7% in the 15th lag period, and the contribution of the number of logistics industry employees and the density of the grade highway network increased to 2.1% and 2.7% respectively in the 10th lag period. It can be seen that the contribution to the development of the capability of logistics base-nuclear is the comprehensive technical efficiency of manufacturing industry, the level of logistics connection-key and the scale of logistics integrator.

The variance contribution rate of the number of logistics industry employees to themselves began to stabilize from 92% in the first lag period to 73.1% in the 15th lag period, indicating that the number of logistics industry employees are their biggest contribution factor. The comprehensive technical efficiency of manufacturing industry, freight turnover and the density of the grade highway network have made relatively small contributions to the number of logistics industry employees, but their contributions are gradually increasing. The manufacturing industry’s comprehensive technical efficiency, freight turnover and the density of the grade highway network have increased to 9%, 16.1% and 1% respectively in the 15th period. From large to small, the contributions to the scale of logistics integrator are the capacity of logistics base-nuclear, the comprehensive technical efficiency of manufacturing industry and the level of logistics connection-key.

The contribution rate of the density of the grade highway network to its own variance tends to be stable from 99.3% in the first stage to 86.2% in the 20th stage, indicating that the density of the grade highway network is its biggest contribution factor. However, the contribution of the comprehensive technical efficiency of manufacturing industry, freight turnover and the number of logistics industry employees to the density of the grade highway network is relatively small, but their contributions are gradually increasing. The comprehensive technical efficiency of manufacturing industry and the number of logistics industry employees increased to 6.7% and 2.9% respectively in the 10th lag period, and the freight turnover increased to 4.2% in the 20th lag period. From the perspective of logistics industry, the contribution to the development of the level of logistics connection-key from large to small is the comprehensive technical efficiency of manufacturing industry, the capacity of logistics base-nuclear and the scale of logistics integrator.

In the long run, the impact of the comprehensive technical efficiency of manufacturing industry on other factors from large to small is the number of logistics industry employees, freight turnover, the density of the grade highway network, that is, the scale of logistics integrator, the capacity of logistics base-nuclear and the level of logistics connection-key. The influence of freight turnover on other factors from large to small is the number of logistics industry employees, the density of the grade highway network and the comprehensive technical efficiency of manufacturing industry, that is, the influence of the capacity of logistics base-nuclear on other factors from large to small is the scale of logistics integrator, the level of logistics connection-key and high-quality development level of manufacturing industry. The influence of the number of logistics industry employees on other factors from large to small is the density of the grade highway network, the freight turnover and the comprehensive technical efficiency of manufacturing industry, that is, the influence of the scale of logistics integrator on other factors from large to small is the level of logistics connection-key, the capacity of logistics base-nuclear and the high-quality development level of manufacturing industry. The influence of the density of the grade highway network on other factors from large to small is freight turnover, the number of logistics industry employees and the comprehensive technical efficiency of manufacturing industry, that is, the influence of the level of logistics connection-key on other factors from large to small is the capacity of logistics base-nuclear, the scale of logistics integrator and high-quality development level of manufacturing industry.

Interaction between logistics industry and the change rate of green total factor productivity in manufacturing industry from the perspective of integration field

In order to avoid the violent fluctuation of data and eliminate the possible heteroscedasticity, the relevant time series data were logarithmicized. The processed data are the logarithm of the change rate of manufacturing green total factor productivity (manufacturing MI) (lngymi), the logarithm of freight turnover (lnzzl), the logarithm of the number of logistics industry employees (lnwlry) and the logarithm of the density of the grade highway network (lndjglmd). The unit root stationarity test was conducted for the variable series, according to the statistical characteristics of the data, HT and IPS were selected as the test standards [ 65 ]. The test results by using stata16 software are shown in Table 11 .

https://doi.org/10.1371/journal.pone.0264585.t011

The test results show that some sequences are non-stationary at the significance level of 10%. Then, the first-order difference sequence was tested and the results shown in Table 12 were obtained.

https://doi.org/10.1371/journal.pone.0264585.t012

The test results show that at the significance level of 1%, the first-order difference sequences are stable, i.e. I(1). Similarly, according to the cointegration theory, manufacturing MI (lngymi) and logistics development variables (lnzzl, lnwlry, lndjglmd) form a long-term equilibrium relationship. The results shown in Table 13 were obtained through the cointegration test. The tests reject the original test without cointegration relationship, that is, it can be judged that there is a cointegration relationship between variables.

https://doi.org/10.1371/journal.pone.0264585.t013

The PVAR model for logistics industry and manufacturing MI was established, first determine the lag order, select it by using Lian Yujun’s stata16.0 software package pvar2, and determine the lag period according to the minimum criteria such as AIC and SC, the results are shown in Table 14 .

https://doi.org/10.1371/journal.pone.0264585.t014

It can be seen from Table 14 that the lag period of PVAR model for logistics industry and manufacturing MI is 5. PVAR(5) model for the two-industry-linkage was established by using Stata software, and the estimation results are shown in Table 15 .

https://doi.org/10.1371/journal.pone.0264585.t015

In the results shown in Table 15 , when the explanatory variable is manufacturing MI, its own lag first-order to fifth-order have significant negative effects; The first, third and fifth-order lag of freight turnover has insignificant negative effect, and the second and fourth-order lag has significant negative effect; The number of logistics industry employees lag first-order, second-order has insignificant positive effect, lag third-order, fifth-order has significant positive effect, lag fourth-order has insignificant negative effect; The first, third and fifth-order lag of the density of the grade highway network has insignificant positive effect, the second-order lag has insignificant negative effect, and the fourth-order lag has significant positive effect, which is manifested as promotion–inhibition–promotion–significant promotion–promotion. When the explained variable is freight turnover, its own lag from first-order to fifth-order, which is manifested as inhibition–promotion–inhibition–inhibition–significant promotion; Manufacturing MI is characterized by inhibition–significant inhibition–inhibition–promotion–promotion; The number of logistics industry employees shows inhibition–significant inhibition–inhibition–promotion–promotion; The density of the grade highway network shows significant promotion–significant promotion–inhibition–promotion–significant promotion. When the explained variable is the number of logistics industry employees, the first to fifth order of its own lag is promotion–promotion–promotion–promotion–inhibition; Manufacturing MI shows promotion–significant promotion–promotion–promotion—inhibition; The freight turnover shows significant promotion–promotion–significant promotion–promotion—significant promotion; The density of the grade highway network shows significant inhibition–inhibition–significant inhibition–promotion–inhibition.

When the explained variable is the density of the grade highway network, its own lag from first order to fifth order is significant promotion–promotion–inhibition–significant inhibition–promotion; Manufacturing MI shows inhibition–inhibition–significant promotion–promotion–significant promotion; The freight turnover shows promotion–significant promotion–promotion—significant promotion–promotion; The number of logistics industry employees show inhibition–inhibition–promotion–significant inhibition–inhibition.

Set the optimal lag order as 5 to further verify the model’s stability of logistics industry and the manufacturing MI, and the Granger causality between variables, all characteristic roots (including real roots and virtual roots) are less than 1, therefore, the PVAR model is stable and passes the stability test. Then, Granger causality test with a lag of 5 periods was conducted for each variable [ 69 ], and the results shown in Table 16 were obtained.

https://doi.org/10.1371/journal.pone.0264585.t016

According to Table 16 , the freight turnover, the number of logistics industry employees and the density of the grade highway network are not Granger reasons for manufacturing MI, manufacturing MI is not the Granger reason for the freight turnover, the number of logistics industry employees and the density of the grade highway network are Granger reasons for the freight turnover, manufacturing MI, freight turnover and the density of the grade highway network are all Granger reasons for the number of logistics industry employees, manufacturing MI and freight turnover are Granger reasons for the density of the grade highway network, the number of logistics industry employees is not the Granger reason for the density of the grade highway network. It shows that the impact of the current logistics industry on the change rate of green total factor productivity of manufacturing industry is not obvious, or its early changes cannot effectively explain the change of green total factor productivity of manufacturing industry, while the change of green total factor productivity of manufacturing industry mainly affects the scale of logistics integrator and the level of logistics connection-key.

In order to fully describe the long-term dynamic impact effect between logistics industry and the change rate of green total factor productivity in manufacturing industry, the impulse response function is further analyzed, and the 95% confidence interval impulse response diagram of other endogenous variables is calculated 200 times by Monte Carlo random simulation, as shown in Fig 3 . The horizontal axis represents the number of response periods, the vertical axis represents the magnitude of impulse response value, the upper and lower lines represent the confidence interval of 5% ~ 95%, and the middle line represents the estimation curve of impulse response function.

https://doi.org/10.1371/journal.pone.0264585.g003

For a standard deviation shock from the manufacturing MI itself, it immediately produces a strong positive response to reach the peak, then quickly reduces and converges to 0 in the fluctuation, indicating that the manufacturing MI is highly dependent on itself. A standard deviation impact of freight turnover on manufacturing MI shows a positive response at the beginning, becomes negative in the first lag period, then reaches the peak in the second lag period, and then converges to 0 in the fluctuation. The impact of a standard deviation impact from the number of logistics industry employees on manufacturing MI makes it produce a peak negative response when it lags behind phase 1, and then converge to 0 in the fluctuation after it reaches the peak in lag phase 2. At first, a standard deviation impact from the density of the grade highway network on manufacturing MI was a weak negative effect, then rose to the peak in lag phase 4, then began to decline and converge to 0 in the fluctuation.

For freight turnover, a standard deviation impact from manufacturing MI starts to be negative in lag phase 1, then rises to be positive in lag phase 5, and converges to 0 in fluctuation. For a standard deviation impact from the freight turnover itself, it reaches a positive peak at the beginning, and then weakens rapidly and converges to 0. For a standard deviation impact of the number of logistics industry employees on freight turnover, it was a negative response at first, then increased, and reached a positive peak in the fifth period, then decreased and gradually converged to 0. For a standard deviation impact from the density of the grade highway network, the freight turnover showed a relatively high positive response in the early stage, and then a weak negative response and converged to 0 after phase 5.

For the number of logistics industry employees, the impact of manufacturing MI gradually increases from 0 to positive, then it is negative in the fourth lag period, reaches the peak in the fifth lag period, and then converges to 0 in positive and negative fluctuations. The impact of a standard deviation shock from freight turnover starts from 0 to negative, reaches the trough in the second lag period, then rises, and converges to 0 in positive and negative fluctuations. For a standard deviation impact from the number of logistics industry employees themselves, the number of logistics industry employees first reach the positive peak, and then quickly decline to 0. For a standard deviation impact from the density of the grade highway network, the number of logistics industry employees initially reacted negatively, then rose in fluctuation and converged to 0 in the seventh lag period.

For the density of the grade highway network, a standard deviation impact from manufacturing MI reached a positive peak in the lag phase, then decreased to the trough in the lag phase 2, and then began to fluctuate upward and converge to 0. For a standard deviation impact from freight turnover, it reaches the peak in the first lag period, and then converges to 0 in the fluctuation. A standard deviation impact of the number of logistics industry employees reached the trough in the first lag period, then rose continuously in the fluctuation, reached the peak in the sixth lag period, and converged gently to 0. For a standard deviation impact from the density of the grade highway network itself, it reaches the positive peak at the beginning, and then converges to 0.

The variance decomposition results of the interaction between logistics and manufacturing MI are shown in Table 17 .

https://doi.org/10.1371/journal.pone.0264585.t017

The results in Table 17 show that the variance contribution rate of manufacturing MI to itself tends to be stable from 100% lagging behind in phase 1 to 96.8% in phase 15, indicating that manufacturing MI is its largest contribution factor. The contribution of freight turnover, the number of logistics industry employees and the density of the grade highway network to manufacturing MI is relatively small at the beginning, but their contributions are gradually increasing, increasing to 1.8%, 1.1% and 0.4% respectively in the 10th lag period. It can be seen that from the perspective of logistics industry, the contribution to the change rate of green total factor productivity of manufacturing industry is the capacity of logistics base-nuclear from large to small, the scale of logistics integrator and the level of logistics connection-key.

The variance contribution rate of freight turnover to itself tends to be stable from 99.9% in phase 1 to 89.9% in phase 10, indicating that freight turnover is its biggest contribution factor. The contribution of manufacturing MI and the density of the grade highway network to freight turnover increased to 2.5% and 4.8% respectively in the 10th lag period, and the impact of the number of logistics industry employees on it increased to 2.9% in the 15th lag period. It can be seen that from the perspective of logistics industry, the contribution to the development of the capacity of logistics base-nuclear is in the order of the level of logistics connection-key, the scale of logistics integrator, and the change rate of green total factor productivity in manufacturing industry.

The variance contribution rate of the number of logistics industry employees to themselves tends to be stable from 91.3% in phase 1 to 75.8% in phase 15, indicating that the number of logistics industry employees are their biggest contribution factor. The contribution of manufacturing MI to the number of logistics industry employees reached 5.3% in the 10th period, the contribution of freight turnover to it reached 16.9% in the 20th period, the density of the grade highway network reached 2% in the 15th period. It shows that from the perspective of logistics industry, the contribution to the scale of logistics integrator from large to small is the capacity of logistics base-nuclear, the change rate of green total factor productivity of manufacturing industry and the level of logistics connection-key.

The variance contribution rate of the density of the grade highway network to itself tends to be stable from 98.8% in phase 1 to 84.4% in phase 15, indicating that the density of the grade highway network is its largest contribution factor. In the manufacturing industry, the impact of freight turnover and the density of the grade highway network on it increased to 6.3% and 4.5% in the 15th lag period, and the number of logistics industry employees reached 4.9% in the 10th lag period. It can be seen that from the perspective of logistics industry, the contribution to the level of logistics connection-key from large to small is the change rate of green total factor productivity of manufacturing industry, the scale of logistics integrator and the capacity of logistics base-nuclear.

In the long run, the impact of the change rate of green total factor productivity of manufacturing industry on other factors from large to small is the density of the grade highway network, the number of logistics industry employees and the freight turnover, that is, the level of logistics connection-key, the scale of logistics integrator and the capacity of logistics base-nuclear.

Interaction between logistics industry and the efficiency of the two-industry-linkage from the perspective of integration field

The relevant time series data are logarithmically processed, the processed data are the logarithm of the efficiency of two-industry-linkage (lnwgd), the logarithm of the freight turnover (lnzzl), the logarithm of the number of logistics industry employees (lnwlry), and the logarithm of the density of the grade highway network (lndjglmd). The unit root stationarity test is conducted for the variable series. According to the statistical characteristics of the data, HT and IPS were selected as the test standards [ 65 ]. The test results by using stata16 software are shown in Table 18 .

https://doi.org/10.1371/journal.pone.0264585.t018

The test results show that some sequences are non-stationary at the significance level of 10%. Then, the first-order difference sequence was tested and the results shown in Table 19 were obtained.

https://doi.org/10.1371/journal.pone.0264585.t019

The test results show that at the significance level of 1%, the first-order difference sequences are stable, i.e. I(1), and there is a long-term stable proportional relationship between them. Some variables in the original data series are unstable. According to the unit root test of the above variables, all variables (lnwgd, lnzzl, lnwlry, lndjglmd) belong to first-order cointegration. According to the cointegration theory, the efficiency of the two-industry-linkage (lnwgd) and the development variables of the logistics industry (lnzzl, lnwlry, lndjglmd) constitute a long-term equilibrium relationship. The cointegration test is carried out to examine whether there is a long-term equilibrium cointegration relationship between the variables, the test results are shown in Table 20 , most of the tests reject the original test without cointegration relationship, that is, it can be judged that there is cointegration relationship between variables.

https://doi.org/10.1371/journal.pone.0264585.t020

The PVAR model for logistics industry and the efficiency of two-industry-linkage was established, first determine the lag order, select it by using Lian Yujun’s stata16.0 software package pvar2, and determine the lag period according to the minimum criteria such as AIC and SC, the results are shown in Table 21 .

https://doi.org/10.1371/journal.pone.0264585.t021

It can be seen from Table 21 that the lag period of PVAR model for logistics industry and the efficiency of two-industry-linkage is 5. The PVAR(5) model was established by using stata16.0 software, and the estimation results are shown in Table 22 .

https://doi.org/10.1371/journal.pone.0264585.t022

In the results shown in Table 22 , when the explained variable is the efficiency of two-industry-linkage, the first-order lag has a significant positive effect, the second-order lag, the third-order lag have an insignificant negative effect, the fourth-order and fifth-order lag have a significant negative effect, which is shown as significant promotion–inhibition–inhibition–significant inhibition–significant inhibition; The first and fifth order lag of freight turnover has insignificant negative effect, the second and fourth order lag have insignificant positive effect, and the third order lag has significant negative effect, which is manifested as inhibition–promotion–significant inhibition–promotion–inhibition; The first-order and fourth-order lag of the number of logistics industry employees has significant negative effect, the second-order lag has insignificant negative effect, the third-order lag and the fifth-order lag have insignificant positive effect, which is manifested as significant inhibition–inhibition–promotion–significant inhibition–promotion; The first-order and third-order lag of the density of the grade highway network has insignificant positive effect, the second-order lag and the fourth-order lag have significant negative effect, and the fifth-order lag has insignificant positive effect, which is shown as promotion–significant inhibition–promotion–significant inhibition–promotion. When the explained variable is freight turnover, its own lag from first order to fifth order is inhibition–promotion–promotion–promotion–significant promotion; The efficiency of two-industry-linkage shows inhibition–inhibition–significant promotion–inhibition–promotion; The number of logistics industry employees shows inhibition–significant inhibition–inhibition–promotion–inhibition; The density of the grade highway network shows significant promotion–significant promotion–inhibition–promotion–significant promotion. When the explained variable is the number of logistics industry employees, the first to fifth is inhibition–promotion–promotion–significant promotion–promotion; The efficiency of two-industry-linkage shows significant promotion–promotion–significant inhibition–inhibition–significant inhibition; The freight turnover is significant promotion–significant promotion–significant promotion–significant promotion–promotion; The density of the grade highway network shows promotion–significant inhibition–inhibition–inhibition–inhibition. When the explained variable is the density of the grade highway network, its own lag from first-order to fifth-order is significant promotion–significant promotion–promotion–inhibition–inhibition; The efficiency of two-industry-linkage shows inhibition–promotion–promotion–promotion–promotion; The freight turnover shows promotion–significant promotion–significant promotion–significant promotion–significant promotion; The number of logistics industry employees show inhibition–inhibition–inhibition–significant inhibition–inhibition.

Set the optimal lag order as 5 to further verify the model’s stability of logistics industry and the efficiency of the two-industry-linkage, and the Granger causality between variables, all characteristic roots (including real roots and virtual roots) are less than 1, therefore, the PVAR model is stable and passes the stability test. Then, Granger causality test with a lag of 5 periods was conducted for each variable [ 69 ], and the results shown in Table 23 were obtained.

https://doi.org/10.1371/journal.pone.0264585.t023

According to Table 23 , in the process of China’s overall economic development, the number of logistics industry employees and the density of the grade highway network are the Granger reasons for the efficiency of two-industry-linkage. The efficiency of two-industry-linkage, the number of logistics industry employees and the density of the grade highway network are the Granger reasons for the freight turnover. The efficiency of two-industry-linkage and the freight turnover are the Granger reasons for the number of logistics industry employees. The freight turnover, the number of logistics industry employees are Granger reasons for the density of the grade highway network. It shows that at present, the main impact on the efficiency of two-industry-linkage is the scale of logistics integrator and the level of logistics connection-key, the capability of logistics base-nuclear on it is not obvious, while the efficiency of two-industry-linkage mainly affects the capability of logistics base-nuclear and the scale of logistics integrator. Within the logistics industry, the interaction effect of the capability of logistics base-nuclear and the scale of logistics integrator on the level of logistics connection-key are obvious, the scale of logistics integrator and the level of logistics connection-key have an obvious impact on the capability of base-nuclear logistics, the capability of logistics base-nuclear has an obvious impact on the scale of logistics integrator.

In order to fully describe the long-term dynamic impact effect between logistics industry and the efficiency of two-industry-linkage, the impulse response function is further analyzed, and the 95% confidence interval impulse response diagram of other endogenous variables is calculated 200 times by Monte Carlo random simulation, as shown in Fig 4 . The horizontal axis represents the number of response periods, the vertical axis represents the magnitude of impulse response value, the upper and lower lines represent the confidence interval of 5% ~ 95%, and the middle line represents the estimation curve of impulse response function.

https://doi.org/10.1371/journal.pone.0264585.g004

For a standard deviation impact from the efficiency of two-industry-linkage itself, it immediately produces a strong positive response, reaches the peak, and then quickly reduces to 0, indicating that the coupling and coordinated development of the two industries is highly dependent on itself, that is, it needs the joint and coordinated development of high-quality manufacturing industry and high-quality logistics industry, but this inertia gradually weakens. For a standard deviation impact from the freight turnover, the efficiency of two-industry-linkage initially showed a negative response, became positive in the third lag period, and then reached the peak in the fifth lag period, and then most of them converged to 0. This shows that the increase of the freight turnover mainly depends on the strengthening of cooperation between the two industries, but in the process of the increase of the freight turnover, some invalid transportation distances and the increase of transportation and transshipment times are not conducive to the high-quality development of economy and even the high-quality development of logistics industry, which will also have some adverse effects on the coupling and coordinated development of the two industries. From the perspective of the capacity of logistics base-nuclear, when it is expanded, the capacity of logistics base-nuclear blindly exceeding the demand will cause a waste of resources, on the contrary, it is not conducive to the high-quality development of the logistics industry and the coupling and coordinated development of the two industries. The matching between the development of the capability of logistics base-nuclear and the efficiency of two-industry-linkage needs to go through a certain stage of practice and running in. When there is an error matching between the two industries, the capability of logistics base-nuclear will inhibit the coupling and coordinated development of the two industries. For a standard deviation impact from the number of logistics industry employees, the efficiency of two-industry-linkage initially shows a positive response, then reaches the peak in lag phase 1, then decreases rapidly, reaches the trough in lag phase 3, and then rises in fluctuation, after lag phase 7, it continues to show a weak positive effect and converges to 0, which shows that, the excessive increase of the number of logistics industry employees will cause a waste of market capacity. The emergence or expansion of logistics integrator is more to meet the needs of the market. It takes a period of market running in to show a positive promotion for the coupling and coordinated development of the two industries. For a standard deviation impact from the density of the grade highway network, the efficiency of two-industry-linkage shows a negative response at first, then rises to positive in the lag phase 1, reaches the peak in the lag phase 5, becomes 0 in the lag phase 6, and converges to 0. The increase of the density of the grade highway network and even the improvement of the level of logistics connection-key have driven the respective development of the two industries and even the linkage development of the two industries. With the gradual improvement of the high-quality development of the two industries in the later stage, the optimization of the linkage system of the two industries has been gradually realized.

For the freight turnover, a standard deviation impact from the efficiency of two-industry-linkage makes it fluctuate up and down in the lag period 1 ~ 10, which also confirms the above-mentioned that the coupling and coordinated development of the two industries, the generation of freight turnover and even the capacity of logistics base-nuclear need some practice and running in, and it is necessary to avoid error matching as much as possible. For a standard deviation impact from the freight turnover itself, it also reached a positive peak at the beginning, and then weakened rapidly and converged to 0. This also shows that the rapid expansion of the capacity of logistics base-nuclear depends on itself, but the long-term development also needs the influence of the outside world. For a standard deviation impact from the number of logistics industry employees, the freight turnover was negative at first, then positive when it lagged behind the first period, reached the peak in the fifth period, then decreased to 0 and converged to 0, the overall performance was positive, which also shows the important role played by the number of logistics industry employees in completing freight turnover. At the same time, it also further explains the impact of the expansion of the scale of logistics integrator on the further demand of the capability of logistics base-nuclear. For a standard deviation impact from the density of the grade highway network, the freight turnover initially showed a positive response, reached the peak in the fourth lag period, and then began to be negative and gradually converged to 0 after the fifth lag period. The reason is that when the density of the grade highway network increases, the freight turnover is more convenient, but it may also focus on surrounding businesses for a period of time, thus shortening the transportation distance. The impact of logistics connection-key on the capability of logistics base-nuclear is also positive in an appropriate range, blindly developing logistics connection-key is not necessarily conducive to the expansion of the capability of logistics base-nuclear. For example, the excessive increase of the density of the grade highway network will promote more incentive competition between logistics base-nuclear.

For the number of logistics industry employees, a standard deviation impact from the efficiency of two-industry-linkage is mostly negative, and it starts to be a weak positive response in the lag phase 5, and converges to 0. The increase of the number of logistics industry employees is reflected in the increase of investment in the high-quality development of the logistics industry, which reduces the high-quality efficiency of logistics and is not conducive to the development of the efficiency of two-industry-linkage, the coupling and coordinated development of the two industries depends more on low investment and high return. When the economy develops to a certain extent, the increase of the number and scale of logistics integrator is not necessarily beneficial to the coupling and coordinated development of the two industries. For a standard deviation impact from freight turnover, most number of logistics industry employees react negatively. For a standard deviation impact from the number of logistics industry employees themselves, their response reached the peak at the beginning, and then gradually began to converge to 0, which also shows that the number of logistics industry employees are more dependent on themselves, and the logistics integrator is more dependent on their own ability and even resources. A standard deviation impact from the density of the grade highway network on the number of logistics industry employees is mostly negative and fluctuates violently, it reaches the trough in the fourth lag period and gradually begins to converge upward to 0.

For the density of the grade highway network, a standard deviation impact from the efficiency of two-industry-linkage fluctuates greatly, it reaches the trough when it behind the third stage, and then fluctuates between positive and negative, which also proves that in the process of the linkage development of the two industries, the two need more wear and tear, and the increase of the density of the grade highway network, to some extent, it is conducive to the logistics integrator to improve its service level and better realize the linkage between the two industries. However, the excessive increase of the density of the grade highway network increases the investment in the logistics industry, which is not conducive to the high-quality development of the logistics industry, and then not conducive to the coupling and coordinated development of the two industries.

The development of the-two-industry linkage under the background of high-quality development poses a higher challenge to the development of logistics connection-key. For a standard deviation impact from the freight turnover, the response of the highway network density is positive, and reaches the peak in the second lag period, and then converges to 0 in the fluctuation. The increase of the freight turnover is a main reason for the increase of the highway network density. This also shows that the increase of the capacity of logistics base-nuclear is a main reason for the further development of logistics connection-key. For a standard deviation impact from the number of logistics industry employees, the response of the density of the grade highway network is initially a weak positive response, then a negative response, and begins to rise slowly. It reaches the peak in the seventh lag period, and then decreases in fluctuation and converges to 0. The improvement of the capacity of logistics integrator will also be accompanied by the improvement of the level of logistics connection-key. A standard deviation impact from the density of the grade highway network itself is positive at the beginning, and then converges to 0 rapidly, which also shows that the density of the grade highway network needs to rely more on the development of external factors.

The variance decomposition results of the interaction between logistics industry and the efficiency of two-industry-linkage are shown in Table 24 .

https://doi.org/10.1371/journal.pone.0264585.t024

The results in Table 24 show that the contribution rate of the efficiency of two-industry-linkage to their own variance tends to be stable from 100% in the first period to 92.8% in the 15th period, indicating that the efficiency of two-industry-linkage is its biggest contribution factor. The contribution of freight turnover, the number of logistics industry employees and the density of the grade highway network to the efficiency of the two-industry-linkage is relatively small at the beginning, but the contributions of the three are gradually increasing. The freight turnover increased to 2.6% in the 10th lag period, the number of logistics industry employees increased to 3.6% in the 5th lag period, and the density of the grade highway network increased to 1% in the 10th lag period. It can be seen from the perspective of the logistics industry, from large to small, the contributions to the coupling and coordinated development of the two industries are the scale of logistics integrator, the capacity of logistics base-nuclear and the level of logistics connection-key, and these contributions are increasing day by day.

The variance contribution rate of freight turnover to itself tends to be stable from 99.5% in the first period to 91.8% in the 15th period, indicating that freight turnover is its biggest contribution factor. From the perspective of logistics industry, the contribution of the efficiency of two-industry-linkage, the number of logistics industry employees and the density of the grade highway network to the development of freight turnover increased to 2.7%, 2.6% and 2.9% respectively in the 10th period. It can be seen that from the perspective of logistics industry, the contribution to the development of the capacity of logistics base-nuclear is the level of logistics connection-key, the efficiency of two-industry-linkage and the scale of logistics integrator.

The variance contribution rate of the number of logistics industry employees to themselves tends to be stable from 95.7% in the first period to 74% in the 25th period, indicating that the number of logistics industry employees are their biggest contribution factor. The contribution of the efficiency of two-industry-linkage to the number of logistics industry employees has increased to 7.3% in the 10th lag period, and the freight turnover and the density of the grade highway network have increased to 17.5% and 1.1% respectively in the 15th lag period. It can be seen that from the perspective of the logistics industry, the contribution to the scale of the logistics integrator from large to small is the capacity of logistics base-nuclear, the efficiency of two-industry-linkage and the level of logistics connection-key.

The variance contribution rate of the density of the grade highway network to itself tends to be stable from 99.3% in phase 1 to 87.7% in phase 20, indicating that the density of the grade highway network is its largest contribution factor. The contribution of the coupling and coordinated development of the two industries and the number of logistics industry employees personnel to the density of the grade highway network reached 1.6% and 4.7% respectively in the 15th lag period, and the freight turnover reached 6% in the 20th lag period, indicating that the contribution to the development of the level of logistics connection-key from large to small is the capacity of logistics base-nuclear, the scale of logistics integrator and the efficiency of the two-industry-linkage.

In the long run, the impact of the efficiency of two-industry-linkage on other factors, from large to small, is the number of logistics industry employees, freight turnover, the density of the grade highway network, that is, the scale of logistics integrator, the capacity of logistics base-nuclear and the level of logistics connection-key. The influence of freight turnover on other factors from large to small is the number of logistics industry employees, the density of the grade highway network and the efficiency of two-industry-linkage, that is, the influence of the capacity of logistics base-nuclear on it from large to small is the scale of logistics integrator, the level of logistics connection-key and the efficiency of two-industry-linkage. The influence of the number of logistics industry employees on other factors from large to small is the density of the grade highway network, the efficiency of two-industry-linkage and the freight turnover, that is, the influence of the scale of logistics integrator on it from large to small is the level of logistics connection-key, the efficiency of two-industry-linkage and the capacity of logistics base-nuclear. The influence of the density of the grade highway network on other factors from large to small is the freight turnover, the number of logistics industry employees and the efficiency of two-industry-linkage, that is, the influence of the level of logistics connection-key on it from large to small is the capacity of logistics base-nuclear, the scale of logistics integrator and the efficiency of two-industry-linkage.

At present, the impact of high-quality development of logistics industry on high-quality development of manufacturing industry is not obvious, manufacturing industry mainly affects the scale of logistics integrator and the level of logistics connection-key. The influence of the comprehensive technical efficiency of manufacturing on other factors from large to small is the scale of logistics integrator, the capacity of logistics base-nuclear and the level of logistics connection-key. The influence of the change of green total factor productivity of manufacturing industry on other factors from large to small is the level of logistics connection-key, the scale of logistics integrator and the capacity of logistics base-nuclear. The impact of the efficiency of two-industry-linkage on other factors from large to small is the scale of logistics integrator, the capacity of logistics base-nuclear and the level of logistics connection-key. Within the logistics industry, the interaction effects of the capability of logistics base-nuclear, the scale of logistics integrator and the level of logistics connection-key are obvious. The contribution of the high-quality development of logistics industry to the high-quality development of manufacturing industry from large to small are the capacity of logistics base-nuclear, the scale of logistics integrator and the level of logistics connection-key. From large to small, the contributions to the coupling and coordinated development of the two industries are the scale of logistics integrator, the capacity of logistics base-nuclear and the level of logistics connection-key, and these contributions are increasing day by day. The high-quality development and improvement of manufacturing industry needs more efficient logistics operation, the coupling and coordinated development of the two industries requires the joint and coordinated development of high-quality manufacturing industry and high-quality logistics industry, but this inertia gradually weakens. The development of logistics industry and manufacturing industry need to go through a certain stage of practice and running in, when there is an error matching between the two industries, the logistics industry will inhibit the coupling and coordinated development of the two industries. At present, in order to improve the linkage level of the two industries, the first choice is to improve the scale of logistics integrator, followed by the capacity of logistics base-nuclear and the level of logistics connection-key. However, it should also be noted that when the economy develops to a certain extent, the overall impact of the expansion of the scale of the logistics system on the coupling and coordinated development of the two industries is not necessarily beneficial. Blindly exceeding the demand for logistics investment will cause a waste of resources, which is not conducive to the high-quality development of the logistics industry and the coupling and coordinated development of the two industries.

The high-quality development of China’s logistics industry and manufacturing industry is mainly caused by the change of scale, but there is no obvious change in technical efficiency, which also provides a way for the high-quality development of the two-industry-linkage in the future. The high-quality development of China’s logistics industry and manufacturing industry is close on the whole, and the development trend is consistent. However, the level of the two-industry-linkage mostly belongs to the situation of low-level mutual restriction, which has not yet reached a high level of mutual promotion, resulting in the overall coupling coordination degree basically in a state of barely coordination. The main reason for this situation is that the development level of the comprehensive technical efficiency of the logistics industry and the manufacturing industry is inconsistent, and once the logistics industry improves its green total factor productivity, it may reduce some efficiency of the manufacturing industry and have some adverse effects on the benefit growth of the manufacturing industry. However, in the long run, the change of the logistics basic-nuclear capacity, the logistics integrator scale and logistics connection-key level will have a positive impact on the change of green total factor productivity in manufacturing industry. High-quality development is the inevitable trend of China’s economic development, and even the direction of the world’s future economic development. This long-term development needs the support of the corresponding policy environment, including the support for innovation, the restriction of green development, the promotion of coordinated and shared development, the improvement of the open development policy and the improvement of relevant systems, it should continue to deepen the reform of "decentralization, management and service", eliminate the institutional disadvantages restricting the market mechanism, implement the fair competition supervision mechanism and standardize the government behavior. High-quality development must attach importance to regional coordinated development, instead of simply requiring all regions to reach the same level of economic and social development, it should recognize the objective differences, formulate logistics development strategies according to local conditions through improving regional strategic planning and other mechanisms, so as to promote high-quality development and better promote the development of developed and underdeveloped regions, realize the common development of the Eastern, Central, Western and Northeast regions, and promote regional cooperation and mutual assistance and interregional interest compensation. At the same time, according to the development level of the two-industry-linkage in different regions, enterprises also need to choose the appropriate two-industry-linkage mode according to their own situation.

The coupling coordination model was used to theoretically analyze and empirically test the coupling and coordinated development level of high-quality development of logistics industry and manufacturing industry, based on the perspective of integration field theory, it takes the three basic synthetic fields of logistics integrator logistics base-nuclear and logistics connection-key as the analysis dimension, to introduce PVAR model into the two-industry-linkage for in-depth analysis and empirical test, it is novel and effective.

In addition, the manufacturing industry can be subdivided into different industries. In view of this, we will also try to analyze the impact of logistics industry on different manufacturing industries in the future, the coupling and coordinated development of logistics industry and different manufacturing industries, and on the basis of the existing model, we will further discuss the role of logistics integrator, logistics base-nuclear and logistics connection-key in different industries, conduct more empirical tests on the integration field theory.

• View Article
• Google Scholar
• 4. Dong QL. Interation field:. Development model and mechanism of the two-industry-linkage: China Social Sciences Press; 2018.
• 9. Stieglitz N. Digital Dynamics and Types of Industry Convergence: The Evolution of the Handheld Computers Market. Social Science Electronic Publishing. 2007.
• 12. Adriana F, editor Developing the integrated manufacturing systems trough modeling of manufacturing and logistic systems. 3rd International Conference on Management, Marketing and Finances; 2009 Apr 30-May 02; Univ Houston, Dowtown, Houston, TX2009.
• PubMed/NCBI
• 21. Bargelis A, Rimasauskas M, Mitruleviciute R, editors. Modeling of industrial logistics cost inside manufacturing company. 8th International Conference on Industrial Logistics (ICIL 2006); 2006 Jun 26–29; Kaunas Univ Technol, Fac Mech Engn & Mechatron, Kaunas, LITHUANIA2006.
• 28. Albadrani A, Alghayadh F, Zohdy MA, Aloufi E, Olawoyin R, editors. Performance and Predicting of Inbound Logistics Processes Using Machine Learning. IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC); 2021 Jan 27–30; Electr Network2021.
• 29. Yang YH. Study on the influence of multi-level logistics resources on the linkage efficiency of manufacturing and logistics industry [PhD]: Beijing Jiao Tong University; 2017.
• 37. Alonso L, Rubio EM, de Agustina B, Domingo R, editors. Latest clean manufacturing trends applied to a world class manufacturing management for improving logistics and environmental performance. 7th Manufacturing-Engineering-Society International Conference (MESIC); 2017 Jun 28–30; Vigo, SPAIN2017.
• 40. Li H, Mao J, Bai J, editors. Logistics management and implement technology in modern manufacturing. 2nd International Conference on Intelligent Information Management Systems and Technology; 2007 Oct 20–22; Ludong Univ China, Yantai, PEOPLES R CHINA2007.
• 45. Guo S. Study on the influence of FDI on the efficiency of manufacturing industry [PhD]: Northeastern Normal University; 2018.
• 56. Li J. Study on the impact of the development quality of logistics industry on the coordinated development of regional economy [PhD]: Shaanxi Normal University; 2019.
• 65. Li ZN, Pan WQ. Econometrics. 3rd Edition; 2010. 274.
• 66. Johansen S. Likelihood-Based Inference in Cointegrated Vector Autoregressive Models: Oxford University Press; 1995.

Research on Optimization of Cold Chain Logistics Distribution Paths for Fresh Food

New citation alert added.

This alert has been successfully added and will be sent to:

You will be notified whenever a record that you have chosen has been cited.

To manage your alert preferences, click on the button below.

New Citation Alert!

Please log in to your account

Information & Contributors

Bibliometrics & citations, index terms.

General and reference

Cross-computing tools and techniques

Recommendations

Logistics path decision optimization method of fresh product export cold chain considering transportation risk.

The cold chain logistics route of fresh product export is characterized by large quantity and complexity, which is prone to cause transportation risks of different degrees in the process of fresh product export transportation and affects the decision-...

Analysis on Temperature Field of Refrigerated Car in Cold Chain Logistics

The paper analyzed the simulating ways about heat system of refrigerated car in cold chain logistics. Heat balance model was chose as the analyzed tool. An analytic system was set up for simulating the heat condition of refrigerated transportation. The ...

Research on Collaborative Optimization of Pharmaceutical Cold Chain Logistics Inventory and Distribution

With the continuous improvement of the residents' requirements for pharmaceutical consumption and the level of medical security, the development space of pharmaceutical logistics in China has been further expanded, but due to the high quality ...

Information

Published in.

Association for Computing Machinery

New York, NY, United States

Publication History

Permissions, check for updates.

• Research-article
• Refereed limited

Contributors

Other metrics, bibliometrics, article metrics.

• 0 Total Citations
• 0 Total Downloads
• Downloads (Last 12 months) 0
• Downloads (Last 6 weeks) 0

View Options

Login options.

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

View options.

View or Download as a PDF file.

View online with eReader .

HTML Format

View this article in HTML Format.

Share this Publication link

Copying failed.

Share on social media

Affiliations, export citations.

• Please download or close your previous search result export first before starting a new bulk export. Preview is not available. By clicking download, a status dialog will open to start the export process. The process may take a few minutes but once it finishes a file will be downloadable from your browser. You may continue to browse the DL while the export process is in progress. Download
• Download citation
• Copy citation

We are preparing your search results for download ...

We will inform you here when the file is ready.

Your file of search results citations is now ready.

Your search export query has expired. Please try again.

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

A bi-objective model for the multi-period inventory-based reverse logistics network: a case study from an automobile component distribution network.

1. Introduction

• To optimize the transportation system in the ISACO company.
• To cut down transportation costs.
• To increase customer satisfaction by increasing the supply of customer demands.
• To allow the customers to return unused parts (which are not used by customers due to seasonal variations or environmental changes and market fluctuations.
• To collect and dispose or recycle the stock parts.

2. Literature Review

2.1. a review of the literature on distribution systems in supply chain management, 2.2. a review of the literature on green logistics in supply chain management, 3. materials and methods.

• Very high transportation costs induced by long round-trip distances.
• High costs imposed on the company as a result of vehicle breakdown.
• Frequent troubles related to timely goods delivery (e.g., the cities located far from Tehran, the chances are high that the goods do not reach on time).
• To benefit from the full capacity of cars, it is required that the amount of the ordered goods reach a certain quantity and then the goods be delivered to the representatives, which leads to dissatisfaction among the representatives and losing the competitive market.
• The lack of order and prioritization in the current system.
• Not considering different scenarios in decision making.
• Not being able to return unused or low-use parts by the representatives.
• The lack of an integrated system for receiving scrap parts.
• Not able to implement strategic planning.
• Some of the expected merits of the new system are the following:
• Reducing the costs resulting from redundant transportation.
• Increasing the representatives’ satisfaction level due to goods’ timely delivery and increasing the power to supply the demanded goods and the possibility of returning low-use parts to the representative.
• Systematizing transportation system which curbs other nuisances.
• Increasing the flexibility of the system.
• Decreasing the risks such as the sensitive parts becoming faulty during long transportation or the possibility of vehicle breakdowns that impose losses on the company.
• Building regional warehouses and reducing the heavy costs of the central warehouse.
• Controlling the system better and the potential to constantly improve.

5. Discussion and Conclusions

• Employing a multi-period model along with the power of inventory management so that it leads to reduced costs and increased revenue.
• With respect to the variety of available products, the number of product groups should be increased and included in the proposed model.
• Reducing the time of ordering periods to better use the multi-period model, supplying faster and more up-to-date customer demands in the year, and removing the barriers of the inventory cost increase through modeling and making decisions at the tactical and operational level.
• Raising the number of customers and applying the proposed model to the actual number of customers. It is worth mentioning that in this model, they were integrated into the provincial centers to facilitate the modeling of customer demand.
• Constructing regional warehouses in the locations suggested by the model outputs considering the construction cost and setting up and storing the goods in these warehouses.
• Launching the central warehouse number 2 when its effectiveness gets approved in all the models to properly benefit from it.
• Regularly controlling the proposed performance evaluation indices considering the possibility of changing the supply or demand pattern and making suitable decisions accordingly.
• Investigating the demand pattern in various time periods and the possibility of presenting a supplementary model for the probability mode of demand.
• Investigating the profit from waste recycling.
• Investigating the benefits of the brand’s mental image in terms of compliance with environmental issues.
• Considering production issues in the supply chain and distribution system.
• Including the demand of the different classes of customers in the distribution system and locating facilities; accordingly, in other words, assessing the effect of marketing decisions on the strategic macro-decisions of facility location.
• Considering other location benchmarks.
• Determining the order supply deadline for all sorts of goods orders and programming to supply them within the deadline and its effect on facility location problems.
• Considering other objective functions like social aspects, employment rates, and environmental impacts according to the priorities of managers and decision-makers.

Author Contributions

Data availability statement, conflicts of interest.

• Kamalahmadi, M.; Parast, M.M. A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research. Int. J. Prod. Econ. 2016 , 171 , 116–133. [ Google Scholar ] [ CrossRef ]
• Wang, G.; Gunasekaran, A.; Ngai, E.W.T.; Papadopoulos, T. Big data analytics in logistics and supply chain management: Certain investigations for research and applications. Int. J. Prod. Econ. 2016 , 176 , 98–110. [ Google Scholar ] [ CrossRef ]
• Alshubiri, F. The Impact of Green Logistics-Based Activities on the Sustainable Monetary Expansion Indicators of Oman. J. Ind. Eng. Manag. 2017 , 10 , 389–405. [ Google Scholar ] [ CrossRef ]
• Sheikh Azadi, A.H.; Shamsi Nesary, V.; Kebriyaii, O.; Khalilzadeh, M.; Antucheviciene, J. Design of a Green Supply Chain Based on the Kano Model Considering Pricing. Sustainability 2023 , 15 , 13038. [ Google Scholar ] [ CrossRef ]
• Liou, J.J.H.; Tamošaitienė, J.; Zavadskas, E.K.; Tzeng, G.-H. New hybrid COPRAS-G MADM Model for improving and selecting suppliers in green supply chain management. Int. J. Prod. Res. 2015 , 54 , 114–134. [ Google Scholar ] [ CrossRef ]
• Scott, J.; Ho, W.; Dey, P.K.; Talluri, S. A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments. Int. J. Prod. Econ. 2015 , 166 , 226–237. [ Google Scholar ] [ CrossRef ]
• Dweiri, F.; Kumar, S.; Khan, S.A.; Jain, V. Designing an integrated AHP based decision support system for supplier selection in automotive industry. Expert Syst. Appl. 2016 , 62 , 273–283. [ Google Scholar ] [ CrossRef ]
• Mogeni, L.M.; Kiarie, D.M. Effect of Green Logistics Practices on Performance of Supply Chains in Multinational Organizations in Kenya. Ind. Eng. Lett. 2016 , 6 , 40–50. [ Google Scholar ]
• Muangpan, T.; Chaowarat, M.; Neamvonk, J. The key activities of green logistics management in the Thai automotive industry. J. Arts Sci. Commer. 2016 , 7 , 1–11. [ Google Scholar ] [ CrossRef ]
• Brusset, X.; Teller, C. Supply chain capabilities, risks, and resilience. Int. J. Prod. Econ. 2017 , 184 , 59–68. [ Google Scholar ] [ CrossRef ]
• Ribeiro, J.P.; Barbosa-Povoa, A. Supply Chain Resilience: Definitions and quantitative modelling approaches. Comput. Ind. Eng. 2018 , 115 , 109–122. [ Google Scholar ] [ CrossRef ]
• Heidari, A.; Imani, D.M.; Khalilzadeh, M. A hub location model in the sustainable supply chain considering customer segmentation. J. Eng. Des. Technol. 2021 , 19 , 1387–1420. [ Google Scholar ] [ CrossRef ]
• Eydi, A.; Fazayeli, S.; Ghafouri, H. Multi-period configuration of forward and reverse integrated supply chain networks through transport mode. Sci. Iran. 2020 , 27 , 935–955. [ Google Scholar ] [ CrossRef ]
• Antucheviciene, J.; Jafarnejad, A.; Amoozad Mahdiraji, H.; Razavi Hajiagha, S.H.; Kargar, A. Robust Multi-Objective Sustainable Reverse Supply Chain Planning: An Application in the Steel Industry. Symmetry 2020 , 12 , 594. [ Google Scholar ] [ CrossRef ]
• Gholamian, N.; Mahdavi, I.; Mahdavi-Amiri, N.; Tavakkoli-Moghaddam, R. Hybridization of an interactive fuzzy methodology with a lexicographic min-max approach for optimizing a multi-period multi-product multi-echelon sustainable closed-loop supply chain network. Comput. Ind. Eng. 2021 , 158 , 107282. [ Google Scholar ] [ CrossRef ]
• Alinezhad, M.; Mahdavi, I.; Hematian, M. A fuzzy multi-objective optimization model for sustainable closed-loop supply chain network design in food industries. Environ. Dev. Sustain. 2022 , 24 , 8779–8806. [ Google Scholar ] [ CrossRef ]
• Pazhani, S.; Mendoza, A.; Nambirajan, R.; Narendran, T.T.; Ganesh, K.; Olivares-Benitez, E. Multi-period multi-product closed loop supply chain network design: A relaxation approach. Comput. Ind. Eng. 2021 , 155 , 107191. [ Google Scholar ] [ CrossRef ]
• Nazari-Ghanbarloo, V.; Ghodratnama, A. Optimizing a robust tri-objective multi-period reliable supply chain network considering queuing system and operational and disruption risks. Oper. Res. 2021 , 21 , 1963–2020. [ Google Scholar ] [ CrossRef ]
• Rabe, M.; Gonzalez-Feliu, J.; Chicaiza-Vaca, J.; Tordecilla, R.D. Simulation-Optimization Approach for Multi-Period Facility Location Problems with Forecasted and Random Demands in a Last-Mile Logistics Application. Algorithms 2021 , 14 , 41. [ Google Scholar ] [ CrossRef ]
• Zarrat Dakhely Parast, Z.; Haleh, H.; Avakh Darestani, S. Green reverse supply chain network design considering location-routing-inventory decisions with simultaneous pickup and delivery. Environ. Sci. Pollut. Res. 2021 . [ Google Scholar ] [ CrossRef ]
• Abdolazimi, O.; Bahrami, F.; Shishebori, D. A multi-objective closed-loop supply chain network design problem under parameter uncertainty: Comparison of exact methods. Environ. Dev. Sustain. 2022 , 24 , 10768–10802. [ Google Scholar ] [ CrossRef ]
• Maliki, F.; Souier, M.; Dahane, M. A multi-objective optimization model for a multi-period mobile facility location problem with environmental and disruption considerations. Ann. Oper. Res. 2022 , 1–26. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Gupta, S.; Vijaygargy, L.; Sarkar, B. A bi-objective integrated transportation and inventory management under a supply chain network considering multiple distribution networks. RAIRO Oper. Res. 2022 , 56 , 3991–4022. [ Google Scholar ] [ CrossRef ]
• Bortolini, M.; Calabrese, F.; Gabriele Galizia, F.; Mora, C. A three-objective optimization model for mid-term sustainable supply chain network design. Comput. Ind. Eng. 2022 , 168 , 108131. [ Google Scholar ] [ CrossRef ]
• Mogale, D.G.; De, A.; Ghadge, A.; Aktas, E. Multi-objective modelling of sustainable closed-loop supply chain network with price-sensitive demand and consumer’s incentives. Comput. Ind. Eng. 2022 , 168 , 108105. [ Google Scholar ] [ CrossRef ]
• Al-Refaie, A.; Kokash, T. Optimization of sustainable reverse logistics network with multi-objectives under uncertainty. J. Remanuf. 2023 , 13 , 1–23. [ Google Scholar ] [ CrossRef ]
• Meidute-Kavaliauskiene, I.; Yıldırım, F.; Ghorbani, S.; Činčikaitė, R. The Design of a Multi-Period and Multi-Echelon Perishable Goods Supply Network under Uncertainty. Sustainability 2022 , 14 , 2472. [ Google Scholar ] [ CrossRef ]
• Allehashemi, T.; Hassanzadeh Amin, S.; Zolfaghari, S. A proposed multi-objective model for cellphone closed-loop supply chain optimization based on fuzzy QFD. Expert Syst. Appl. 2022 , 210 , 118577. [ Google Scholar ] [ CrossRef ]
• Abdi, F.; Farughi, H.; Sadeghi, H.; Arkat, J. Location-inventory-reliability optimisation problem in a multi-objective multi-period three-level supply chain network with stochastic demand. Eur. J. Ind. Eng. 2023 , 17 , 479–528. [ Google Scholar ] [ CrossRef ]
• Arab Momeni, M.; Jain, V.; Bagheri, M. A Multi-Objective Model for Designing a Sustainable Closed-Loop Supply Chain Logistics Network. Logistics 2024 , 8 , 29. [ Google Scholar ] [ CrossRef ]
• Varshney, N.; Gupta, S.; Ahmed, A. Addressing uncertainty in closed-loop supply chain networks: A multi-objective approach to integrated production and transportation problems. J. Model. Manag. 2024 , in press . [ Google Scholar ] [ CrossRef ]
• Xia, H.; Chen, Z.; Milisavljevic-Syed, J.; Salonitis, K. Uncertain programming model for designing multi-objective reverse logistics networks. Clean. Logist. Supply Chain 2024 , 11 , 100155. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Share and Cite

Khalilzadeh, M.; Antucheviciene, J.; Božanić, D. A Bi-Objective Model for the Multi-Period Inventory-Based Reverse Logistics Network: A Case Study from an Automobile Component Distribution Network. Systems 2024 , 12 , 299. https://doi.org/10.3390/systems12080299

Khalilzadeh M, Antucheviciene J, Božanić D. A Bi-Objective Model for the Multi-Period Inventory-Based Reverse Logistics Network: A Case Study from an Automobile Component Distribution Network. Systems . 2024; 12(8):299. https://doi.org/10.3390/systems12080299

Khalilzadeh, Mohammad, Jurgita Antucheviciene, and Darko Božanić. 2024. "A Bi-Objective Model for the Multi-Period Inventory-Based Reverse Logistics Network: A Case Study from an Automobile Component Distribution Network" Systems 12, no. 8: 299. https://doi.org/10.3390/systems12080299

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

Gartner: Three in Four Logistics Transformations are Failing

Logistics leaders based around the world are regularly launching new transformation initiatives in a bid to cut costs and enhance efficiency. 

But, according to the results of a new survey carried out by Gartner , a significant majority (76%) of these attempted transformations never fully succeed, failing to meet critical budget, timeline or KPI metrics.

There is hope, however, for executives and other decision-makers. The findings also show that effectively responding to team resistance and incorporating feedback dramatically increased the odds of transformation success.

"Leaders often respond to resistance by ramping up urgency and adopting a directive leadership style, which is not only ineffective, but actually counterproductive," comments Snigdha Dewal, Senior Principal Researcher in  Gartner’s Supply Chain practice .

"Instead, leaders should engage their teams from the start of the process, embrace the areas of resistance as a resource, not a problem and act on feedback to adapt transformation plans and how they are implemented. Harvesting the collective wisdom of their teams can lead to dramatically improved odds of success.”

Internal change resistance obstructs transformation

In carrying out their global survey, researchers from Gartner gathered the thoughts of 306 logistics professionals from organisations with US$500 million or more in enterprise-wide annual revenues. 

The study discovered that more than 80% of respondents had attempted four transformations in fewer than five years, averaging almost one a year. Internal change resistance was found to play a greater role in obstructing the success of their transformation initiatives when compared to outside pressures.

A similar proportion (81%) of  logistics leaders  believe transformation is critical, yet only one in five adopted the approach of using resistance as a resource to leverage the collective wisdom of their teams to improve transformation outcomes. 

Adopting this less common approach improved the odds of transformation success by 62%.

The prevailing ‘urgency approach’, characterised by directive leadership, limited stakeholder engagement and a ‘get with the programme’ mindset, led to a 47% decrease in the odds of transformation success. 

Gartner’s calculation of transformation success was determined from regression analysis of actions and the messaging adopted by organisations towards their teams during logistics transformations, in addition to subsequent impact on success metrics. 

Leaders must leverage resistance, says Gartner

Snigdha’s take is that, while resistance to transformation can be either productive or unproductive, leaders must shift from viewing resistance as a barrier to seeing it as a source of  valuable insights  – learning how to leverage it. 

"This approach not only enhances project management outcomes, but also boosts staff morale and can help unearth new competitive advantages,” she adds. 

Gartner says the three key drivers behind this leadership approach include: 

• Demonstrate listening: Leadership figures recognise objectives, but remain open-minded to changes or evolutions based on lessons learned during the process
• Involve resistant stakeholders: Leaders collaborate with the most resistant team members to better understand hurdles or interpret the broader team’s change appetite
• Maintain an adaptable mindset: Acknowledging that transformations have many setbacks, leadership teams often focus on mission-critical aspects. Failure can be viewed as an indicator of where not to focus efforts.

Check out the latest edition of Supply Chain Magazine and sign up to our global conference series – Procurement and Supply Chain LIVE 2024 . 

Supply Chain Digital is a BizClik brand.

• Gebrüder Weiss: E-Commerce Solutions for Leading Brands Logistics
• Analysing Transit Time Delays Along Key Global Trade Routes Logistics
• Cushman & Wakefield: Demand for Cold Chain Storage Booming Operations
• UPS, FedEx and DHL Ranked as Most Valuable Logistics Brands Logistics

Featured Articles

Analysing transit time delays along key global trade routes.

Nidhi Gupta, CEO at Portcast, examines how transit times across major global trade routes have been affected over the past 12 months …

Procurement & Supply Chain LIVE Dubai 2024 Watch On-Demand

Did you attend Procurement & Supply Chain LIVE Dubai in May? Watch on-demand to discover the latest trends and insights in the industry …

UPS, FedEx and DHL Ranked as Most Valuable Logistics Brands

For the 10th consecutive year, Brand Finance's logistics ranking is led by UPS, followed by its big American rival FedEx and Germany’s DHL …

P&SC LIVE: Ben Madin Senior Director at Bamboo Rose

Global supply & demand: key vendors in supply chain planning, 2 new speakers from pwc & versuni join p&sc live london.

• Greener Supply Chains: IKEA, Schneider Electric & npower
• P&SC LIVE Singapore 2024: Watch On-Demand Now
• FedEx Goes for Gold with Paris 2024 Olympics Strategy
• Two Months to Go: Procurement & Supply Chain LIVE London
• Shortlist Announced – The Global P&SC Awards 2024