literature review in inventory management

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South African Journal of Industrial Engineering

On-line version  issn 2224-7890 print version  issn 1012-277x, s. afr. j. ind. eng. vol.33 n.2 pretoria jul. 2022, http://dx.doi.org/10.7166/33-2-2527 .

GENERAL ARTICLES

Inventory management concepts and implementations: a systematic review

J.B. Munyaka I, * ; V.S.S. Yadavalli II

I CEAT - Urban and Regional Planning Community, ENAC -School of Architecture, Civil and Environmental Engineering, Swiss Federal Institute of Technology Lausanne, Switzerland II Department of Industrial and Systems Engineering, University of Pretoria, South Africa

Inventory is a central management function. It is a cornerstone of supply chain management and logistics in the material management system. Depending on the organisational objectives, inventories in warehouses may be needed to fulfil customer or humanitarian demands. Controlling inventory is critical to operational success and organisational performance. This research reviews inventory management concepts and implementations in the face of increasingly demanding human need. Demand is a critical variable in the inventory control system, and its characteristics affect inventory treatment. Important demand characteristics include its level of certainty, which could be deterministic (i.e., known with certainty) or stochastic/Bayesian (i.e., known but uncertain), and its structural dependency (i.e., independent or dependent). This review considers the deterministic independent and dependent natures of demand and their respective impact on inventory management in operations.

Voorraad is 'n sentrale bestuursfunksie. Dit is die hoeksteen van voorsieningskettingbestuur en -logistiek in materiaalbestuurstelsels. Afhangende van die doelwitte van die organisasie, mag voorraad in pakhuise nodig wees om aan klante of humanitére eise te voldoen. Die beheer van voorraad is van kritieke belang vir operasionele sukses en organisatoriese prestasie. Hierdie navorsing hersien voorraadbestuurskonsepte en die implementering daarvan te midde van toenemende veeleisende menslike nood. Aanvraag is 'n kritieke veranderlike in die voorraadbeheerstelsel en die kenmerke daarvan beinvloed voorraadbehandeling. Belangrike aanvraag kenmerke sluit in die vlak van sekerheid, wat deterministies (d.w.s. met sekerheid bekend) of stochasties/Bayesian (d.w.s. bekend, maar onseker) kan wees, en die strukturele afhanklikheid daarvan (d.w.s. onafhanklik of afhanklik). Hierdie oorsig oorweeg die deterministiese onafhanklike en afhanklike aard van aanvraag en hul impak op voorraadbestuur in bedrywighede.

1 INTRODUCTION

Inventory (stock) management is a critical operation in manufacturing and supply chain processes. The manufacturing process uses raw materials and work-in-process goods to create finished products that are stored as inventory or sold, some of which may also be used in follow-up operations. Inventory is the most important asset held by many organisations, representing as much as half of the company's expenses, or even half of the total capital investment. In addition, according to the Science Direct publication website (accessed in 2020) [48], the past two decades have seen an increase in inventory management research interest. As shown in Figure 1 , the publication of articles on inventory management has seen an increase of over 525 per cent, with the number of published articles increasing from 2 544 in 1998 to 13 381 in 2020.

Inventory management models are applied in nearly all operations. The scope in the literature spans fields such as manufacturing, medicine, humanitarian aid, environmental science, engineering, agriculture, and even energy. By filtering Science Direct's search for publications on inventory management, and taking into account both open access journals and journals to which only subscribers have access, it was found that the most frequently discussed topic in inventory management is environmental science, followed by engineering, energy, and others ( Figure 2 ). Given the increasing impact of climate change, environmental science has created the need for re-engineering, thus increasing the demand for stock management.

Figure 2 shows the range of areas affected by stock management. The crucial challenge for an organisation is often to have a balanced demand supply that minimises inventory costs and increases the satisfaction of the target beneficiaries. As stated by Nemtajela and Mbohwa [85], proper inventory management ensures a good balance between minimising the total cost of inventory and maintaining the desired customer satisfaction level.

The rise of online retailers such as Amazon or Alibaba over the past three decades has shown the growing importance of proper inventory management. In addition, there were situations in the past when poorly managed stocks caused companies such as Solectron to lose billions of dollars. This paper undertakes a systematic review of a number of stock management concepts and their contribution to the topics outlined in Figure 2 and to future research.

2 LITERATURE REVIEW

The concepts of inventory management date back to the early days of humanity. The practice of inventory has modernised and evolved over the last 100 years, with new tools and technologies being used to support the process. For instance, in ancient times, traders counted and tallied items that were sold each day - until the Egyptians and the Greeks developed more accurate inventory record management and accounting systems, in contrast to the inaccurate and inefficient practice of hand-written notes and hunches. Over the years, progress has been made in inventory management practices. These advances have led to further cost reduction and improved customer satisfaction.

What is inventory? According to Render et al. [96], inventory is a stored resource used to satisfy a demand, current and future. Similarly, Vrat [123] defines inventory as component parts, raw materials, WIP (work-in-process), or finished products held at a specific location (a warehouse) in the supply chain. Both authors, as well as Plinere and Borisov [92], listed inventory types that included the following: 1) raw materials, work-in-process, transit, finished goods, buffer (safety stocks), decoupling (contingencies stock), anticipation (speculation inventory), and cycle (business's standing inventory), etc. These inventories differ from one organisational sector to another. For example, in humanitarian relief supply chains, the difference between life and death depends, among many other things, on decoupling stocks, whereas, in the wholesale trade, buffer stocks or even transit stocks can prevent the organisation from losing its valuable reputation. Figure 3 shows the three types of inventories commonly used in manufacturing:

Inventory management is viewed as a central function in the inventory management system [79], [94], [3]. For Khobragade et al. [61], inventory management, also known as materials management, is identified as the organisation, securing, storage, and distribution of the right materials, of the right quality, in the right quantity, in the right place and at the right time, in order to coordinate and organise the creative movement in an integrated way within a mechanical project. Inventory management involves maintaining some stock levels at a minimised cost while improving the value-adding measures of customer satisfaction, which are useful measures of organisational performance [85], [43], [41], [105]. According to Christopher [27], an organisation with a good inventory management system is able to establish good policies and controls that monitor the level of inventory and determine what levels to maintain, when the inventory should be replenished, and the size of the order. Inventory levels for finished goods are viewed as a direct function of demand [96]. In the event of a higher demand in the supply chain, the inventory level decreases proportionally.

There are factors that influence inventory management practices. Prominent among these are organisational and human factors, financial constraints, and, more recently, the increasing rate of technology adoption [3]. Financially, 'inventory' is considered the biggest and most important asset of an organisation, which - according to Render et al. [96] - constitutes up to about 50% of the total capital investment of the company's assets. In humanitarian operations, inventory not only represents a significant financial asset, but also has a direct impact on saving lives. In the industrial sector, firms with goods inventory management practices are able to increase their overall profit margins, and so increase their level of production capital, and overall customer satisfaction [33], [92]. The flowchart in Figure 4 details the basic stages in an inventory management system.

Ivanov et al. [54] consider the trade-off between service level and cost as one of the most important financial decisions in inventory management. Other important financial decisions include how much to order and how much physical inventory to hold in a warehouse in anticipation of a sudden increase in demand, to avoid delays in supply chains. It is also worth including the management of unused stock and the costs associated with holding physical stock in a warehouse as an important financial decision.

Another important factor influencing inventory management practices, apart from the financial, is technology. Although technology has largely influenced inventory management positively, Ahmad and Mohamed Zabri [2] believe that, since its introduction, technology has exposed the human impact on the day-to-day handling of inventory. In Table 1 , Rushton et al. [98] compare different technological applications with different daily human processing of inventory activities. With a similar number of activities (3,000,000), Table 1 shows the error occurrence rate in processing activities using inventory management applications. The results show that the less daily human handling of stocks there is, the fewer the errors.

The study conducted by Ayad [7] examined the influence of human factors in inventory management practices by assessing different stores from the same company, each run by a store manager. The aim of the exercise was to identify human variables within the store manager's control. Findings from the study revealed that the diversity of store types and the variety of departmental operations resulted in different outcomes within the same organisation. Furthermore, analysis of Ayad's [7] findings showed that human factor variations were the result of their useful knowledge, leadership ability, or critical thinking. Another study was conducted by Strohhecker and Grobler [107] that focused on inventory managers' physiological traits; their findings identified the following four traits: personality, knowledge, intelligence, and interests. The authors also investigated the impacts of these traits on their performance, specifically during a dynamically complex inventory management task [107]. The findings showed the 'intelligence' trait to be the sturdiest predictor of inventory routine, while 'interest' in social matters led to worse inventory performance and higher costs. Using technology in inventory control is not a novelty; but a good number of organisations still avoid using technology for economic (financial) and expertise (human) reasons. Many inventory management technology applications offer efficiency - but they come at a cost.

Other factors influencing inventory management practices are related to forecasting decisions. Rajeev [94] discussed the 'rule of the thumb' decision, which resulted in less purchasing, use of a computer, variable lead-times, less attention to forecasting, training, and development, and random ordering of material [2]. Bala [9] showed that using sophisticated computerised systems for forecasting improves the profitability of an inventory management system.

3 ROLES, INVENTORY MODELS, AND THE DEMAND VARIABLE

3.1 Role of inventory

Since the primary role of inventory management is to maintain a desired stock level of defined products or items [119], the role of inventory in operations management cannot be overemphasised. History has shown that organisations that have neglected or failed to consider the importance of inventory management have lived to regret it. According to Tanthatemee and Phruksaphanrat [114], inventory management helps to improve customer service and to cope with demand uncertainty. Demand uncertainty is a potential challenge that results in high inventory levels and high carrying costs, which can lead to higher prices and low customer satisfaction, and thus a less profitable business.

3.2 Inventory models and the demand variable

The main decisions affecting demand in an inventory management problem are a) when to purchase (creation of a purchase order), and b) how much to purchase (lot size) [82]. Resolving both problems in a decision-making process requires the development of inventory models and techniques [96]. These two decision-making problems connect the inventory model's objective function with a number of decision variables (e.g., lot size and re-order point). It also links up with several inventory-related cost parameters and situational variables, such as a) the demand nature and level (including its level of certainty); b) the lead time (including its level of certainty); c) extant constraints (if any); d) quantity discounts or inflationary trends; and e) other relevant issues.

Demand is regarded as a critical variable in inventory management. Accurately forecasting the market or the level of demand helps to make the correct inventory decisions that optimise sales and profits. Based on the level of certainty of demand, two types of inventory model are usually developed: (1) deterministic demand models, and (2) stochastic (Bayesian) demand models.

3.2.1 Deterministic demand model (demand known with certainty)

In the deterministic model, inventory operations are determined on the basis of a known (certain) demand. A deterministic model produces the same output because of certainty about the factors, conditions and parameters involved, which are clearly stated at the start. Among the parameters is demand. According to Antic et al. [4], deterministic demand is represented as a sales forecast for each product per month. The deterministic demand model aims to minimise the overall costs related to production time, setup time, and overtime, and those associated with inventory, such as ordering costs, carrying costs, and stock-out costs (overstocks and shortages).

The deterministic demand model may have two types of demand: independent and dependent. It is crucial in an inventory control system to understand the difference between those two demand types as the starting point for an inventory policy. Independent demand is the demand for finished products such as cars or books, and may involve some level of uncertainty as well; while dependent demand focuses on component parts or sub-assemblies such as box console sub-assemblies for Toyota cars, and is usually considered certain once the end item on which it depends is known. Figure 5 illustrates the nature of both kinds of demand.

In an independent demand environment, the demand for an item does not depend on the demand of another item (see Figure 5 ). For example, finished goods items do not depend on other items because the focus is on item sale, order processing, or sales forecast. The independent demand for inventory is founded on confirmed forecasts, customer orders, estimation, and past history.

However, unlike independent demand, dependent demand for an item depends on the demand for another item. For instance, raw material and component stocks are dependent on the demand for finished goods. Raw materials and other manufacturing components, for instance, are converted to finished goods through systems such as material resources planning (MRP), distribution resources planning (DRP), or enterprise resource planning (ERP).

3.2.2 Stochastic (or Bayesian) model (demand known without certainty)

In a stochastic (probabilistic or Bayesian) model, inventory decisions are made in the light of uncertainty (demand and/or lead time). According to Antic et al. [4], stochastic demand is generated as a random variation of sales forecast within a range of about 20 per cent. For Nemtajela and Mbohwa [85], the uncertainty in demand is the result of factors such as changes in purchase orders and unpredictable events. Furthermore, Tanthatemee and Phruksaphanrat [114] believe that uncertain inventory demand is the result of changes to orders, the random capacity of suppliers, or unpredictable events. Sil [103] listed three types of stochastic model:

a) Single period: mainly concerns fashion products, perishable products, products with a short life cycle, or even seasonal products. The single period is a one-off decision (how much to order).

b) Multiple period: concerns goods whose demand is recurrent but varies from period to period; inventory systems with periodic revisions. The multi-period stochastic model is a periodic decision (what quantity to order in each period).

c) Continuous time: related to goods with recurring demand but with variable inter-arrival time between customer orders; inventory systems with continuous revisions. The continuous period stochastic model is a continuous decision (continuously deciding how much to order).

4 INDEPENDENT DEMAND INVENTORY MODELS

The main objective of an inventory management is to minimise operational costs. Minimising cost in independent demand inventory models consists of the following functions:

• optimisation of fast-moving stock to avoid stock-out (understocking)

• proper definition of safety stock (involving ordering point to prevent any risk of premature depletion of inventory)

• reduction in excessive inventory (overstocking)

Among the most significant inventory-related costs in a decision support system are: 1) ordering cost, 2) carrying or holding cost, 3) goods purchase cost, 4) stockouts and shortages cost, and 5) storing cost. According to Vrat [123], Inventory carrying (holding) costs, cost of shortage and stockout, and ordering costs are the three types of inventory-related cost that are primarily associated with inventory decision-making models. Independent demand inventory is composed of two main types of models: a) single-period (perishable) inventory models, and b) multi-period inventory models, as shown in Table 2 .

4.1 Single-period (perishable) inventory model or the newsboy problem

A single-period inventory model is one that is applied by organisations that order perishable or one-time items. Such models require the order to be of the right quantity, fearing overstocks and waste as soon as the product has passed its perishable or expiry date. The same rule applies to seasonal items or any item that is no longer of value after the time it is required. A single-period inventory model does not include only perishable items, but also a wide variety of items such as style items, spare parts, or special season items. As items become obsolete at the end of the cycle, decision-makers face challenges such as managing the demand for single or multiple items. The key differences between a single-period (perishable) inventory model and a multi-period inventory model are listed in Table 2 below.

In addition to the above differences, a single-period inventory model requires that orders for items be placed before the start of the period, and replenishment cannot be done during the period. Furthermore, the stock remaining at the end of the period is considered obsolete and so is eliminated from the inventory; and its value is deducted from the profit of another order at the beginning of a new period. When the order is larger than the demand, the extra items ordered are counted as lost.

Research into the single-period inventory model is essential for businesses as well as for humanitarian aid organisations, as determining the exact number of perishable or seasonal items an organisation needs maximises the productivity expected from that particular organisation and reduces waste. In a humanitarian environment, little research has been conducted on disaster inventory systems that focus on perishable or seasonal items, particularly in the aftermath of a disaster. Among the few notable researchers are Yadavalli et al. [127]. Their analysis proposed a continuous review disaster inventory model with a doubly substitutable perishable item. Using an emergency situation, they proposed to replace an out-of-stock item with a similar item available in the inventory, thus avoiding a long waiting time and favouring instant replenishment. For example, a particular blood type that is not available in the blood bank at the time of application or that is past its expiry date may be replaced by an acceptable and available universal type.

Researchers on the newsboy (Single-period [perishable]) inventory model also called the Christmas tree problem, particularly deal with (1) single perishable items (see, for example, [56], [88], [57], [10], [40]) and (2) multi perishable items (see, for example, [90], [126], [34], [12]). According to Satyendra et al. [99], a newsboy must achieve a stock decision quantity that maximises the expected profit while minimising the expected loss under stochastic demand conditions (see the first statement in Table 2 ).

4.1.1 Single perishable item problem

According to Joy and Jose [55], a perishable product is one whose value decreases over a given period. Perishable items make inventory management more challenging, as they affect the inventory, service, and re-order levels. Some examples of perishable item are vegetables, fruit, baked products, and fashion items. The deterioration or loss of value of perishable products can be explained by their short life (fruit and vegetables), changes in trends (fashion items), etc. The application of the perishable inventory model is broad, and needs to be assessed on a case-by-case basis. Since the first study on perishables by Whitin [124], models have been developed that take into account perishable aspects such as items on display, the freshness of items, and price dependency [39]. Avinadav et al. [5] developed a single perishable item model that optimises the price, order quantity, and replenishment periods of a perishable item with price- and time-dependent demand. Taleizadeh et al. [112] studied discounted inventory models, focusing on the customer decision as a key factor, while Liu [73] studied a perishable inventory model with product lifetime incorporated into it. Under specific assumptions, single perishable item models are developed.

4.1.2 Multi perishable Items problem

A multi perishable items problem was initially solved by Hodges and Moore [47] using stochastic demand competing for a number of limited resources. Authors who have been solving problems related to multi-constraint or multi-item single period inventory intend to maximise the probability of targeted profits [103], [68]. In addition, an approach to solving the multi perishable items inventory model with constraints was developed by Ben-Daya and Raouf [12], considering both financial and space constraints, with items demand following a uniform probability distribution function. Further studies were conducted by Layek et al. [69] that targeted the investigation of a two-fold solution space with constraints for a multi perishable item problem. Rahimi et al. [93] introduced a two-stage stochastic mixed integer non-linear programming (MINLP) model that assists companies that are considering discount policies. With regard to the multi-product single-period inventory problem, Bhattacharya [14] and Kar et al. [59] developed a multi-item inventory model for items that are deteriorating. With Kar et al. [59], the main focus was on multi perishable items with constraints in the storage space available and the level of investment. A multi-item inventory model for items that are deteriorating was further studied by Tayal et al. [115], targeting an acceptable shortage level and the product expiration date.

Kumar et al. [65] presented a multi-item, multi-constraint problem with stochastic demands for different types of item. The model also considered the replenishment time, constraints in storage space, and the level of stock-out associated with the cost of understocking and overstocking per unit shortage, and surplus inventory. The notation used in the Kumar et al. [65] model is as follows:

r Set of retailers (1 to R).

p Set of products (1 to P).

x A random variable representing the demand.

f rp (x) Probability density-function of demand of product 'p' at retail outlet 'r'.

US p Understocking cost of product 'p' (Rs. per unit).

0S P Overstocking cost of product 'p' (Rs. per unit).

Cap r Storage capacity at retail outlet V (units).

Sup p Available supply of product 'p' (units).

E rp Expected cost of product 'p' at retail outlet 'r' when supply quantity is Q rp .

µ rp Mean demand for product 'p' at retailer 'r'.

σ rp Standard deviation of product 'p' at retailer 'r'.

z rp () Standard normal deviation of product 'p' at retailer 'r'.

Φ rp Q Cumulative density function of product 'p' at retailer 'r'.

Φ rp Q Probability density function of product 'p' at retailer 'r'.

The model's objective was to minimise (Z{) the total expected cost of all products associated with understocking and overstocking in all the retail outlets. The model is presented next. Minimise (Q rp )

Equation (2) deals with retail outlets' storage capacity constraint, while Equation (3) makes sure that the product supply is not exceeded by delivery. Finally, Equation (4) is the non-negativity constraint. Kumar et al. 's [64] model is most applicable in the distribution of high-quality perishable foods chain.

4.2 Multi-period deterministic inventory models

A multi-period inventory model is a lot-sizing model that optimises the procurement of both single and multiple products, with cases of both a particular supplier and multiple suppliers, and from one period to another. A multi-period inventory model further involves the option of focusing on the economies of scale in the procurement process instead of accruing inventory costs from one period to the next [97]. Lot sizing, both static and dynamic, involves determining the number of items required during a manufacturing process. A multi-period inventory model has two variations:

• Fixed order quantity systems (Q model): a fixed order is placed each time the minimum stock level (re-ordering point) is reached.

• Fixed time period models (P model): orders are placed at allocated times, with the amount of inventory being determined in the aftermath of a review of the stock levels.

Figure 6 below compares both types of multi-period inventory model for further understanding:

4.2.1 Fixed-order quantity models (Q model)

A fixed-size ordering system is a pre-defined standard inventory system of a quantity of items that gradually decreases from the maximum level (Q) to the minimum level (Zero), until it reaches the ROP (re-order point), and then a new order of size EOQ (economic order quantity) is placed.

Fixed-order quantity models generally deal with certain demands; therefore, a new order of a fixed size is lodged as soon as the stock reaches the ROP ( Figure 6 ). However, schedule orders can be scheduled to arrive at an increased lead time (L), introducing uncertainty to the demand and the system. In such cases, the precise demand throughout the lead time (ROP in the EOQ) becomes unknown. Barros et al. [11] listed a variety of uncertainty and risk factors associated with the procurement process ( Table 3 ):

To avoid stockouts, extra stock, called safety stock, is kept on hand ( Figure 7 ). Safety stock prevents stockouts in case the demand is higher than expected.

Figure 8 focuses on a stock level that will trigger re-ordering, while in Figure 7 , although there is a re-order point, a safety stock (minimum stock) is applied. Table 4 below lists relevant publications discussing the application of the fixed-order quantity model.

4.2.2 Fixed-time period model (P model)

Maintaining an optimal stock is a challenge faced by all organisations. Unlike in the fixed-order quantity system (Q model) in which orders are only placed after the item has reached the determined re-order point (ROP), in a fixed-time period model (P model), each item's stock position is reviewed periodically, as shown in Figure 9 below. When a stock level of a given item is determined, the decision to place an order is made, taking into account the following elements: 1) the customer's request for the item in question, and 2) the adequacy of the current stock level of the item in the supporting chain or production operation until the next revision.

Using diverse service levels, Mahfuz et al. [76] applied the fixed-time period model while conducting a study in a services environment from 2004 to 2006. The study found that applying the P model saved an average cost for the services that ranged between 65 and 80 per cent of inventory investment, with a 98 to 100 per cent service level being provided. The capital raised through savings gives organisations a competitive advantage, as it can be used to purchase new technologies such as those for stock counting and stock location within the system.

According to Huang et al. [50], the fixed-time period ordering is conducted at a fixed and predetermined interval with some assumption such as 1) a variable demand, 2) a regular Lead time, 3) an ordering to restock the system to its full capacity, 4) non fixed ordered quantity, while the order timing is fixed, 5) the ordered quantity is much lower than the safety levels.

Following the above assumptions of Huang et al. [50], Equation (Eq 5, 6, 7 and 8) was generated below:

d is the average demand,

t is the fixed interval for re-order,

L is the lead time,

σ d is the standard deviation of demand and

Z a Is the standard normal Table

5 DEPENDENT DEMAND INVENTORY MODELS

Unlike independent demand inventory models, in which the demand for one item is independent of the demand for other items, in dependent demand inventory models, items are interconnected. Therefore, the demand for one item is directly dependent on the demand for another item. In an engine assembly plant, for example, the demand for the console, tyres, engine, etc. depends on the demand for a car. To manage the manufacturing process of finished products in the case of dependent demand for raw materials and other components, material resource planning (MRP) is mainly used. This management tool is applied with the help of models or applications such as just-in-time (JIT) and Kanban. In addition to MRP, enterprise resource planning (ERP) software is used to integrate all the departmental functions of organisations into one system. Software such as Oracle, SAP, and Microsoft Dynamics are also used in distribution resource planning (DRP) situations.

The association between independent demand and dependent demand is shown in a bill of material. The dependent demand is derived from the independent demand, and helps to find the quantities ordered for the dependent demand. For example, determining the quantity of finished products, such as automobiles, that are expected to be sold (independent demand) can help to determine the dependent quantities of components, such as wheels, tyres, and braking systems, that are needed to complete the production of the automobiles. For one car produced, for example, four wheels, two windscreen wipers, and two headlights are needed, among other components.

5.1 Material resources (requirements) planning (MRP)

The order quantities for dependent demand are found through the material requirements planning (MRP) system. MRP takes into account of the quantities of the required components, as well as the time needed to produce and receive them. Moustakis [80] defines MRP as a time-phased priority planning technique for computing the material requirement and scheduling supply to meet the allocated item demand [52]. This planning technique is a computer-based production as well as inventory control system that ensures a better customer order response. According to Heizer and Render [46], some areas where MRP implementation are useful include the following: (1) production scheduling, (2) quicker response to market variations, (3) improved adherence, (4) enhanced labour and facilities utilisation, and (5) control of inventory levels. Furthermore, as a starting point for further actions, MRP is dependent on the sales forecast for finished goods. Handling raw materials is far more challenging than handling finished goods, as it involves the analysis and coordination of delivery capacity, logistical processes, lead time, transportation, warehousing, and scheduling, before their final supply to the production shop floor. Raw materials administration also involves the periodic review of inventory holding and inventory tally and audit, followed by a comprehensive analysis report, leading to good financial management decisions.

A successfully implemented MRP system is able, simultaneously: (1) to ensure that the required materials (including components and other items needed for production) are available and meet customer delivery targets; (2) to ensure that inventory levels are kept as low as possible, and (3) to plan manufacturing and purchasing activities and delivery schedules.

The MRP system has three main inputs that help the system to function: (1) the master production schedule (MPS); (2) the bill of material (BOM), also known as product structure records; and (3) inventory status records. The MPS indicates the quantity of finished goods desired and the expected time of receipt of the delivery, including the necessary safety stock. The BOM consists of data on each material or process required to produce that material. The information included in the BOM includes the type of raw materials (parts and components), the item number, and the description and quantity per assembly and sub-assemblies needed to manufacture an item. Finally, the stock status file also has the role of maintaining the integrity of the record by recording and maintaining information about all of the items in stock, including stock on hand and scheduled receipts.

5.1.1 MRP applications in a material structure tree

MRP is used for both single and multiple items. Its application to a single material is illustrated in the following example. '10 units of A' means that 10 units of material A are needed, while '20 units of B' means that 20 units of material B are needed. This illustration shows that one unit represents one material for A and B respectively. But MRP is also successfully applied to multiple materials with two or more complex BOMs. For example, when 10 units of material A are to be produced within seven weeks, material A requires seven units of material B and five units of product C, while material C requires 10 units of material D and eight units of material E. In addition, item B requires three units of item D and eight units of item C; and item B requires three units of item E. From the BOM, the material structure tree is developed (as in Figure 10 below), and the stock items to be produced at each level have been calculated, the demand for B, C, D, and E being dependent on the demand for A (10 units). The demands for items B, C, D, and E were calculated as follows: Req(B)=10 x 7 = 70 units; Req(C)=10 x 5 = 50 units; Req(D) = 50 x 10 = 500 units; Req(E)= (8 x 50) + (3 x 70) = 610 units.

The material structure tree in Figure 10 has three levels, defined as follows: Level 0, Level 1, Level 2; the 'parent' elements being A, B and C, while the 'component' elements are B, C, D, E. It can be seen that elements B and C are both components and parents.

5.1.2 An outline of the MRP process

After constructing the material structure tree as in Figure 10 , a schedule needs to be constructed that reveals: 1) the schedule of items ordered from suppliers in case there is no stock available, and 2) the schedule of production of the final items in order to satisfy the customer's demand for the finished products in time.

Five scheduling steps are used to determine the schedule in an MRP process. The schedules used for the production of the required material A in seven weeks, as shown in Table 5 , are as given below:

(1) Gross material requirements plan

The raw material requirements plan is the step in the schedule that determines when a material is needed and when its production is required to meet the customer's demand for finished goods. A key aspect of establishing or constructing gross material requirements is determining the lead times. Using the above example of producing 10 items of A, assume that the lead time for item A is one week, for item B two weeks, for item C two weeks, and for items D and E one week each, while for parent item B it is two weeks. Since the lead time of parent item A is one week (level 0), items B and C must be available at the end of the sixth week. Since the lead time of item B is two weeks, it must be released for production at the end of the third week. Similarly, item C and its units must be released for production at the end of the third week. Finally, items D and E and their units from parent item C are to be released to production at the end of the second week, while item E from parent item B is released to production at the end of the first week.

(2) Net material requirements plan.

The net material requirements plan is constructed in a similar way to the gross requirements plan. As with the gross requirements plan, the scheduling work starts with parent A and is scheduled backwards (from week 7) to the last components, as shown in the material structure tree in Figure 9 . Determining the lead times for each item is another critical aspect in the calculation of the net material requirements plan. Using the same data for A, B, C, D, and E as shown in the material structure tree, the net material requirement plans are also calculated using the available inventory. As shown in Table 5 , the net requirements balance the quantity needed to meet the demand (the gross requirement).

The on-hand inventory is the number of inventory items available to a store, ready for production and shipment. In an MRP process, the on-hand inventory is the parent materials and components available before the next batch of materials is received. Looking at the material structure tree and its number of materials in Figure 9 , it is assumed that material A has two units of stock, materials B and C have 10 units of stock, material D has 20 units of stock, material E has 15 units of components for material C and 20 units of components for material B. The stock on hand has a direct impact on the net material requirement and the receipt of the planned order, as it is the value subtracted from the gross material requirement plan.

For item A, with a gross requirement of 10 units and an assumed available stock of two units, the net requirement and planned order receipt are both eight units, as shown in Table 5 . Table 5 also shows that the net requirements and the planned incoming order are planned for week 7.

(3) Planned-order receipt.

The planned receipt of order, also known as the planned receipt, is the projected quantity of planned material receipt based on the net material requirement. For example, item A in Table 5 has a net material requirement of 40 units, so the planned receipt is estimated to be the same quantity - 40 units.

(4) Planned-order release.

In contrast to the previous stages of the schedule, planning delivery schedules, as shown in Table 5 , take into account the lead times of each material, starting with zero level materials (A) up to level 2 materials (D, E). With 40 units of item A cleared and staggered in time in the seventh week of production, and considering that item A has a lead time of one week, the planned order release for item A is therefore 40 units that are scheduled in the sixth week of production. The process continues until all materials are broken down

5.1.3 Just-in-time (JIT)

Just-in-time (JIT) is a famous Japanese concept of material planning, initially used by Japanese manufacturing companies before spreading worldwide. According to Vrat [123], JIT (or the zero-inventory system) is an idealised concept of inventory management in which a supply is delivered just-in-time, whatever material is required, wherever it is required, whenever a maximum supply is needed, and without keeping any stock on hand. In terms of material resource planning, the just-in-time concept allows companies to manage their warehouses with greater efficiency, avoiding inventories, shortages, or replenishment orders.

5.2 Enterprise resources planning (ERP)

An ERP is an integrated software that includes a set of functional modules (production, human resources, sales, finance, etc.) and incorporates all the departmental functions of organisations into one system, meeting the needs of all of the departments [15].

An ERP system, when implemented, can improve departmental performance and increase productivity. According to Bhamangol et al. [13], ERP improves access to and the accuracy and timeliness of information. It improves workflow, reduces dependence on paper trails, improves knowledge sharing, enhances control, and automates all processes by integrating and coordinating the flow of information across departments. ERP systems that are implemented in software such as Oracle, SAP, SYSPRO, or Microsoft Dynamics help large organisations to manage the large amount of data they process. Table 6 below lists the advantages of ERP systems.

5.3 Distribution resources planning (DRP)

Enns and Suwanruji [36] defined distribution requirement planning (DRP) as a time-based replenishment approach with revised inventory status and periodically generated shipping plans. The main objective of the distribution system is to provide maximum service to the customer. The concepts and logic used in the DRP system are similar to those used in the material requirements planning system, with the notable difference that DRP focuses on the distribution of remote goods rather than on the flow of parts (materials, components) within a warehouse or operating facility. Figure 11 illustrates the flow of DRP materials from the factory to the retailer and then to the end user.

In a DRP, the retailer's demand is treated as an independent demand because it is closely related to the end user, while the factory's demand (upstream) is treated as a dependent demand with a time logic that is used to anticipate needs. DRP has many benefits, including better service to customers and reduced inventory [36]. In addition, DRP for material flow is compatible with other supply chain systems [36].

6 CONCLUSION

A systematic review of inventory management concepts was discussed in this article. The study showed a growing level of interest in this process of supply chain management. With the increasing range of problems related to climate change, environmental science has been among the sectors with the most interest in stockpile management. The article reviews the literature on the deterministic demand model, highlighting the implementation of independent and dependent demand in a real situation. In the independent demand models, decision variables were developed based on the demand, type of products, cost/profit, time, ordering opportunity, and selling season. In contrast to independent demand inventory models, in which the demand for one material is independent of the demand for other materials, in dependent demand inventory models the materials are interconnected. In implementation, to manage the manufacturing process of finished products in a case of dependent demand for raw materials and other components, material resource planning (MRP) is mainly used. Enterprise resource planning (ERP) software integrates all of the departmental functions of organisations into one system. Examples of well-known ERP systems have been listed. On the other hand, like the material requirements planning system, the concepts and logic used in the DRP system focus on the distribution of goods at a distance rather than on the flow of parts (materials, components) within a warehouse or operating facility. This systematic review provides the research community with the tools to implement an inventory management project.

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[114] Tanthatemee, T. and Phruksaphanrat, B. 2012. Fuzzy inventory control system for uncertain demand and supply. Proceedings of the international Multi conference of Engineers and Computer Scientists 11 (1), IMECS 2012, March 14-16, Hong Kong.         [  Links  ]

[115] Tayal, S., Singh, S.R. and Sharma, R. 2014. An inventory model for deteriorating items with seasonal products and an option of an alternative market. Uncertain Supply Chain Management, 3(1), pp. 69-86.         [  Links  ]

[116] Taylor Ill, B.W. (2010). The Introduction to management Science. 8 th Edition. Chapter 16, Inventory Management. https://jeryfrl.files.wordpress.com/2013/04/ch16-inventory-management.pdf (Accessed 8 June 2022).         [  Links  ]

[117] Teng, J.T. and Chang, C.H.T. 2009. Optimal manufacturer's replenishment policies in the EPQ model under two levels of trade credit policy. European Journal of Operational Research, 195(2), pp. 358-363.         [  Links  ]

[118] Tinani, K.S. and Kandpal, D.H. 2017. Literature review on supply uncertainty problems: Yield uncertainty and supply disruption. Journal of the Indian Society for Probability and Statistics, 18(2), pp. 89-109.         [  Links  ]

[120] Ucharia, S.V. and Kumar, P. 2017. To study the inventory management system at organization level. International Journal of Engineering Science and Computing, 7(8), pp. 14503-14506.         [  Links  ]

[121] Ullah, A., Baharun, R.B., Nor, K.M., Siddique, M. and Sami, A. 2018. Enterprise resource planning (ERP) systems and user performance. International Journal of Applied Decision Sciences, 11(3): pp. 297-322.         [  Links  ]

[123] Vrat, P. 2014. Basic Concepts in Inventory Management. Springer Texts in Business and Economics, in: Materials Management, edition 127, chapter 2, pp. 21-36, Springer.         [  Links  ]

[124] Whitin, T.M. 1957. Theory of inventory management. Princeton, NJ: Princeton University Press.         [  Links  ]

[125] Wu, Q., Fang, A., & Gao, H. 2010. A study on inventory cost reduction based on economic order quantity model. 2010 International Conference on Logistics Systems and Intelligent Management (ICLSIM), 3 (1), pp. 1341-1343.         [  Links  ]

[126] Yadavalli, V.S.S., Van Wyk, E. and Udayabaskaran, S. 2015. A temporo-spatial model for optimal positioning of humanitarian inventories for disaster relief management. Applied Mathematics & Information Sciences an International Journal, 9 (3), pp. 1205-1211. http://dx.doi.org/10.12785/amis .         [  Links  ]

[128] Zhen, H., Lee, C.K.M. and Linda, Z. 2018. Procurement risk management under uncertainty: A review. Industrial Management & Data Systems, 118(7), pp. 1547-1574.         [  Links  ]

[129] Zhou, Y. 2012. The bi-ramp type demand and price discount inventory model for deteriorating items. World Congress on Intelligent Control and Automation (WCICA), Beijing, China, pp. 3298-3304.         [  Links  ]

Submitted by authors 12 May 2021 Accepted for publication 25 Apr 2022 Available online 29 Jul 2022

ORCID® identifiers J.B. Munyaka: 0000-0001-9452-8225 V.S.S. Yadavalli: 0000-0002-3035-8906 * Corresponding author [email protected]

• Corpus ID: 168936950

Inventory Management- A Review of Relevant Literature

• V. Lakshmi , K. Ranganath
• Published 17 October 2016
• Paripex Indian Journal Of Research

34 References

Inventory management in fertiliser industry of india: an empirical analysis, an analytical study on inventory management in commercial vehicle industry in india, impact of inventory management on the financial performance of the firm, inventory and working capital management: an empirical analysis, relationship between inventory management and profitability: an empirical analysis of indian cement companies, inventory management in malaysian construction firms: impact on performance, inventory reduction and productivity growth: linkages in the japanese automotive industry, panel data analysis on retail inventory productivity, on the relationship between inventory and financial performance in manufacturing companies, inventory management practices and business performance for small-scale enterprises in kenya, related papers.

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Please note you do not have access to teaching notes, a review of inventory management research in major logistics journals: themes and future directions.

The International Journal of Logistics Management

ISSN : 0957-4093

Article publication date: 15 August 2008

The purpose of this paper is to provide a review of inventory management articles published in major logistics outlets, identify themes from the literature and provide future direction for inventory management research to be published in logistics journals.

Design/methodology/approach

Articles published in major logistics articles, beginning in 1976, which contribute to the inventory management literature are reviewed and cataloged. The articles are segmented based on major themes extracted from the literature as well as key assumptions made by the particular inventory management model.

Two major themes are found to emerge from logistics research focused on inventory management. First, logistics researchers have focused considerable attention on integrating traditional logistics decisions, such as transportation and warehousing, with inventory management decisions, using traditional inventory control models. Second, logistics researchers have more recently focused on examining inventory management through collaborative models.

Originality/value

This paper catalogs the inventory management articles published in the major logistics journals, facilitates the awareness and appreciation of such work, and stands to guide future inventory management research by highlighting gaps and unexplored topics in the extant literature.

• Inventory management
• Supply chain management

Williams, B.D. and Tokar, T. (2008), "A review of inventory management research in major logistics journals: Themes and future directions", The International Journal of Logistics Management , Vol. 19 No. 2, pp. 212-232. https://doi.org/10.1108/09574090810895960

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• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology

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DOI : 10.17485/ijst/2019/v12i13/132758

Year : 2019, Volume : 12, Issue : 13, Pages : 1-13

Review Article

Inventory Management in Manufacturing Systems: A Literature Review

German Herrera Vidal 1* , Dayrene Junco Villadiego 2 and Margarita Mancebo Calle 3

1 Ingeniería Mencion en Industrial, M.Sc. en Ingenieria con Enfasis en Industrial. Docente investigador de la Universidad del Sinu Seccional Cartagena, Grupo de Investigacion Deartica, Cartagena, Colombia; [email protected] 2 Ingeniera Industrial, Universidad del Sinu Seccional Cartagena, Cartagena, Colombia; [email protected] 3 Ingeniera industrial, Universidad del Sinu Seccional, Colombia; [email protected]

*Author for correspondence German Herrera Vidal Ingeniería Mencion en Industrial, M.Sc. en Ingenieria con Enfasis en Industrial. Docente investigador de la Universidad del Sinu Seccional Cartagena, Grupo de Investigacion Deartica, Cartagena, Colombia. Email: [email protected]

Objectives: This research seeks to review the literature, based on exploration mechanisms, on the subject of planning and control of inventories in manufacturing systems. Methods: The purpose of this research is based on a review of the literature under a scientometric and bibliometric approach, regarding the planning and control of inventories in manufacturing systems, important services such as number of publications, authors, journals, countries and languages. Findings: Current issues have been found and they have worked with greater intensity, in this sense. Improvements: Provides a broad spectrum to develop new research that contributes to literature.

Keywords: Decision Making, Inventory Management, Systems of Manufacture

• 14 April 2020

How to cite this paper

Vidal et al., Inventory Management in Manufacturing Systems: A Literature Review. Indian Journal of Science and Technology. 2019;12(13):1-13

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• Published: 20 August 2024

A rank ordering and analysis of four cognitive-behavioral stress-management competencies suggests that proactive stress management is especially valuable

• Robert Epstein   ORCID: orcid.org/0000-0002-7484-6282 1 ,
• Jessica Aceret 1 ,
• Ciara Giordani 1 ,
• Vanessa R. Zankich   ORCID: orcid.org/0000-0003-2375-6209 1 &
• Lynette Zhang   ORCID: orcid.org/0000-0001-9435-6312 1  

Scientific Reports volume  14 , Article number:  19224 ( 2024 ) Cite this article

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• Human behaviour

The main objective of this study was to determine the relative value of four cognitive-behavioral competencies that have been shown in empirical studies to be associated with effective stress management. Based on a review of relevant psychological literature, we named the competencies as follows: Manages or Reduces Sources of Stress, Manages Thoughts, Plans and Prevents, and Practices Relaxation Techniques. We measured their relative value by examining data obtained from a diverse convenience sample of 18,895 English-speaking participants in 125 countries (65.0% from the U.S. and Canada) who completed a new inventory of stress-management competencies. We assessed their relative value by employing a concurrent study design, which also allowed us to assess the validity of the new instrument. Regression analyses were used to rank order the four competencies according to how well they predicted desirable outcomes. Both regression and factor analyses pointed to the importance of proactive stress-management practices over reactive methods, but we note that the correlational design of our study has no implications for the possible causal effects of these methods. Questionnaire scores were strongly associated with self-reported happiness and also significantly associated with personal success, professional success, and general level of stress. Data were collected between 2007 and 2022, but we found no effect for time. The study supports the value of stress-management training, and it also suggests that moderate levels of stress may not be as beneficial as previously thought.

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Introduction.

The main objectives of the present study were to (a) introduce and evaluate a new instrument—the Epstein Stress Management Inventory for individuals (ESMI-i)—for assessing four cognitive-behavioral competencies that have been shown in empirical studies to be associated with effective stress management, (b) analyze data from a large, international group of English-speaking people who completed the new questionnaire online, (c) compare the relative value of the four competencies, and (d) analyze the data from a demographic perspective. By using a competencies approach, we are providing both the public and practitioners with a practical tool for measuring and potentially improving practices associated with effective stress management.

The ESMI-i joins a large number of test instruments and tools that have been developed since the 1950s to help people deal with various kinds of mental health challenges. It is also fairly unique in some respects. For one thing, it was designed for the general population, rather than for a particular group, and it was designed to measure broad competency areas rather than skills that might be helpful mainly in specific contexts. Similar inventories have been designed, for example, to assist individuals who regularly face stress in their work environments 1 , 2 , 3 . Other inventories have been designed to measure people’s coping skills in response to specific stressors 4 , 5 .

The ESMI-i is also available online, non-commercial, and free of charge, maintained by a nonprofit organization. Validated instruments measuring the “level” of stress people feel, such as the Holmes and Rahe Stress Scale 6 and the Perceived Stress Scale 7 , are currently available online, and so are numerous non-validated tests of this sort, accessible at websites such as OkCupid.com. Validated inventories that assess “coping styles” also exist, such as the Multidimensional Coping Inventory 8 . These instruments differ from the ESMI-i in that they are designed to classify people, while we are careful to avoid labeling those who complete the ESMI-i; we will explore this matter later in more detail.

Other validated inventories measure cognitive-behavioral skills and overlap to some extent with the ESMI-i. However, most of these instruments were developed with cognitive-behavioral therapy (CBT) in mind and thus may not be ideal measures of stress-management competencies per se. In addition, many of these inventories, such as the Cognitive-Behavioral Therapy Skills Questionnaire 9 , the Skills of Cognitive Therapy measure 10 , and the CBT Skills Checklist 11 , include items that sometimes conflate skills with reductions in symptoms of depression 12 .

Validated inventories that measure stress-management or coping skills exist, such as the Proactive Coping Inventory 13 , 14 , Chronic Pain Coping Inventory 15 , 16 , 17 , 18 , 19 , the Coping Inventory for Stressful Situations 20 , 21 , 22 , the COPE Inventory 23 , 24 , 25 , the Coping with Stress Scale 26 , the Coping Intelligence Questionnaire 27 , the Dispositional Resilience Scale 28 , 29 , the Stress Mindset Scale 30 , 31 , 32 , and the Performance of Cognitive Therapy Strategies measure 33 . However, they are either not available online or, in some cases, they can only be administered by licensed professionals or trained observers 12 . Because many people are now relying on the internet as a major resource for self-evaluation 34 , 35 , 36 , 37 , we believe that it is important to make validated tests widely available online. Self-help books that teach similar stress-management techniques are available to members of the general public, such as Mind Over Mood 38 ; however, such books are not free, and they take much longer to read than it takes to complete an online inventory.

By stress, we are referring to internal, usually unpleasant physiological and psychological states that are often induced by perceived environmental threats or environmental demands, which are sometimes called “stressors” 39 , 40 , 41 , 42 , 43 . Actual environmental threats do not necessarily produce stress reactions, and the same stressor can cause different stress reactions in different people—or even no stress reaction at all 40 , 41 .

We are not concerned in the present paper with the definitional ambiguities in the terms “stress” and “stressor.” Rather, we are focusing on stress-management practices—thoughts and behaviors that reduce stress—and we are especially interested in practices of this sort that can be both measured and trained by coaches, therapists, or counselors. The instrument we developed focuses on four classes of such behaviors; we define each class of behaviors to be a stress-management competency. The term “coping skills” is sometimes used to describe practices of this sort 44 . For purposes of the present discussion, we will avoid using that term, as well as the related term “coping strategies,” 45 , because we view these terms as referring mainly to reactive practices. In the present study, we will be measuring both reactive and proactive competencies, and we will use our data to compare the relative value of each type.

We believe that it is important to identify and measure stress-management competencies—especially those that can be trained—because of their enormous practical value. Stress-management competencies not only reduce levels of reported stress but have also been associated with increased functioning and well-being, as well as with improvements in mental, emotional, and physical health 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 . Unmanaged stress is costly, both in personal and economic terms 60 , 65 , 66 , 67 , 68 , 69 , 70 . Fortunately, stress management can be trained, and benefits of such training have been demonstrated 46 , 71 , 72 , 73 , 74 , 75 , 76 , 77 . Levels of stress can also be measured 78 , 79 , 80 , 81 , 82 , 83 , and so can stress-management proficiency 17 , 18 , 21 , 24 , 27 , 28 , 30 , 84 , 85 , 86 , 87 .

The nature and value of a competencies approach

Many, if not most, test instruments used by psychologists are based on theories, and those theories are often about hypothetical constructs such as intelligence or personality traits. The methodology for developing and evaluating such instruments is quite advanced. Factor and item analyses are often employed, for example, to remove items that do not improve the statistical validity of the construct measures.

The ESMI-i is not a theory-based questionnaire, and it does not introduce or attempt to measure constructs. The ESMI-i is a competency test, developed in the spirit of a testing approach strongly advocated by David McClelland, notably in a seminal paper published in The American Psychologist in 1973 88 ; other experts have also been strong advocates of this approach over the years 89 , 90 . A competencies approach to understanding and improving human performance is widely used in multiple fields and arenas—by the military 91 , in healthcare 92 , in business 93 , 94 , in education 95 , and in other areas in which human performance is important 96 , 97 , 98 , 99 , 100 , 101 .

As McClelland and others have pointed out, a competencies approach to understanding human functioning has some practical advantages over more traditional psychological approaches. Before he and his colleagues applied this approach to the study of leadership, for example, both businesses and armies had long been searching for “natural born leaders,” and such people exist, of course 102 . But leadership, along with many other areas of human functioning—even intelligence 88 —can be broken down into a number of skill areas that not only can be observed and measured; they can often be trained. Those skill areas, such as the set of skills one needs to drive a car, are not hypothetical, and they are also not constructs. They are sets of behaviors, many of which are observable. Tests that measure traits or constructs often leave people with labels, such as the trait measures yielded by the ubiquitous Minnesota Multiphasic Personality Inventory, and labels can be both demoralizing 103 , 104 and self-fulfilling 105 , 106 , 107 , 108 . In contrast, competency scores simply tell people where they stand at the moment; they are often used in combination with training programs that employ questionnaires to measure post-training improvements.

Development of the ESMI-i questionnaire

The ESMI-i measures “competencies,” a term that is typically defined as “the knowledge, skills, abilities, and behaviors that contribute to individual and organizational performance” 109 —specifically those competencies that are mainly cognitive or behavioral in nature and that might help people to reduce, eliminate, or avoid stress. Beginning in 2001, the first author of this paper, with the help of his students, set about searching the psychology literature to find peer-reviewed papers that identified skills, behavior, or knowledge that were associated with successful stress management, the goal being to use these papers to develop a test instrument that could measure the strength of such competencies. We were especially interested in competencies of the sort that therapists or counselors might be likely to teach—those, as opposed to stress-management techniques that might be taught by medical personnel, nutritionists, or other experts.

Over time, we developed a questionnaire that measured four relatively distinct cognitive-behavioral competencies: Manages or Reduces Sources of Stress, Manages Thoughts, Plans and Prevents, and Practices Relaxation Techniques. Table 1 shows the competencies, definitions, the scored items for each competency, and a list of relevant references. Below are examples of how studies published between 2001 and 2007 were employed to develop the four competency categories and to compose a total of 24 scored items. Table 1 also includes relevant references found after the questionnaire was posted online in May of 2007.

When possible, we tried to create items that “pinpoint” specific behaviors. Items that pinpoint behavior are good predictors of actual behavior 94 , 110 , 111 , 112 . So instead of saying, “I’m great at making people laugh,” we say, “I often try to use humor to diffuse tension.” That wording tells us about behavior and also, to some extent, about the frequency of that behavior. We were not able to do this for every item, but we used pinpointing as a standard for item composition.

Competency 1: manages or reduces sources of stress

In a year-long study with 100 adult residents of the Alameda County area of California, Folkman and Lazarus 113 interviewed participants once every four weeks to determine what strategies they employed to help them cope with the stress they experienced as a result of activities of daily living. The researchers were guided by Richard Lazarus’ cognitive-phenomenological approach to analyzing psychological stress 114 . One of two types of coping strategies analyzed in the 1980 study was labeled “problem-focused” (p. 223), and it suggested, along with other studies (see below), that one robust category of stress management was managing sources of stress. In defining this strategy, the authors spoke of “management or alteration of the person-environment relationship that is the source of stress” (p. 223), “cognitive problem-solving efforts and behavioral strategies for altering or managing the source of the problem” (p. 224), and other actions that reduced or eliminated sources of stress.

We also used language from the Folkman and Lazarus 113 study to help us construct one of our questionnaire items. In elaborating on problem-focused coping strategies, the authors stated that these strategies “include seeking information, trying to get help, inhibiting action, and taking direct action” (p. 229). Item 2 on the ESMI-i is, “I’m comfortable seeking help from other people” (Table 1 ).

Problem-focused coping strategies were also analyzed in a 2006 study with 67 families of young children with disabilities. Stoneman and Gavidia-Payne 115 found that marital harmony was higher when fathers in these families employed problem-focused strategies to overcome challenges, thus reducing sources of stress.

Competency 2: manages thoughts

Thought management—often taught by counselors and therapists as part of therapeutic treatment—can be a powerful means for reducing or eliminating stress. “Reframing,” a technique most often associated with Albert Ellis 116 and a main component of rational emotive behavior therapy 117 , is just one example of a thought-management technique. Others include cognitive restructuring 118 , 119 , 120 , 121 , cognitive reappraisal 47 , 54 , 64 , cognitive redefinition 26 , and cognitive defusion 122 , 123 , 124 . Most mindfulness techniques, such as acceptance and commitment therapy, incorporate methods for managing thoughts 125 . Cognitive-behavioral therapy, developed by Aaron Beck in the 1960s 126 , 127 , also emphasizes techniques aimed at reducing automatic thoughts and cognitive distortions 128 . Generally speaking, people have little control over the undesirable things that happen to them, but, in theory, they have—or could be trained to have—complete control over how they interpret such events; hence, the logic of using thoughts to manage stress.

Murphy 120 conducted a meta-analysis of 64 studies that examined ways in which people managed workplace stress. The most common methods used to manage stress in work settings were meditation, muscle relaxation, cognitive-behavioral skills, and biofeedback. Cognitive-behavioral techniques, which included “thought restructuring” and other methods, proved to be especially effective in reducing what the author called “psychologic” stress (as opposed to “physiologic” stress). The Murphy study proved to be especially helpful in generating possible items for inclusion in the ESMI-i. Items based in part or in full on content from the Murphy study include, “I often reinterpret events in order to lower my stress” (item 5), “Negative events can always be reinterpreted so that they seem more positive” (item 9), and “I regularly examine and try to correct any irrational beliefs I might have” (item 10).

A comprehensive literature review by Giga et al. 129 also proved helpful in developing our Manages Thoughts competency category (as well as the competency that follows—see below). Focusing again on the work environment, the authors found that thought restructuring, reframing, and similar techniques were helpful in managing stress. Language from the Giga et al. 129 study was helpful in developing items 5 and 9 (see above paragraph).

An experimental study by Keogh et al. 119 also helped us develop this competency category. 209 students in the UK were randomly assigned to either a cognitive-behaviorally oriented treatment group or a no-treatment control group. Stress reduction was significantly higher in the treatment group, with students being taught, among other things, to replace irrational beliefs (such as “I am bad at taking tests” [p. 342]) with more rational ones (such as “I can take tests, if I prepare appropriately” [p. 342]). This study helped us to develop items such as the reverse-scored item, “My thinking is as clear and as rational as it can possibly be” (item 11), as well as item 10 (noted above).

Competency 3: plans and prevents

Once again, the Giga et al. 129 literature review was helpful in developing this category. It spoke specifically about the value of “plan[ning] to prevent and manage stress,” and it helped us develop two items: “I have a clear picture of how I’d like my life to proceed” (item 15) and “I try to fight stress before it starts” (item 17).

McWilliams et al. 22 studied a group of 298 outpatients with major depressive disorder, having them complete multiple questionnaires, such as the Coping Inventory for Stressful Situations 21 . They concluded that planning and scheduling, among other strategies, were associated with lower levels of psychological stress. Content from this article helped us compose two ESMI-i items: “I keep an up-to-date list of things I’m supposed to do” (item 7), and “I spend a few moments each morning planning my day” (item 22). In addition, the Folkman et al. study 130 , mentioned earlier, helped us compose item 18: “I try to avoid destructive ways of dealing with stress.”

Competency 4: practices relaxation techniques

The value of practicing various relaxation techniques in managing stress began to be established even before the concept of “biological stress” was introduced in the early 1900s and before Hans Selye’s breakthrough research in the 1930s on the relationship between the stress response and disease 131 . Edmund Jacobson’s classic book, Progressive Relaxation 132 —based on techniques he had been developing and studying since 1915—asserted that progressive muscle relaxation exercises had multiple benefits, including improvements in memory, attention, thinking, and emotions 133 .

Later sources have repeatedly confirmed the value that various relaxation techniques have in stress management. For example, Pawlow and Jones 134 conducted a controlled experiment on progressive muscle relaxation with 55 undergraduate students, concluding that the experimental group benefitted in multiple ways from the relaxation exercises. Among other benefits, the exercises “produced significantly reduced self ratings of perceived stress and state anxiety [and] significantly increased ratings of relaxation from immediately before to immediately after the training” (p. 381). In contrast, quiet sitting (practiced by the control group) produced no such benefits. The Pawlow and Jones 134 study helped us to compose the questionnaire item, “I regularly tense and relax my muscles as a way of fighting stress” (item 13).

Smith et al. 135 compared progressive muscle relaxation to yoga with 131 adults from South Australia in a randomized study, concluding that yoga (which included breathing exercises and postures) was as effective as muscle relaxation in producing positive outcomes, including reductions in stress and anxiety. This study helped us develop three ESMI-i items: “I frequently use special breathing techniques to help me relax” (item 1), “I regularly tense and relax my muscles as a way of fighting stress” (item 13), and the reverse-scored item, “Breathing is a very hard thing to control” (item 20).

Dummy items and internal consistency score (ICS)

As is common in competency questionnaires designed by the first author 96 , 98 , 99 , 101 , the ESMI-i includes one dummy item for each of the four competencies assessed. Each dummy item rephrases a corresponding scored item. The purpose of having these dummy pairs is to be able, at the end of each user session, to quickly compute how closely the answers within each pair match each other. The match is computed using a modified version of Cohen’s kappa coefficient, a standard measure of inter-observer agreement 136 (see Supplementary Text S1 to compare the two formulas). We call this calculation our “internal consistency score” (ICS). In theory, if the ICS is low, we can ask a user to retake the questionnaire. In the present study, no users were asked to retake the questionnaire based on a low ICS. Instead, we elected to examine this issue as part of the data cleaning process (see below).

Participants

Before data cleaning, our dataset included 21,398 people who had completed the ESMI-i between May 3, 2007, and June 1, 2022. If someone completed the questionnaire more than once on the same day, we preserved only the first instance in which more than half the questionnaire items were answered. We also removed all cases in which self-reported English fluency was below 6 (on a scale from 1 to 10, where 10 indicated the highest level of fluency). After cleaning, 18,895 participants remained in the dataset.

The self-reported demographic characteristics of the participants were as follows (for details, see Table 2 ): Age ranged from 12 to 83 ( M  = 30.4 [ SD  = 14.1]). Because the ESMI-i has a Flesch–Kincaid reading level of 5.8, and because most 11-year-old children in the U.S. have completed the fifth grade, we received Institutional Review Board (IRB) approval for participants age 11 and over; however, our youngest participants (after cases were removed because of low self-reported fluency levels) were 12 ( n  = 19).

After cleaning by English fluency and duplicate cases, we had no need to remove cases because of low ICSs. We made this determination based on the value of Cronbach’s alpha for groups of people with differing ICSs. The group of people with ICSs between 0.4 and 0.5 (or, more precisely, 0.4 < ICS ≤ 0.5) had an alpha of 0.71, and with each successive group of people with higher ICSs (0.5–0.6, 0.6–0.7, 0.7–0.8, 0.8–0.9, 0.9–1.0), alpha increased (range 0.71 to 0.88). Because alphas greater than 0.7 are normally considered acceptable in test development 137 , 138 , 139 , using this criterion, we could not justify removing cases based on low ICSs. A small number of people (40 in total, 0.21% of the total N ) had ICSs less than 0.4, but that was too few people for us to compute an alpha. Because we had no objective reason to eliminate these people from our study, we took the conservative course of action and let them remain (see Supplementary Table S1 and Supplementary Figure S1 for details).

Overall, 12,242 (64.8%) of our participants identified themselves as female, 6,565 (34.7%) as male, and 88 (0.5%) as other. Racial and ethnic background was as follows: 128 (0.7%) of our participants identified themselves as American Indian, 2,379 (12.6%) as Asian, 1,165 (6.2%) as Black, 1,107 (5.9%) as Hispanic, 13,097 (69.3%) as White, and 869 (4.6%) as Other; 150 individuals (0.8%) did not answer this question. Overall, 29.9% of the individuals in the sample identified themselves as non-White.

Regarding level of education completed: 2,115 (11.2%) reported not having a high school degree; 5,857 (31.0%) reported completing high school; 1,844 (9.8%) reported having an associates degree; 5,361 (28.4%) reported having completed college; 2,952 (15.6%) reported having a master’s degree; 644 (3.4%) reported having a doctoral degree; and 122 (0.6%) did not answer the question. Regarding sexual orientation: 16,713 (88.5%) identified themselves as straight; 558 (3.0%) as gay or lesbian; 1,211 (6.4%) as bisexual; 25 (0.1%) as other, and 388 (2.1%) did not answer this question. Regarding country of origin: 12,279 (65.0%) were from the United States and Canada; 5,712 (30.2%) were from 123 other countries; and 904 (4.8%) did not answer this question.

Study design

The present investigation utilized a “concurrent study design” that used criterion validity evidence, consistent with guidelines in the most recent edition of Standards for Educational and Psychological Testing 140 , prepared jointly by the American Educational Research Association, the American Psychological Association, and the National Council on Measurement in Education. Specifically, we sought to measure the strength of the relationships between our questionnaire scores and the scores on our self-reported criterion questions. This design is called “concurrent” because we obtained questionnaire scores and criterion measures at the same time, a strategy that avoids possible temporal confounds. Results from studies employing this design are considered especially robust when the pattern of relationships between questionnaire scores and criterion measures proves to be consistent across different demographic groups.

As noted above, the questionnaire employed in the study measured four cognitive-behavioral competencies. A total score was calculated, with higher scores indicating greater stress-management competence. Separate scores were also calculated for each of the competencies. We report all questionnaire scores as a percentage of possible maximum scores rather than as raw scores. We also calculated scores for each of the four criterion questions (see Procedure below).

Participants were first presented with brief instructions informing them, for example, that there are no right or wrong answers to the items on the questionnaire. They were then asked some basic demographic questions, following which they were asked four criterion questions regarding desirable outcomes that are sometimes associated with successful stress management, namely: How happy and fulfilled are you? How much success have you had in your personal life? How much success have you had in your professional life? How stressed do you generally feel? Answers were given on a 10-point Likert scale from Low to High (see Supplementary Figures S2-S4 for the demographic questions and the questionnaire items). After completing the 28-item questionnaire (24 items were scored), participants were given their overall questionnaire score as well as scores on the four subscales and detailed explanations about the nature of each competency (see Supplementary Fig. S5 for a screenshot of the results page). Primary access to the questionnaire was at the URL https://MyStressManagementSkills.com . Over time, links to the questionnaire appeared elsewhere on the internet, a process over which we had no control. We also had no control over the demographic characteristics of the participants (see Discussion).

Ethics statement

The federally registered Institutional Review Board (IRB) of the sponsoring institution (American Institute for Behavioral Research and Technology) approved this study with exempt status and a waiver of the requirement for informed consent under U.S. Department of Health and Human Services regulations (45 CFR 46.116(d), 45 CFR 46.117(c)(2), and 45 CFR 46.111) because (a) the anonymity of participants was preserved and (b) the risk to participants was minimal. The IRB is registered with the Office for Human Research Protections under number IRB00009303, and the Federalwide Assurance number for the IRB is FWA00021545.

Regressions and factor analysis

Linear regression was used to determine which competencies were most strongly associated with self-reported levels of happiness, personal success, professional success, and general level of stress. Notably, the Plans and Prevents competency proved to be the best predictor of all four criterion variables (Tables 3 and 4 ). Please note that by using the language of prediction, we do not mean to imply causation.

An exploratory principal components factor analysis for the 24 scored items in the questionnaire yielded a Kaiser–Meyer–Olkin sampling adequacy of 0.89, which is well above the recommended cutoff of 0.6, as well as a highly significant Bartlett’s Test of Sphericity ( p  < 0.001). The factor analysis yielded four components that overlap our original four competencies and that can reasonably be described as: (1) Plans Ahead, (2) Practices Relaxation Techniques, (3) Regulates and Manages Stressors, and (4) Recognizes Weaknesses (Table 5 ). The results of the factor analysis were not used to revise the original competencies or items, because these competencies are practical skillsets, not hypothetical constructs. We did not conduct a confirmatory factor analysis for the same reason.

Reliability and validity evidence

The questionnaire had moderate but acceptable internal-consistency reliability (Cronbach’s alpha = 0.79; Guttman split-half = 0.70) 137 , 138 , 139 . Because the study was conducted over the internet and because we could not collect contact information for our participants (in order to preserve their anonymity), test–retest reliability could not be assessed. We also did not develop an alternate form of the questionnaire, so alternate-form reliability could not be estimated.

Internal consistency scores for the four competencies varied considerably: Manages or Reduces Sources of Stress: Cronbach’s alpha = 0.60; Guttman split-half = 0.64. Manages Thoughts: Cronbach’s alpha = 0.34; Guttman split-half = 0.37. Plans and Prevents: Cronbach’s alpha = 0.64; Guttman split-half = 0.68. Practices Relaxation Techniques: Cronbach’s alpha = 0.65; Guttman split-half = 0.61.

Regarding validity evidence, total scores were correlated with scores on our criterion questions, as we had predicted: Total scores were positively correlated with participants’ self-reported level of happiness (Spearman’s ρ  = 0.45, p  < 0.001), personal success ( ρ  = 0.35, p  < 0.001), and professional success ( ρ  = 0.32, p  < 0.001), and negatively correlated with participants’ general level of stress ( ρ  = -0.33, p  < 0.001). Because this was an internet-based study in which the anonymity of participants was protected, we could not assess validity by comparing scores on our questionnaire to scores on comparable questionnaires (see Discussion). (Because scores on the ESMI-i lie on an ordinal scale, nonparametric statistical tests such as Spearman’s ρ , the Mann–Whitney U , and the Kruskal–Wallis H are used throughout this study, except in our regression analyses. Nonparametric regressions are generally used only when extreme values might distort the results 141 ; outliers are unlikely with an instrument like the ESMI-i in which scores are constrained.).

Although not specifically predicted, the validity of the measuring instrument is also suggested by the fact that the mean total score for those who reported having had stress-management training was significantly higher than the mean total score for those who did not ( M Yes  = 58.8 [13.2], M No  = 52.6 [13.3], U  = 19,888,845, p  < 0.001, r  = 0.18). In addition, overall questionnaire scores were positively correlated with the number of hours of stress-management training participants reported having ( ρ  = 0.24, p  < 0.001).

Gender, race, and other demographic effects

The overall mean total score was 53.8 [13.5] and subscale means were as follows: Manages or Reduces Sources of Stress ( M  = 61.3 [18.4]), Manages Thoughts ( M  = 57.2 [14.7]), Plans and Prevents ( M  = 54.9 [19.7]), and Practices Relaxation Techniques ( M  = 41.9 [20.5]).

We found a significant but not necessarily substantial effect for education level ( M None  = 48.8 [13.1], M Highschool  = 52.3 [12.9], M Associates  = 54.9 [13.1], M Bachelors  = 55.1 [13.6], M Masters  = 57.1 [13.2], M Doctorate  = 56.3 [15.1], H  = 604.3, p  < 0.001, E 2 R  = 0.03), and scores were higher for participants who reported having been married ( M Yes  = 55.0 [13.6], M No  = 53.0 [13.3], U  = 37,980,528.5, p  < 0.001, r  = 0.07) and also, surprisingly, for participants who reported having been divorced ( M Yes  = 55.4 [14.0], M No  = 53.6 [13.4], U  = 20,128,336.5, p  < 0.001, r  = 0.05).

We found a significant but not necessarily substantial effect for gender ( M female  = 53.7 [13.3], M male  = 54.1 [13.7], M other  = 44.1 [15.4] , H  = 42.7, p  < 0.001, E 2 R  = 0.00) and no significant male/female difference ( U  = 39,631,749.5, p  = 0.12, r  = 0.01). Participants ages 18 and older scored significantly higher than minors ( M 12-17  = 49.3 [12.9], M 18-83  = 54.8 [13.4], U  = 19,539,532, p  < 0.001, r  = 0.15). (Note that because this is a large-n study, statistical significance is not necessarily a good indicator of the importance of mean differences. For this reason, we also have included two different measures of effect size: r , where we are comparing two means, and epsilon-squared, where we are comparing three or more means 142 . We also found a significant but not necessarily substantial effect for ethnicity, with respondents of Asian descent outscoring all other ethnicities ( M AmericanIndian  = 54.5 [14.8], M Asian  = 58.3 [13.4], M Black  = 54.6 [14.0], M Hispanic  = 53.0 [13.8], M White  = 54.0 [14.1], M Other  = 53.0 [13.2], H  = 330.1, p  < 0.001, E 2 R  = 0.02 ; Asian vs. non-Asian: M Asian  = 58.3 [13.4], M NonAsian  = 53.2 [13.4], U  = 15,133,970.5, p  < 0.001, r  = 0.13), a finding that is consistent with other research 143 , 144 , 145 .

We also found a significant but not necessarily substantial effect for sexual orientation, with self-labeled straights outscoring self-labeled gays, lesbians, and bisexuals ( M Bisexual  = 49.0 [14.0], M Gay/Lesbian  = 50.0 [14.2], M Straight  = 54.3 [13.3], M Other  = 52.2 [17.1], H  = 203.0, p  < 0.001, E 2 R  = 0.01; straight vs. non-straight: M Straight  = 54.3 [13.3], M NonStraight  = 49.4 [14.1], U  = 11,946,435, p  < 0.001, r  = 0.10). Participants outside the U.S. and Canada scored higher than participants from the U.S. and Canada ( M US/Canada  = 53.6 [13.2], M Other  = 54.5 [14.0], U  = 33,681,800, p  < 0.001, r  = 0.03), possibly because of the higher scores of Asian participants in the study. We did not have enough participants in individual countries outside the U.S. and Canada for us to conduct, with sufficient statistical power, a country-by-country analysis; a larger sample might allow us to conduct such an analysis in future years. Age proved to be a small but significant predictor of questionnaire scores ( ρ  = 0.12, p  < 0.001).

Apparent value of low stress

Our results suggest that high stress is associated with low levels of happiness, personal success, and professional success; that low stress is associated with high levels of these outcomes; and that the benefits sometimes associated with a moderate level of stress might not be as beneficial as previously thought (Figs.  1 and 2 ; see Discussion). Although the spikes toward the center of the curves in Fig.  1 could be interpreted as indicating possible benefits of moderate stress, when one looks closely at the happiness, personal success, and professional success ratings reported by people who experience different levels of stress in their lives, it seems evident that low stress is more consistently associated with desirable outcomes (Fig.  2 ).

Relationship between self-reported levels of stress and self-reported levels of happiness, personal success, and professional success. Although higher stress is generally associated with poorer outcomes (note the overall downward slopes of the curves), the upward spikes in the center of the graph are sometimes mistakenly interpreted to mean that moderate stress is beneficial. Vertical bars show 95% confidence intervals.

Histograms showing distributions of self-reported levels of happiness, personal success, and professional success, separated into three categories of level of stress. Although one can find high levels of happiness, personal success, and professional success in the bottom two rows of graphs, the patterns of scores are more nearly optimal in the top row, which shows data only for people reporting their overall level of stress as very low (1 on a scale from 1 to 10).

Year-by-year analysis

Because our data were collected over a period of more than 14 years, we asked whether any trends were evident in scores, as well as in demographic characteristics of the sample. Although statistically significant changes were evident in both scores and demographic characteristics over the course of the study (Table 6 ), we did not find a linear trend in the mean total scores ( p  = 0.813, r 2  = 0.005, β  = 0.07, t  = 0.24).

Discussion and limitations

The present study sheds light on various aspects of people’s ability to manage stress. One of its greatest limitations—that the data were collected over the internet—is also a strength. On the downside, internet sampling gives one no control over demographics, and all participants are self-selected. Our sample presumably consisted of people who were concerned about stress or how they managed it. This could mean, among other things, that our mean level of self-reported stress (6.5 out of 10) is higher than that of the general population and, perhaps, that the stress-management proficiency level we found ( M  = 53.8) is lower than normal. A 2013 report by the American Psychological Association 65 states that the average stress level for Americans is 4.9 out of 10, 1.6 points below the mean we found. We might also be attracting people with abnormally low levels of happiness or success.

On the upside, the internet allows researchers to look at a large, diverse, international sample, which almost certainly yields more valid findings than the proverbial pool of second-year college students 146 , 147 , 148 . There is also accumulating evidence that people are more honest when answering personal questions through anonymous internet surveys than perhaps through any other means 149 , 150 , 151 , 152 , 153 ; a recent study by Robertson et al. 154 suggests that anonymous internet surveys yield more valid responses than sixteen other common survey techniques. Surveys yield especially valid responses when people are completing them voluntarily and they know that the results will not be used by supervisors or other authority Figures. 155 , 156 . For these reasons, we conjecture that our participants were probably honest in their responding. We also have no a priori reason to believe that the relationships we have found among variables—for example, the negative correlation between total questionnaire scores and self-reported levels of stress ( ρ  = − 0.33, p  < 0.001)—are invalid.

We also have no reason to doubt the validity of some of the more distinctive demographic findings in the study, particularly where such findings are consistent with those of other research. Especially notable in this study is the relatively high mean score of participants identifying as Asian. Other studies looking more directly at this issue have also found that various Asian groups are better at managing stress than non-Asians, perhaps because of the collectivist nature of many Asian cultures 157 . Tweed et al. 145 found, for example, that East Asian Canadians reported using internal strategies to manage stressful situations more often than European Canadians did. In collectivist cultures, people tend to be more mutually supportive than in individualistic cultures 158 , 159 , and Asian cultures also tend to teach explicit techniques—yoga, meditation, tai chi, and so on—which have been shown to improve well-being and lower stress 71 , 160 , 161 . In many non-Asian cultures, well-being is often sought through self-destructive means (alcoholism, drug abuse, overeating) or, at best, left to chance. Similarly, our findings that self-reported straights outscored self-reported non-straights on the ESMI-i and that self-reported straights reported experiencing less stress than self-reported non-straights are consistent with the findings of other researchers 162 , 163 , 164 , 165 .

Our study found no significant difference between scores for males and scores for females. Researchers disagree about gender differences in both stress-management proficiency and perceived stress levels. Some studies suggest that women are more likely to utilize emotion-focused coping in response to stressors while men more often use problem-focused coping 166 . The 2013 report on stress published by the American Psychological Association stated that women report higher stress levels than men 65 . However, and consistent with our results, some studies have found that gender differences in coping styles are not apparent when confounding factors such as socioeconomic status and race are controlled for 167 .

The two largest demographic effects we found should be studied in further detail in future studies. Self-labeled straights outscored non-straights by 4.9 points ( r  = 0.10), and adults outscored minors (ages 12–17) by 5.5 points ( r  = 0.15). Considerable research has examined the emotional problems often experienced by non-straights (brought about, most likely, by entrenched heteronormativity in most cultures 168 , 169 , 170 ), but why straights should score higher on a test of stress-management competencies is unclear. The age difference seems less mysterious. Competencies take time to learn, after all 171 , but it would be interesting to look at this learning process in more detail, especially over the teen years.

A second notable limitation of the present study is that it is correlational in design. In follow-ups to this study, one could, by employing either between-subjects or within-subjects experimental designs, assess the possible causal impact of each of the four competencies we have examined in this report.

Perhaps the clearest and, in some sense, the most surprising finding in this study is that proactive stress-management methods appear to be more helpful than reactive ones. All four of our criterion variables were best predicted by the Plans and Prevents competency, of which all questionnaire items describe proactive methods of fighting stress—in other words, ways of trying to ensure that stressful situations never arise (Tables 3 and 4 ). A planning competency also emerged in our factor analysis (Table 5 ). Unfortunately, our respondents scored relatively poorly on Plans and Prevents ( M  = 54.9), which ranked third on actual competency scores. We note that our findings about proactive methods do not necessarily show that such methods are more beneficial than reactive ones; it might simply be the case that people with lower stress levels rely less on reactive coping strategies than on proactive ones. Again, questions of cause and effect can only be answered with experimental research.

Our study also yielded intriguing findings regarding the supposed value of moderate stress. Ever since the formation of the Yerkes-Dodson law in the early 1900s, researchers have suggested that moderate levels of stress (at least for stressors of certain types) are beneficial 172 , 173 , 174 , 175 , 176 . Our study suggests another possibility—namely, that the bulge that often appears in the center of performance or other curves where the stress level is moderate is a statistical anomaly. As we noted earlier, this seems evident when we examine the relationship between participants’ self-reported levels of stress and their self-reported levels of happiness, personal success, and professional success (Fig.  1 ), as well as when we look closely at the distributions of self-reported levels of happiness, personal success, and professional success when separated into low, medium, and high of levels of self-reported stress (Fig.  2 ).

The strong relationship ( ρ  = 0.45, p  < 0.001, ρ 2  = 0.20) we found between total questionnaire scores and self-reported happiness is also notable, suggesting the importance of stress management in having a happy life 177 (although, once again, we remind the reader that this is a correlational study). Unfortunately, our results suggest that people are generally poor at stress management; the mean percentage score on the questionnaire was 53.8%, with Practices Relaxation Skills having a mean score of only 41.9%. Our study confirms the need to educate and train people in how to manage stress, although, as noted above, our questionnaire scores might be lower than the average scores one would find in the general population. Fortunately, although the present study employed a concurrent design 140 , not an experimental one, our results are consistent with the view that stress-management training has value; as noted earlier, study participants who had had such training scored significantly higher than participants who had not, and questionnaire scores were positively correlated with the number of training hours reported.

We have already mentioned several ways in which our analysis was constrained because we collected our data online. These limitations are not trivial, because collecting data on the internet—especially when one is required to protect the anonymity of the participants—means that some of the standard tools used to validate new test instruments cannot be employed. Ideally, one would like to compare test scores to those obtained on previously validated tests. Again, ideally, one would like to be able to measure the stability of test scores by readministering the test to the same cohort after different periods of time have passed. One would also like to use multiple measures to validate the test, such as ratings by peers or clinicians. None of these methods is possible given our current design.

As noted above, future versions of the ESMI-i might include additional competency categories. People can mitigate stress using many tools, such as exercise or changes in diet; the present study focuses on cognitive-behavioral methods that might be taught by therapists and counselors. More detailed demographic analyses might also be conducted, especially with larger samples. The current online version of the ESMI-i (as of July 8, 2024) already includes additional gender and sexual orientation categories. The present study also assumes, implicitly, that stress management is the same in all cultures around the world, which is clearly not the case. Stressors themselves are culturally based 163 , 165 , 178 , 179 , and so are effective techniques of stress management 145 .

Data availability

Anonymized raw data employed in the present study will be made available at Zenodo.com upon acceptance of the manuscript. Anonymized raw data can also be requested by writing to [email protected]. Anonymization was required to comply with the requirement of the sponsoring institution’s Institutional Review Board that the identities of the participants be protected in accordance with HHS Federal Regulation 45 CFR 46.101.(b)(2).

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Acknowledgements

We are grateful to Marco Buenaventura, Philip Cheung, Matea Djokic, Shannon Fox, Allison He, Paul McKinney, Krystie Mei, and Rachel Smith for assistance in various aspects of this research. Funding was provided by the American Institute for Behavioral Research and Technology, which also provided IRB approval (IRB registered with OHRP under number IRB00009303, Federalwide Assurance number FWA00021545). This report is based in part on papers presented at the 91st (2011), 94th (2014), and 101st (2021) annual meetings of the Western Psychological Association. Each successive report analyzed larger datasets. Brief summaries of these talks were posted on the first author’s website.

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Exploring the factors driving AI adoption in production: a systematic literature review and future research agenda

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• Heidi Heimberger   ORCID: orcid.org/0000-0003-3390-0219 1 , 2 ,
• Djerdj Horvat   ORCID: orcid.org/0000-0003-3747-3402 1 &
• Frank Schultmann   ORCID: orcid.org/0000-0001-6405-9763 1  

Our paper analyzes the current state of research on artificial intelligence (AI) adoption from a production perspective. We represent a holistic view on the topic which is necessary to get a first understanding of AI in a production-context and to build a comprehensive view on the different dimensions as well as factors influencing its adoption. We review the scientific literature published between 2010 and May 2024 to analyze the current state of research on AI in production. Following a systematic approach to select relevant studies, our literature review is based on a sample of articles that contribute to production-specific AI adoption. Our results reveal that the topic has been emerging within the last years and that AI adoption research in production is to date still in an early stage. We are able to systematize and explain 35 factors with a significant role for AI adoption in production and classify the results in a framework. Based on the factor analysis, we establish a future research agenda that serves as a basis for future research and addresses open questions. Our paper provides an overview of the current state of the research on the adoption of AI in a production-specific context, which forms a basis for further studies as well as a starting point for a better understanding of the implementation of AI in practice.

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1 Introduction

The technological change resulting from deep digitisation and the increasing use of digital technologies has reached and transformed many sectors [ 1 ]. In manufacturing, the development of a new industrial age, characterized by extensive automation and digitisation of processes [ 2 ], is changing the sector’s ‘technological reality’ [ 3 ] by integrating a wide range of information and communication technologies (such as Industry 4.0-related technologies) into production processes [ 4 ].

Although the evolution of AI traces back to the year 1956 (as part of the Dartmouth Conference) [ 5 ], its development has progressed rapidly, especially since the 2010s [ 6 ]. Driven by improvements, such as the fast and low-cost development of smart hardware, the enhancement of algorithms as well as the capability to manage big data [ 7 ], there is an increasing number of AI applications available for implementation today [ 8 ]. The integration of AI into production processes promises to boost the productivity, efficiency as well as automation of processes [ 9 ], but is currently still in its infancy [ 10 ] and manufacturing firms seem to still be hesitant to adopt AI in a production-context. This appears to be driven by the high complexity of AI combined with the lack of practical knowledge about its implementation in production and several other influencing factors [ 11 , 12 ].

In the literature, many contributions analyze AI from a technological perspective, mainly addressing underlying models, algorithms, and developments of AI tools. Various authors characterise both machine learning and deep learning as key technologies of AI [ 8 , 13 ], which are often applied in combination with other AI technologies, such as natural language recognition. While promising areas for AI application already exist in various domains such as marketing [ 14 ], procurement [ 15 ], supply chain management [ 16 ] or innovation management [ 17 ], the integration of AI into production processes also provides significant performance potentials, particularly in the areas of maintenance [ 18 ], quality control [ 19 ] and production planning and management [ 20 ]. However, AI adoption requires important technological foundations, such as the provision of data and the necessary infrastructure, which must be ensured [ 11 , 12 , 21 ]. Although the state of the art literature provides important insights into possible fields of application of AI in production, the question remains: To what extent are these versatile applications already in use and what is required for their successful adoption?

Besides the technology perspective of AI, a more human-oriented field of discussion is debated in scientific literature [ 22 ]. While new technologies play an essential role in driving business growth in the digital transformation of the production industry, the increasing interaction between humans and intelligent machines (also referred to as ‘augmentation’) creates stress challenges [ 23 ] and impacts work [ 24 ], which thus creates managerial challenges in organizations [ 25 , 26 ]. One of the widely discussed topics in this context is the fear of AI threatening jobs (including production jobs), which was triggered by e.g. a study of Frey, Osborne [ 27 ]. Another issue associated to the fear of machines replacing humans is the lack of acceptance resulting from the mistrust of technologies [ 28 , 29 ]. This can also be linked to the various ethical challenges involved in working with AI [ 22 ]. This perspective, which focuses on the interplay between AI and humans [ 30 ], reveals the tension triggered by AI. Although this is discussed from different angles, the question remains how these aspects influence the adoption of AI in production.

Another thematic stream of current literature can be observed in a series of contributions on the organizational aspects of the technology. In comparison to the two research areas discussed above, the number of publications in this area seems to be smaller. This perspective focuses on issues to implement AI, such as the importance of a profound management structure [ 31 , 32 ], leadership [ 33 ], implications on the organizational culture [ 34 ] as well as the need for digital capabilities and special organizational skills [ 33 ]. Although some studies on the general adoption of AI without a sectoral focus have already been conducted (such as by Chen, Tajdini [ 35 ] or Kinkel, Baumgartner, Cherubini [ 36 ]) and hence, some initial factors influencing the adoption of AI can be derived, the contributions from this perspective are still scarce, are usually not specifically analyzed in the context of production or lack a comprehensive view on the organization in AI adoption.

While non-industry specific AI issues have been researched in recent years, the current literature misses a production-specific analysis of AI adoption, providing an understanding of the possibilities and issues related to integrating AI into the production context. Moreover, the existing literature tells us little about relevant mechanisms and factors underlying the adoption of AI in production processes, which include both technical, human-centered as well as organizational issues. As organizational understanding of AI in a business context is currently still in its early stages, it is difficult to find an aggregate view on the factors that can support companies in implementing AI initiatives in production [ 37 , 38 ]. Addressing this gap, we aim to systematise the current scientific knowledge on AI adoption, with a focus on production. By drawing on a systematic literature review (SLR), we examine existing studies on AI adoption in production and explore the main issues regarding adoption that are covered in the analyzed articles. Building on these findings, we conduct a comprehensive analysis of the existing studies with the aim of systematically investigating the key factors influencing the adoption of AI in production. This systematic approach paves the way for the formulation of a future research agenda.

Our SLR addresses three research questions (RQs). RQ1: What are the statistical characteristics of existing research on AI adoption in production? To answer this RQ, we conduct descriptive statistics of the analyzed studies and provide information on time trends, methods used in the research, and country specifications. RQ2: What factors influence the adoption of AI in production? RQ2 specifies the adoption factors and forms the core component of our analysis. By adoption factors, we mean the factors that influence the use of AI in production (both positively and negatively) and that must therefore be analyzed and taken into account. RQ3: What research topics are of importance to advance the research field of AI adoption in production? We address this RQ by using the analyzed literature as well as the key factors of AI adoption as a starting point to derive RQs that are not addressed and thus provide an outlook on the topic.

2 Methodology

In order to create a sound information base for both policy makers and practitioners on the topic of AI adoption in production, this paper follows the systematic approach of a SLR. For many fields, including management research, a SLR is an important tool to capture the diversity of existing knowledge on a specific topic for a scientific investigation [ 39 ]. The investigator often pursues multiple goals, such as capturing and assessing the existing environment and advancing the existing body of knowledge with a proprietary RQ [ 39 ] or identifying key research topics [ 40 ].

Our SLR aims to select, analyze, and synthesize findings from the existing literature on AI adoption in production over the past 24 years. In order to identify relevant data for our literature synthesis, we follow the systematic approach of the Preferred Reporting Items for Systematic reviews (PRISMA) [ 41 ]. In evaluating the findings, we draw on a mixed-methods approach, combining some quantitative analyses, especially on the descriptive aspects of the selected publications, as well as qualitative analyses aimed at evaluating and comparing the contents of the papers. Figure  1 graphically summarizes the methodological approach that guides the content of the following sub-chapters.

Methodical procedure of our SLR following PRISMA [ 41 ]

2.1 Data identification

Following the development of the specific RQs, we searched for suitable publications. To locate relevant studies, we chose to conduct a publication analysis in the databases Scopus, Web of Science and ScienceDirect as these databases primarily contain international scientific articles and provide a broad overview of the interdisciplinary research field and its findings. To align the search with the RQs [ 42 ], we applied predefined key words to search the titles, abstracts, and keywords of Scopus, Web of Science and ScienceDirect articles. Our research team conducted several pre-tests to determine the final search commands for which the test results were on target and increased the efficiency of the search [ 42 ]. Using the combination of Boolean operators, we covered the three topics of AI, production, and adoption by searching combinations of ‘Artificial Intelligence’ AND ‘production or manufacturing’ AND ‘adopt*’ in the three scientific databases. Although ‘manufacturing’ tends to stand for the whole sector and ‘production’ refers to the process, the two terms are often used to describe the same context. We also follow the view of Burbidge, Falster, Riis, Svendsen [ 43 ] and use the terms synonymously in this paper and therefore also include both terms as keywords in the study location as well as in the analysis.

AI research has been credited with a resurgence since 2010 [ 6 ], which is the reason for our choice of time horizon. Due to the increase in publications within the last years, we selected articles published online from 2010 to May 8, 2024 for our analysis. As document types, we included conference papers, articles, reviews, book chapters, conference reviews as well as books, focusing exclusively on contributions in English in the final publication stage. The result of the study location is a list of 3,833 documents whose titles, abstracts, and keywords meet the search criteria and are therefore included in the next step of the analysis.

2.2 Data analysis

For these 3,833 documents, we then conducted an abstract analysis, ‘us[ing] a set of explicit selection criteria to assess the relevance of each study found to see if it actually does address the research question’ [ 42 ]. For this step, we again conducted double-blind screenings (including a minimum of two reviewers) as pilot searches so that all reviewers have the same understanding of the decision rules and make equal decisions regarding their inclusion for further analysis.

To ensure the paper’s focus on all three topics regarded in our research (AI, production, and adoption), we followed clearly defined rules of inclusion and exclusion that all reviewers had to follow in the review process. As a first requirement for inclusion, AI must be the technology in focus that is analysed in the publication. If AI was only mentioned and not further specified, we excluded the publication. With a second requirement, we checked the papers for the context of analysis, which in our case must be production. If the core focus is beyond production, the publication was also excluded from further analysis. The third prerequisite for further consideration of the publication is the analysis of the adoption of a technology in the paper. If technology adoption is not addressed or adoption factors are not considered, we excluded the paper. An article was only selected for full-text analysis if, after analyzing the titles, abstracts, and keywords, a clear focus on all three research areas was visible and the inclusion criteria were met for all three contexts.

By using this tripartite inclusion analysis, we were able to analyse the publications in a structured way and to reduce the 3,833 selected documents in our double-blind approach to 300 articles that were chosen for the full-text analysis. In the process of finding full versions of these publications, we had to exclude three papers as we could not access them. For the rest of the 297 articles we obtained full access and thus included them for further analysis. After a thorough examination of the full texts, we again had to exclude 249 publications because they did not meet our content-related inclusion criteria mentioned above, although the abstract analysis gave indications that they did. As a result, we finally obtained 47 selected papers on which we base the literature analysis and synthesis (see Fig.  1 ).

2.3 Descriptive analysis

Figure  2 summarises the results of the descriptive analysis on the selected literature regarding AI adoption in production that we analyse in our SLR. From Fig.  2 a), which illustrates annual publication trends (2010–2024), the increase in publications on AI adoption in production over the past 5 years is evident, yet slightly declining after a peak in 2022. After a steady increase until 2022, in which 11 articles are included in the final analysis, 2023 features ten articles, followed by three articles for 2024 until the cut-off date in May 2024. Of the 47 papers identified through our search, the majority (n = 33) are peer-reviewed journal articles and the remaining thirteen contributions conference proceedings and one book chapter (see Fig.  2 b)).

Descriptive analyses of the selected articles addressing AI adoption in production

The identified contributions reveal some additional characteristics in terms of the authors country base (Fig.  2 c)) and research methods used (Fig.  2 d)). Almost four out of ten of the publications were written in collaboration with authors from several countries (n = 19). Six of the papers were published by authors from the United States, five from Germany and four from India. In terms of the applied research methods used by the researchers, a wide range of methods is used (see Fig.  2 c), with qualitative methods (n = 22) being the most frequently used.

2.4 Factor analysis

In order to derive a comprehensive list of factors that influence the use of AI in production at different levels, we follow a qualitative content analysis. It is based on inductive category development, avoiding prefabricated categories in order to allow new categories to emerge based on the content at hand [ 44 , 45 ]. To do this, we first read the entire text to gain an understanding of the content and then derive codes [ 46 ] that seem to capture key ideas [ 45 ]. The codes are subsequently sorted into distinct categories, each of which is clearly defined and establishes meaningful connections between different codes. Based on an iterative process with feedback loops, the assigned categories are continuously reviewed and updated as revisions are made [ 44 ].

Various factors at different levels are of significance to AI and influence technology adoption [ 47 , 48 ]. To identify the specific factors that are of importance for AI adoption in production, we analyze the selected contributions in terms of the factors considered, compare them with each other and consequently obtain a list of factors through a bottom-up approach. While some of the factors are based on empirical findings, others are expected factors that result from the research findings of the respective studies. Through our analysis, a list of 35 factors emerges that influence AI adoption in production which occur with varying frequency in the studies analyzed by our SLR. Table 1 visualizes each factor in the respective contributions sorted by the frequency of occurrence.

The presence of skills is considered a particularly important factor in AI adoption in the studies analyzed (n = 35). The availability of data (n = 25) as well as the need for ethical guidelines (n = 24) are also seen as key drivers of AI adoption, as data is seen as the basis for the implementation of AI and ethical issues must be addressed in handling such an advanced technology. As such, these three factors make up the accelerants of AI adoption in production that are most frequently cited in the studies analyzed.

Also of importance are issues of managerial support (n = 22), as well as performance measures and IT infrastructure (n = 20). Some factors were also mentioned, but only addressed by one study at a time: government support, industrial sector, product complexity, batch size, and R&D Intensity. These factors are often used as quantitatively measurable adoption factors, especially in empirical surveys, such the study by Kinkel, Baumgartner, Cherubini [ 36 ].

3 Factors influencing AI adoption

The 35 factors presented characteristically in Sect.  2.4 serve as the basis for our in-depth analysis and for developing a framework of influences on AI adoption in production which are grouped into supercategories. A supercategory describes a cluster of topics to which various factors of AI adoption in production can be assigned. We were able to define seven categories that influence AI adoption in production: the internal influences of ‘business and structure’, ‘organizational effectiveness’, ‘technology and system’, ‘data management’ as well as the external influences of the ‘regulatory environment’, ‘business environment’ and ‘economic environment’ (see Fig.  3 ). The factors that were mentioned most frequently (occurrence in at least half of the papers analyzed) are marked accordingly (*) in Fig.  3 .

Framework of factors influencing AI adoption in production

3.1 Internal Environment

The internal influences on AI adoption in production refer to factors that an organization carries internally and that thus also influence adoption from within. Such factors can usually be influenced and clearly controlled by the organization itself.

3.1.1 Business and structure

The supercategory ‘business and structure’ includes the various factors and characteristics that impact a company’s performance, operations, and strategic decision-making. By considering and analyzing these business variables when implementing AI in production processes, companies can develop effective strategies to optimize their performance, increase their competitiveness, and adapt to changes in the business environment.

To understand and grasp the benefits in the use of AI, quantitative performance measures for the current and potential use of AI in industrial production systems help to clarify the value and potential benefits of AI use [ 49 , 54 , 74 , 79 , 91 ]. Assessing possible risks [ 77 ] as well as the monetary expected benefits for AI (e.g. Return on Investment (ROI)) in production plays an important role for adoption decisions in market-oriented companies [ 57 , 58 , 63 , 65 , 78 ]. Due to financial constraints, managers behave cautiously in their investments [ 78 ], so they need to evaluate AI adoption as financially viable to want to make the investment [ 61 , 63 , 93 ] and also drive acceptance [ 60 ]. AI systems can significantly improve cost–benefit structures in manufacturing, thereby increasing the profitability of production systems [ 73 ] and making companies more resilient [ 75 ]. However, in most cases, the adoption of AI requires high investments and the allocation of resources (s.a. personnel or financial) for this purpose [ 50 , 51 , 57 , 80 , 94 ]. Consequently, a lack of budgets and high expected transition costs often hinder the implementation of smart concepts [ 56 , 62 , 67 , 82 , 84 , 92 ]. It is up to management to provide necessary funding for AI adoption [ 53 , 59 , 79 ], which is required, for example, for skill development of employees [ 59 , 61 , 63 ], IT adaptation [ 62 , 66 ], AI development [ 74 ] or hardware deployment [ 68 ]. In their empirical study, Kinkel, Baumgartner, Cherubini [ 36 ] confirm a positive correlation between company size and the intensity in the use of AI technologies. Large companies generally stand out with a higher propensity to adopt [ 53 ] as they have less difficulties in comparison to small firms regarding the availability of resources [ 69 ], such as know-how, budget [ 68 , 84 ] and general data organization [ 68 ]. Others argue that small companies tend to be more open to change and are characterized by faster decision-making processes [ 68 , 93 ]. Product complexity also influences a company’s propensity for AI. Companies that produce rather simple products are more likely to digitize, which in turn offers good starting points for AI adoption. On the other hand, complex product manufacturers (often characterized by small batch sizes) are often less able to standardize and automate [ 36 ]. The company’s produced batch size has a similar influence on AI adoption. Small and medium batch sizes in particular hinder the integration of intelligent technologies, as less automation often prevails here as well. Nevertheless, even small and medium lot sizes can benefit economically from AI [ 36 ]. Since a high R&D intensity indicates a high innovation capability of a company, it is assumed to have a positive influence on AI adoption, as companies with a high R&D intensity already invest heavily in and use new innovations. This in turn speaks for existing competencies, know how and structures [ 36 ].

3.1.2 Organizational effectiveness

This supercategory focuses on the broader aspects that contribute to the effectiveness, development, and success of an organization when implementing AI in a production context. As the factors are interconnected and influence each other, decision makers should consider them carefully.

Users´ trust in AI is an essential factor to enable successful AI adoption and use in production [ 52 , 68 , 78 , 79 , 88 , 90 ]. From the users´ perspective, AI often exhibits the characteristics of a black box because its inherent processes are not fully understood [ 50 , 90 ] which can lead individuals to develop a fear towards the unknown [ 71 ]. Because of this lack of understanding, successful interaction between humans and AI is not guaranteed [ 90 ], as trust is a foundation for decisions that machines are intended to make autonomously [ 52 , 91 ]. To strengthen faith in AI systems [ 76 , 80 ], AI users can be involved in AI design processes in order to understand appropriate tools [ 54 , 90 ]. In this context, trust is also discussed in close connection with transparency and regulation [ 79 ]. User resistance is considered a barrier to implementing new information technologies, as adoption requires change [ 53 , 62 , 92 ]. Ignorance, as a kind of resistance to change, is a main obstacle to successful digital transformation [ 51 , 56 , 65 ]. Some employees may resist the change brought about by AI because they fear losing their jobs [ 52 ] or have other concerns [ 78 ]. Overcoming resistance to technology adoption requires organizational change and is critical for the success of adoption [ 50 , 51 , 62 , 67 , 71 , 80 ]. Therefore, change management is important to create awareness of the importance of AI adoption and increase acceptance of the workforce [ 66 , 68 , 74 , 83 ]. Management commitment is seen as a significant driver of technology adoption [ 53 , 59 , 81 , 82 , 86 ] and a lack of commitment can negatively impact user adoption and workforce trust and lead to skepticism towards technology [ 86 ]. The top management’s understanding and support for the benefits of the adopted technology [ 53 , 56 , 67 , 78 , 93 , 94 ] enhances AI adoption, can prioritize its implementation and also affects the performance of the AI-enabled application [ 55 , 60 , 83 ]. Preparing, enabling, and thus empowering the workforce, are considered the management’s responsibility in the adoption of digital technologies [ 59 , 75 ]. This requires intelligent leadership [ 52 ] as decision makers need to integrate their workforce into decision-making processes [ 75 ]. Guidelines can support managers by providing access to best practices that help in the adoption of AI [ 50 ]. Critical measures to manage organizational change include the empowerment of visionaries or appointed AI champions leading the change and the collaborative development of digital roadmaps [ 54 , 62 ]. To demonstrate management commitment, managers can create such a dedicated role, consisting of an individual or a small group that is actively and enthusiastically committed to AI adoption in production. This body is considered the adoption manager, point of contact and internal driver of adoption [ 62 , 74 , 80 ]. AI initiatives in production do not necessarily have to be initiated by management. Although management support is essential for successful AI adoption, employees can also actively drive integration initially and thus realize pilot projects or initial trials [ 66 , 80 ]. The development of strategies as well as roadmaps is considered another enabling and necessary factor for the adoption of AI in production [ 50 , 53 , 54 , 62 , 71 , 93 ]. While many major AI strategies already exist at country level to further promote research and development of AI [ 87 ], strategy development is also important at the firm level [ 76 , 77 , 81 ]. In this context, strategies should not be delegated top-down, but be developed in a collaborative manner, i.e. by engaging the workforce [ 75 ] and be in alignment with clear visions [ 91 , 94 ]. Roadmaps are used to improve planning, support implementation, facilitate the adoption of smart technologies in manufacturing [ 93 ] and should be integrated into both business and IT strategy [ 62 , 66 ]. In practice, clear adoption roadmaps that provide approaches on how to effectively integrate AI into existing strategies and businesses are often lacking [ 56 , 87 ]. The need for AI-related skills in organizations is a widely discussed topic in AI adoption analyses [ 79 ]. In this context, the literature points both at the need for specific skills in the development and design of AI applications [ 57 , 71 , 72 , 73 , 76 , 93 ] as well as the skills in using the technology [ 53 , 65 , 73 , 74 , 75 , 84 , 93 ] which availability in the firm is not always given [ 49 ]. AI requires new digital skills [ 36 , 50 , 52 , 55 , 56 , 59 , 61 , 63 , 66 , 78 , 80 ], where e.g. advanced analytics [ 64 , 75 , 81 ], programming skills [ 68 ] and cybersecurity skills [ 78 , 93 ] gain importance. The lack of skills required for AI is seen as a major challenge of digital transformation, as a skilled workforce is considered a key resource for companies [ 51 , 54 , 56 , 60 , 62 , 67 , 69 , 70 , 82 , 93 ]. This lack of a necessary skillset hinders the adoption of AI tools in production systems [ 58 , 77 ]. Closely related to skills is the need for new training concepts, which organizations need to consider when integrating digital technologies [ 49 , 50 , 51 , 56 , 59 , 63 , 71 , 74 , 75 ]. Firms must invest in qualification in order to create necessary competences [ 73 , 78 , 80 , 81 , 92 ]. Additionally, education must target and further develop the skills required for effectively integrating intelligent technologies into manufacturing processes [ 54 , 61 , 62 , 83 ]. Regarding this issue, academic institutions must develop fitting curricula for data driven manufacturing engineering [ 64 ]. Another driving factor of AI adoption is the innovation culture of an organization, which is influenced by various drivers. For example, companies that operate in an environment with high innovation rates, facing intense competitive pressures are considered more likely to see smart technologies as a tool for strategic change [ 83 , 91 , 93 ]. These firms often invest in more expensive and advanced smart technologies as the pressure and resulting competition forces them to innovate [ 93 ]. Another way of approach this is that innovation capability can also be supported and complemented by AI, for example by intelligent systems supporting humans in innovation or even innovating on their own [ 52 ].The entrepreneurial orientation of a firm is characterized in particular by innovativeness [ 66 ], productivity [ 63 ], risk-taking [ 86 ] as well as continuous improvement [ 50 ]. Such characteristics of an innovating culture are considered essential for companies to recognise dynamic changes in the market and make adoption decisions [ 51 , 71 , 81 , 84 , 86 , 94 ]. The prevalence of a digital mindset in companies is important for technology adoption, as digital transformation affects the entire organizational culture and behavior [ 59 , 80 , 92 ] and a lack of a digital culture [ 50 , 65 ] as well as a ‘passive mindset’ [ 78 ] can hinder the digital transformation of firms. Organizations need to develop a corresponding culture [ 66 , 67 , 71 ], also referred to as ‘AI-ready-culture’ [ 54 ], that promotes development and encourages people and data through the incorporation of technology [ 71 , 75 ]. With the increasing adoption of smart technologies, a ‘new digital normal’ is emerging, characterized by hybrid work models, more human–machine interactions and an increased use of digital technologies [ 75 , 83 ].

3.1.3 Technology and System

The ‘technology and system’ supercategory focuses on the broader issues related to the technology and infrastructure that support organizational operations and provide the technical foundation for AI deployment.

By IT infrastructure we refer to issues regarding the foundational systems and IT needed for AI adoption in production. Industrial firms and their IT systems must achieve a mature technological readiness in order to enable successful AI adoption [ 51 , 60 , 67 , 69 , 83 ]. A lack of appropriate IT infrastructure [ 68 , 71 , 78 , 91 ] or small maturity of Internet of Things (IoT) technologies [ 70 ]) hinders the efficient use of data in production firms [ 56 ] which is why firms must update their foundational information systems for successful AI adoption [ 53 , 54 , 62 , 66 , 72 , 75 ]. IT and data security are fundamental for AI adoption and must be provided [ 50 , 51 , 68 , 82 ]. This requires necessary developments that can ensure security during AI implementation while complying with legal requirements [ 52 , 72 , 78 ]. Generally, security concerns are common when implementing AI innovations [ 72 , 79 , 91 , 94 ]. This fear of a lack of security can also prevent the release of (e.g. customer) data in a production environment [ 56 ]. Additionally, as industrial production systems are vulnerable to failures as well as cyberattacks, companies need to address security and cybersecurity measures [ 49 , 76 , 88 , 89 ]. Developing user-friendly AI solutions can facilitate the adoption of smart solutions by increasing user understanding and making systems easy to use by employees as well as quick to integrate [ 50 , 72 , 84 ]. When developing user-friendly solutions which satisfy user needs [ 76 ], it is particularly important to understand and integrate the user perspective in the development process [ 90 ]. If employees find technical solutions easy to use, they are more confident in its use and perceived usefulness increases [ 53 , 67 , 68 ]. The compatibility of AI with a firm and its existing systems, i.e., the extent to which AI matches existing processes, structures, and infrastructures [ 53 , 54 , 56 , 60 , 78 , 80 , 82 , 83 , 93 , 94 ], is considered an important requirement for the adoption of AI in IT systems [ 91 ]. Along with compatibility also comes connectivity, which is intended to ensure the links within the overall network and avoid silo thinking [ 59 ]. Connectivity and interoperability of AI-based processes within the company’s IT manufacturing systems must be ensured at different system levels and are considered key factors in the development of AI applications for production [ 50 , 72 , 89 ]. The design of modular AI solutions can increase system compatibility [ 84 ]. Firms deciding for AI adoption must address safety issues [ 51 , 54 , 59 , 72 , 73 , 78 ]. This includes both safety in the use and operation of AI [ 60 , 69 ]. In order to address safety concerns of integrating AI solutions in industrial systems [ 49 ], systems must secure high reliability [ 71 ]. AI can also be integrated as a safety enabler, for example, by providing technologies to monitor health and safety in the workplace to prevent fatigue and injury [ 75 ].

3.1.4 Data management

Since AI adoption in the organization is strongly data-driven, the ‘data management’ supercategory is dedicated to the comprehensive aspects related to the effective and responsible management of data within the organization.

Data privacy must be guaranteed when creating AI applications based on industrial production data [ 49 , 58 , 59 , 60 , 72 , 76 , 78 , 79 , 82 , 88 , 89 , 91 , 94 ] as ‘[M]anufacturing industries generate large volumes of unstructured and sensitive data during their daily operations’ [ 89 ]. Closely related to this is the need for anonymization and confidentiality of data [ 61 , 69 , 70 , 78 ]. The availability of large, heterogeneous data sets is essential for the digital transformation of organizations [ 52 , 59 , 78 , 80 , 88 , 89 ] and is considered one of the key drivers of AI innovation [ 62 , 68 , 72 , 86 ]. In production systems, lack of data availability is often a barrier to AI adoption [ 58 , 70 , 77 ]. In order to enable AI to establish relationships between data, the availability of large input data that is critical [ 62 , 76 , 81 ]. New AI models are trained with this data and can adapt as well as improve as they receive new data [ 59 , 62 ]. Big data can thus significantly improve the quality of AI applications [ 59 , 71 ]. As more and more data is generated in manufacturing [ 85 ], AI opens up new opportunities for companies to make use of it [ 62 ]. However, operational data are often unstructured, as they come from different sources and exist in diverse formats [ 85 , 87 ]. This challenges data processing, as data quality and origin are key factors in the management of data [ 78 , 79 , 80 , 88 , 89 , 91 ]. To make production data valuable and usable for AI, consistency of data and thus data integrity is required across manufacturing systems [ 50 , 62 , 77 , 84 ]. Another key prerequisites for AI adoption is data governance [ 56 , 59 , 67 , 68 , 71 , 78 , 88 ] which is an important asset to make use of data in production [ 50 ] and ensure the complex management of heterogenous data sets [ 89 ]. The interoperability of data and thus the foundation for the compatibility of AI with existing systems, i.e., the extent to which AI matches existing processes, structures, and infrastructures [ 53 , 56 , 84 , 93 ], is considered another important requirement for the adoption of AI in IT systems. Data interoperability in production systems can be hindered by missing data standards as different machines use different formats [ 87 ]. Data processing refers to techniques used to preparing data for analysis which is essential to obtain consistent results from data analytics in production [ 58 , 72 , 80 , 81 , 84 ]. In this process, the numerous, heterogeneous data from different sensors are processed in such a way that they can be used for further analyses [ 87 ]. The capability of production firms to process data and information is thus important to enable AI adoption [ 77 , 86 , 93 ]. With the increasing data generation in the smart and connected factory, the strategic relevance of data analytics is gaining importance [ 55 , 69 , 78 ], as it is essential for AI systems in performing advanced data analyses [ 49 , 67 , 72 , 86 , 88 ]. Using analytics, valuable insights can be gained from the production data obtained using AI systems [ 58 , 77 , 87 ]. In order to enable the processing of big data, a profound data infrastructure is necessary [ 65 , 75 , 87 ]. Facilities must be equipped with sensors, that collect data and model information, which requires investments from firms [ 72 ]. In addition, production firms must build the necessary skills, culture and capabilities for data analytics [ 54 , 75 , 87 , 93 ]. Data storage, one of the foundations and prerequisites for smart manufacturing [ 54 , 68 , 71 , 74 ], must be ensured in order to manage the larg amounts of data and thus realize the adoption of intelligent technologies in production [ 50 , 59 , 72 , 78 , 84 , 87 , 88 , 89 ].

3.2 External environment

The external drivers of AI adoption in production influence the organization through conditions and events from outside the firm and are therefore difficult to control by the organization itself.

3.2.1 Regulatory environment

This supercategory captures the broader concept of establishing rules, standards, and frameworks that guide the behavior, actions, and operations of individuals, organizations, and societies when implementing AI.

AI adoption in production faces many ethical challenges [ 70 , 72 , 79 ]. AI applications must be compliant with the requirements of organizational ethical standards and laws [ 49 , 50 , 59 , 60 , 62 , 75 ] which is why certain issues must be examined in AI adoption and AI design [ 62 , 73 , 82 , 91 ] so that fairness and justice are guaranteed [ 78 , 79 , 92 ]. Social rights, cultural values and norms must not be violated in the process [ 49 , 52 , 53 , 81 ]. In this context, the explainability and transparency of AI decisions also plays an important role [ 50 , 54 , 58 , 70 , 78 , 89 ] and can address the characteristic of AI of a black box [ 90 ]. In addition, AI applications must be compliant with legal and regulatory requirements [ 51 , 52 , 59 , 77 , 81 , 82 , 91 ] and be developed accordingly [ 49 , 76 ] in order to make organization processes using AI clear and effective [ 65 ]. At present, policies and regulation of AI are still in its infancy [ 49 ] and missing federal regulatory guidelines, standards as well as incentives hinder the adoption of AI [ 67 ] which should be expanded simultaneously to the expansion of AI technology [ 60 ]. This also includes regulations on the handling of data (e.g. anonymization of data) [ 61 , 72 ].

3.2.2 Business environment

The factors in the ‘business environment’ supercategory refer to the external conditions and influences that affect the operations, decision making, and performance of the company seeking to implement AI in a production context.

Cooperation and collaboration can influence the success of digital technology adoption [ 52 , 53 , 59 , 72 ], which is why partnerships are important for adoption [ 53 , 59 ] and can positively influence its future success [ 52 , 67 ]. Both intraorganizational and interorganizational knowledge sharing can positively influence AI adoption [ 49 ]. In collaborations, companies can use a shared knowledge base where data and process sharing [ 51 , 59 , 94 ] as well as social support systems strengthen feedback loops between departments [ 79 , 80 ]. With regard to AI adoption in firms, vendors as well as service providers need to collaborate closely to improve the compatibility and operational capability of smart technologies across different industries [ 82 , 93 ]. Without external IT support, companies can rarely integrate AI into their production processes [ 66 ], which is why thorough support from vendors can significantly facilitate the integration of AI into existing manufacturing processes [ 80 , 91 ]. Public–private collaborations can also add value and governments can target AI dissemination [ 60 , 74 ]. The support of the government also positively influences AI adoption. This includes investing in research projects and policies, building a regulatory setting as well as creating a collaborative environment [ 60 ]. Production companies are constantly exposed to changing conditions, which is why the dynamics of the environment is another factor influencing the adoption of AI [ 52 , 63 , 72 , 86 ]. Environmental dynamics influence the operational performance of firms and can favor an entrepreneurial orientation of firms [ 86 ]. In order to respond to dynamics, companies need to develop certain capabilities and resources (i.e. dynamic capabilities) [ 86 ]. This requires the development of transparency, agility, as well as resilience to unpredictable changes, which was important in the case of the COVID-19 pandemic, for example, where companies had to adapt quickly to changing environments [ 75 ]. A firm’s environment (e.g. governments, partners or customers) can also pressure companies to adopt digital technologies [ 53 , 67 , 82 , 91 ]. Companies facing intense competition are considered more likely to invest in smart technologies, as rivalry pushes them to innovate and they hope to gain competitive advantages from adoption [ 36 , 66 , 82 , 93 ].

3.2.3 Economic environment

By considering both the industrial sector and country within the subcategory ‘economic environment’, production firms can analyze the interplay between the two and understand how drivers can influence the AI adoption process in their industrial sector’s performance within a particular country.

The industrial sector of a firm influences AI adoption in production from a structural perspective, as it indicates variations in product characteristics, governmental support, the general digitalization status, the production environment as well as the use of AI technologies within the sector [ 36 ]. Another factor that influences AI adoption is the country in which a company is located. This influences not only cultural aspects, the availability of know-how and technology orientation, but also regulations, laws, standards and subsidies [ 36 ]. From another perspective, AI can also contribute to the wider socio-economic growth of economies by making new opportunities easily available and thus equipping e.g. more rural areas with advanced capabilities [ 78 ].

3.3 Future research directions

The analysis of AI adoption in production requires a comprehensive analysis of the various factors that influence the introduction of the innovation. As discussed by Kinkel, Baumgartner, Cherubini [ 36 ], our research also concludes that organizational factors have a particularly important role to play. After evaluating the individual drivers of AI adoption in production in detail in this qualitative synthesis, we draw a conclusion from the results and derive a research agenda from the analysis to serve as a basis for future research. The RQs emerged from the analyzed factors and are presented in Table  2 . We developed the questions based on the literature review and identified research gaps for every factor that was most frequently mentioned. From the factors analyzed and RQs developed, the internal environment has a strong influence on AI adoption in production, and organizational factors play a major role here.

Looking at the supercategory ‘business and environment’, performance indicators and investments are considered drivers of AI adoption in production. Indicators to measure the performance of AI innovations are necessary here so that managers can perform cost–benefit analyses and make the right decision for their company. There is a need for research here to support possible calculations and show managers a comprehensive view of the costs and benefits of technology in production. In terms of budget, it should be noted that AI adoption involves a considerable financial outlay that must be carefully weighed and some capital must be available to carry out the necessary implementation efforts (e.g., staffing costs, machine retrofits, change management, and external IT service costs). Since AI adoption is a complex process and turnkey solutions can seldom be implemented easily and quickly, but require many changes (not only technologically but also on an organizational level), it is currently difficult to estimate the necessary budgets and thus make them available. Especially the factors of the supercategory ‘organizational effectiveness’ drive AI adoption in production. Trust of the workforce is considered an important driver, which must be created in order to successfully implement AI. This requires measures that can support management in building trust. Closely related to this are the necessary change management processes that must be initiated to accompany the changes in a targeted manner. Management itself must also play a clear role in the introduction of AI and communicate its support, as this also influences the adoption. The development of clear processes and measures can help here. Developing roadmaps for AI adoption can facilitate the adoption process and promote strategic integration with existing IT and business strategy. Here, best practice roadmaps and necessary action steps can be helpful for companies. Skills are considered the most important driver for AI adoption in manufacturing. Here, there is a lack of clear approaches that support companies in identifying the range of necessary skills and, associated with this, also opportunities to further develop these skills in the existing workforce. Also, building a culture of innovation requires closer research that can help companies foster a conducive environment for AI adoption and the integration of other smart technologies. Steps for developing a positive mindset require further research that can provide approaches for necessary action steps and measures in creating a positive digital culture. With regard to ‘technology and system’, the factors of IT infrastructure and security in particular are driving AI adoption in production. Existing IT systems must reach a certain maturity to enable AI adoption on a technical level. This calls for clear requirements that visualize for companies which systems and standards are in place and where developments are needed. Security must be continuously ensured, for which certain standards and action catalogs must be developed. With regard to the supercategory ‘data management’, the availability of data is considered the basis for successful AI adoption, as no AI can be successfully deployed without data. In the production context in particular, this requires developments that support companies in the provision of data, which usually arises from very heterogeneous sources and forms. Data analytics must also be closely examined, and production companies usually need external support in doing so. The multitude of data also requires big data storage capabilities. Here, groundwork is needed to show companies options about the possibilities of different storage options (e.g., on premis vs. cloud-based).

In the ‘regulatory environment’, ethics in particular is considered a driver of AI adoption in production. Here, fundamental ethical factors and frameworks need to be developed that companies can use as a guideline to ensure ethical standards throughout the process. Cooperations and environmental dynamism drive the supercategory ‘business environment’. Collaborations are necessary to successfully implement AI adoption and action is needed to create the necessary contact facilitation bodies. In a competitive environment, companies have to make quick decisions under strong pressure, which also affects AI adoption. Here, guidelines and also best practice approaches can help to simplify decisions and quickly demonstrate the advantage of the solutions. There is a need for research in this context.

4 Conclusions

The use of AI technologies in production continues to gain momentum as managers hope to increase efficiency, productivity and reduce costs [ 9 , 13 , 20 ]. Although the benefits of AI adoption speak for themselves, implementing AI is a complex decision that requires a lot of knowledge, capital and change [ 95 ] and is influenced by various internal and external factors. Therefore, managers are still cautious about implementing the technology in a production context. Our SLR seeks to examine the emergent phenomenon of AI in production with the precise aim of understanding the factors influencing AI adoption and the key topics discussed in the literature when analyzing AI in a production context. For this purpose, we use the current state of research and examine the existing studies based on the methodology of a systematic literature analysis and respond to three RQs.

We answer RQ1 by closely analyzing the literature selected in our SLR to identify trends in current research on AI adoption in production. In this process, it becomes clear that the topic is gaining importance and that research has increased over the last few years. In the field of production, AI is being examined from various angles and current research addresses aspects from a business, human and technical perspective. In our response to RQ2 we synthesized the existing literature to derive 35 factors that influence AI adoption in production at different levels from inside or outside the organization. In doing so, we find that AI adoption in production poses particularly significant challenges to organizational effectiveness compared to other digital technologies and that the relevance of data management takes on a new dimension. Production companies often operate more traditionally and are sometimes rigid when it comes to change [ 96 , 97 ], which can pose organizational challenges when adopting AI. In addition, the existing machines and systems are typically rather heterogeneous and are subject to different digitalization standards, which in turn can hinder the availability of the necessary data for AI implementation [ 98 , 99 ]. We address RQ3 by deriving a research agenda, which lays a foundation for further scientific research and deepening the understanding of AI adoption in production. The results of our analysis can further help managers to better understand AI adoption and to pay attention to the different factors that influence the adoption of this complex technology.

4.1 Contributions

Our paper takes the first step towards analysing the current state of the research on AI adoption from a production perspective. We represent a holistic view on the topic, which is necessary to get a better understanding of AI in a production-context and build a comprehensive view on the different dimensions as well as factors influencing its adoption. To the best of our knowledge, this is the first contribution that systematises research about the adoption of AI in production. As such, it makes an important contribution to current AI and production research, which is threefold:

First, we highlight the characteristics of studies conducted in recent years on the topic of AI adoption in production, from which several features and developments can be deduced. Our results confirm the topicality of the issue and the increasing relevance of research in the field.

Having laid the foundations for understanding AI in production, we focused our research on the identification and systematization of the most relevant factors influencing AI adoption in production at different levels. This brings us to the second contribution, our comprehensive factor analysis of AI adoption in production provides a framework for further research as well as a potential basis for managers to draw upon when adopting AI. By systematizing the relevant factors influencing AI adoption in production, we derived a set of 35 researched factors associated with AI adoption in production. These factors can be clustered in two areas of analysis and seven respective supercategories. The internal environment area includes four levels of analysis: ‘business and structure’ (focusing on financial aspects and firm characteristics), ‘organizational effectiveness’ (focusing on human-centred factors), ‘technology and system’ (based on the IT infrastructure and systems) as well as ‘data management’ (including all data related factors). Three categories are assigned to the external environment: the ‘regulatory environment’ (such as ethics and the regulatory forms), the ‘business environment’ (focused on cooperation activities and dynamics in the firm environment) and the ‘economic environment’ (related to sectoral and country specifics).

Third, the developed research plan as outlined in Table  2 serves as an additional outcome of the SLR, identifying key RQs in the analyzed areas that can serve as a foundation for researchers to expand the research area of AI adoption in production. These RQs are related to the mostly cited factors analyzed in our SLR and aim to broaden the understanding on the emerging topic.

The resulting insights can serve as the basis for strategic decisions by production companies looking to integrate AI into their processes. Our findings on the factors influencing AI adoption as well as the developed research agenda enhance the practical understanding of a production-specific adoption. Hence, they can serve as the basis for strategic decisions for companies on the path to an effective AI adoption. Managers can, for example, analyse the individual factors in light of their company as well as take necessary steps to develop further aspects in a targeted manner. Researchers, on the other hand, can use the future research agenda in order to assess open RQs and can expand the state of research on AI adoption in production.

4.2 Limitations

Since a literature review must be restricted in its scope in order to make the analyses feasible, our study provides a starting point for further research. Hence, there is a need for further qualitative and quantitative empirical research on the heterogeneous nature of how firms configure their AI adoption process. Along these lines, the following aspects would be of particular interest for future research to improve and further validate the analytical power of the proposed framework.

First, the lack of research on AI adoption in production leads to a limited number of papers included in this SLR. As visualized in Fig.  2 , the number of publications related to the adoption of AI in production has been increasing since 2018 but is, to date, still at an early stage. For this reason, only 47 papers published until May 2024 addressing the production-specific adoption of AI were identified and therefore included in our analysis for in-depth investigation. This rather small number of papers included in the full-text analysis gives a limited view on AI adoption in production but allows a more detailed analysis. As the number of publications in this research field increases, there seems to be a lot of research happening in this field which is why new findings might be constantly added and developed as relevant in the future [ 39 ]. Moreover, in order to research AI adoption from a more practical perspective and thus to build up a broader, continuously updated view on AI adoption in production, future literature analyses could include other publication formats, e.g. study reports of research institutions and companies, as well discussion papers.

Second, the scope of the application areas of AI in production has been increasing rapidly. Even though our overview of the three main areas covered in the recent literature serves as a good basis for identifying the most dominant fields for AI adoption in production, a more detailed analysis could provide a better overview of possibilities for manufacturing companies. Hence, a further systematisation as well as evaluation of application areas for AI in production can provide managers with the information needed to decide where AI applications might be of interest for the specific company needs.

Third, the systematisation of the 35 factors influencing AI adoption in production serve as a good ground for identifying relevant areas influenced by and in turn influencing the adoption of AI. Further analyses should be conducted in order to extend this view and extend the framework. For example, our review could be combined with explorative research methods (such as case studies in production firms) in order to add the practical insights from firms adopting AI. This integration of practical experiences can also help exploit and monitor more AI-specific factors by observing AI adoption processes. In enriching the factors through in-depth analyses, the results of the identified AI adoption factors could also be examined in light of theoretical contributions like the technology-organization-environment (TOE) framework [ 47 ] and other adoption theories.

Fourth, in order to examine the special relevance of identified factors for AI adoption process and thus to distinguish it from the common factors influencing the adoption of more general digital technologies, there is a further need for more in-depth (ethnographic) research into their impacts on the adoption processes, particularly in the production context. Similarly, further research could use the framework introduced in this paper as a basis to develop new indicators and measurement concepts as well as to examine their impacts on production performance using quantitative methods.

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Heimberger, H., Horvat, D. & Schultmann, F. Exploring the factors driving AI adoption in production: a systematic literature review and future research agenda. Inf Technol Manag (2024). https://doi.org/10.1007/s10799-024-00436-z

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