manufacturing company research paper

• Open access
• Published: 16 September 2021

Implementation of strategic cost management in manufacturing companies: overcoming costs stickiness and increasing corporate sustainability

• Mohammad Mahdi Rounaghi   ORCID: orcid.org/0000-0002-9640-678X 1 ,
• Hajer Jarrar 2 &
• Leo-Paul Dana 3  

Future Business Journal volume  7 , Article number:  31 ( 2021 ) Cite this article

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In today's competitive world, three factors: price, quality and time have critical roles in the success of the companies to achieve success in the competition. For this purpose, the companies have to also adapt themselves to changes in technology and environment. Strategic cost management is the best way to improve the sustainable management models in the manufacturing companies. Strategic cost management has solved many of the problems and shortcomings of traditional accounting system and by accurate determination of costs, their proper allocation to products and elimination of waste, tries to create value for shareholders by using continuous improvement. The objective of this paper was to develop a management model called strategic cost management that reduced costs stickiness and increased corporate sustainability. Using strategic cost management approach can create competitive advantage for the companies, because it provides accurate cost price information so that the users can easily understand the information. The aim of the paper by introducing strategic cost management was to contribute toward accurate pricing, which could result in the increased profitability and competitiveness of the manufacturing companies in a highly competitive global market and at a market‐based price. Also, due to the growing competition among companies in providing high quality products with reasonable prices, a precise system of measurement of the cost of the product is necessary.

Introduction

In recent years, economic analysis in the planning process and in the monitoring process of the production process shows that three factors: price, quality and time have critical roles in the success of the companies to achieve success in the competition. The world faces the problem of integration between sustained business functions. The sustainability data are not sufficiently integrated. To solve this problem, organizations need information systems to facilitate their sustainability initiatives [ 1 , 2 ]. Also, businesses and academics worldwide agree regarding the benefits of sustainable development (SD). Improving reputation and branding and increasing revenues by reducing costs are the primary strategic objectives of any entity [ 3 , 4 ]. In this paper, we introduce the strategic cost management approach that helps manufacturing companies for overcoming the costs stickiness and monitoring the life cycle of products and it introduces integrated sustainable development system for manufacturing companies.

Strategic cost management is a process connecting financial management, cost management and strategic management. It involves cost optimization and financial resources preparation which are needed to achieve desired strategic market position in cost effective manner. The importance of managing costs and aligning them with the business strategy of an entity is critical especially in the midst of challenging economic times faced by businesses today. Traditionally companies have been under pressure to cut cost in the short-term without really thinking about sustainable change, impact on the people and integration with the overall business strategy. In the current business environment of increased global competition, new markets, increasing regulation and changing demographics, successful companies are changing their approach to cost structuring and control.

Over the last decade, research in management accounting has challenged the fundamental assumption that cost behavior is symmetric for activity increases and decreases. Cost behavior is an important issue in cost accounting and management accounting, as it widely affects decision-making processes. Moreover, several techniques generally used by managerial accountants and financial analysts depend mainly on cost behavior, such as conventional ABC, cost estimation and cost-volume-profit analysis. Quality management (QM) has been widely viewed as a management paradigm that enables firms to gain a competitive. Therefore, overcoming on cost stickiness is a critical issue for mangers of manufacturing companies. Also, understanding cost behavior is an essential element of cost and management accounting [ 5 – 8 ].

Cost stickiness, also referred to as asymmetric cost behavior, is a well-documented result of managerial discretion underlying the development of corporate cost compared to changes in firm activity. Managers’ decisions to maintain the resource allocations due to product market competition can be costly, especially during periods of sales decreases. Under the traditional model of cost behavior, costs are assumed to be either fixed or move proportionately and symmetrically with sales changes. The traditional model of cost behavior distinguishes between fixed and variable costs and posits a proportional relation between variable costs and underlying activity levels. Understanding sticky cost behavior is important and has direct benefits for the economy as it provides useful information to managers making decisions on cost control and to external stakeholders (e.g., financial analysts) assessing firm performance. As the global economy integrates and competes, strengthening cost management and operational efficiency becomes increasingly important to firms’ survival and development [ 9 – 14 ].

Cost management is an important part of business management in the manufacturing industry. The degree of cost management implementation is a comprehensive index to measure the level of enterprise management. In particular, firms with limited access to capital have higher costs of securing external financing during the capacity expansion periods, which increases the upward adjustment costs. When activity decreases, firms with limited access to capital may suffer more decrease in the present value of revenue generated by a marginal capacity, as these firms have higher opportunity cost of capital and thus higher discount rates compared to firms with better access to capital. Therefore, we hypothesize that limited access to capital not only reduces contemporary capacity expansions associated with sales increases, but also weakens the degree of cost stickiness when sales decrease [ 15 , 16 ].

On the other hand, cost management is an important part of business management in the manufacturing industry. The degree of cost management implementation is a comprehensive index to measure the level of enterprise management. From investors’ perspective, investors depend on the published financial statements prepared by the management that are based on available information regarding the determinants of cost behavior. From financial analysts’ perspective, predicting cost behavior is an essential part of earnings prediction [ 16 – 18 ].

In many production firms, it is common practice to financially reward managers for firm performance improvement. For decades, firms have devoted to improving the speed and efficiency of material and information flows in the supply chain, acknowledging the importance of time-based competitive advantage in the dynamic business environment. As one of the key factors in decision-making process, the evolution of product price passes critical information. Managing costs by utilizing resources effectively is regarded as fundamental to success in today's competitive environment. Cost behavior as “sticky” if costs increase more for activity increases than they decrease for an equivalent activity decrease. Sticky behavior is the result of decisions made by managers when activity decreases. When activity drops, the manager must decide whether to (a) maintain committed resources and bear the cost of unutilized capacity at least in the short-term or (b) immediately reduce committed resources and incur potentially large retrenching costs in the current period and, if activity increases in the future, incur further costs to replace resources. Traditional accounting cost models assume that fixed costs are independent of the level of activity and variable costs change proportionately with changes in the level of activity. In the common traditional model of the behavior of costs, which is generally accepted in accounting literature, costs are usually divided into two categories of fixed and variable ones in terms of changes in activity level: fixed occupants are variable. Most management accounting texts assume that unit variable costs are linear and proportional to changes in activity and that fixed costs are fixed. The proportionality and symmetry between costs and activity implies that a 1% increase in activity results in a 1% increase in costs, and a 1% decrease in activity results in a 1% decrease in costs. Stickiness might also be conditioned by existing capacity [ 5 , 19 – 26 ].

Notions of cost behavior are a key element in management accounting [ 27 ]. There are two main views about the existence of expense stickiness: rational decision-making and motivational. The rational decision-making view treats expense stickiness as a consequence of management rationally choosing between alternatives after comprehensively weighting costs and benefits. The second view is motivation-based and relates expense stickiness to managerial incentives, suggesting that managers are not expected to behave as if they were in an ideal world. Among their dysfunctional behavior, perks and earnings management reflecting different contracting stimulations are often observed [ 28 ].

Planning and control are of the important tasks of management. Cost related information that managers need them to perform these tasks may be received from classified information reflected in the financial statements. The required information in this regard cannot be easily extracted from the financial statements [ 29 ]. A business entity expenses can show different behaviors suitable to the level of activity. In traditional cost model it is often assumed that administration, general and selling costs varies according to activity level. However, recent experimental studies have revealed evidence that shows that administration, general and selling costs behave asymmetrically [ 30 ]. An asymmetric behavior is a behavior in which cost increase more rapidly. In other words, the reduction in costs at the time of declining sales is lower than when the cost increases at the time of the same level of sales. This cost behavior is called cost stickiness. Expanding researches show that economic factors such as increase in assets and uncertainty about the future can have an impact on the asymmetric behavior of cost.

Costs stickiness

Cost behavior is defined as cost reaction in response to changes in activity level. Managers who understand how costs behave, have better circumstances for predicting spending trends in various operational positions. This position allows them to plan their activities and thus plan their operating revenues better. The traditional view related to costs indicates that changes in costs have a proper relationship with increased and decreased activity level. However, recent researches about costs behaviors indicate costs stickiness. Thus the degree of increase in costs level as a result of increase in activity level is higher than the degree of reduction in costs level as a result of decrease in activity level.

According to the idea of Anderson et al. [ 31 ], there are many reasons for costs stickiness. Some of these reasons include natural reluctance to lay off employees when downsizing, firm costs and the need for time to approve a reduction in the volume of activity and management decisions for maintaining used resources which could be the result of individual consideration and leads to imposing cost to the firm. By determining the stickiness of cost, the company owners can analyze whether managers incur costs to the firm or not [ 32 ].

Managers of manufacturing companies must consider the relationship of costs with income and the effect of income changes on the costs rate when planning and budgeting the company activities for predicting the future costs and thus offer a more comprehensive budget [ 33 ]. The ultimate goal of any business unit is maximizing profits and consequently, an increase in equity. Management of each profit-oriented enterprise tries to gain maximum benefit and efficiency from using the fewest resources and one of the simplest ways to reduce consumption of resources is cost control. But this requires complete knowledge of how costs behave and the factors influencing the behavior of the cost. One of the items that should be considered in the analysis of cost behavior is the phenomenon of cost stickiness. The public and dominant view is that with declining sales, costs should also be changed accordingly. But in fact, it does not happen [ 34 ].

Today, increasing competition in domestic and international markets has forced managers to better understand their cost structure and become aware of cost orientations means how the costs change. The meaning of cost orientation is a model according which costs react to changes in activity level [ 35 ]. Therefore, it is suggested that managers calculate their costs stickiness and consider all aspects of this important issue in their decisions. Orientation or the concept of cost stickiness gives a great help to investors and shareholders. Because in companies with strong stickiness, by reduced selling, costs will change more than the time when selling increases and this will be considered as a weakness of management by the investors and shareholders; while one of the main reasons of cost stickiness is bearing the current costs to avoid more losses in the future and or more profit in the future and it depends on management decisions [ 36 ].

Review of literature

Sustainable development refers to an economic, environmental and social development that meets the needs of the present and does not prevent future generations from fulfilling their needs. In manufacturing companies, collaboration between supply chain members is important for the sustainability and competitive advantage of a supply chain. The collaborative activities in a supply chain include various joint activities for cost reduction, research and development (R&D), product development, manufacturing, marketing, distribution, and service. The commitment of companies to corporate sustainability has been frequently discussed in theory and practice. Such a commitment to corporate sustainability demands a strategic approach to ensure that corporate sustainability is an integrated part of the business strategy and processes. Also, the effective adoption of continuously developing new technologies is a critical determinant of organizational competitiveness [ 37 – 41 ].

For the first time [ 5 ] tested the hypothesis that costs are sticky and approved the presence of stickiness in the costs behavior. They established a model with administration, general and sales costs as a function of sales, and found that costs increase by an average of 55% in response to a 1% increase in net income, but decrease only by 35% against 1% reduced income. In other words, a 1% increase in net sales, costs increase by 55% but by 1% decrease in net sales, costs decrease only by 35%. Due to the lack of public information about costs related drivers, they used data of administration, general and sales costs and net income of sales for the analysis of cost stickiness, and stated that they can analyze the behavior of administration, general and sales costs based on sales net income because sales volume stimulates many parts of this cost. Subramaniam and Weidenmier Watson [ 25 ] tested the presence of behavior of stickiness in the cost price of goods sold, and the results showed a positive relationship. They also tested the effect of different economic conditions, such as rates of GDP and the different characteristics of companies, such as total assets and number of employees of companies on costs stickiness. Their results showed that in periods of economic growth, the severity of stickiness is more and in the periods that income decrease happened in its previous periods, severity of stickiness decreases. Also, by increasing the ratio of total assets to sales and an increase in the number of personnel of companies, severity of cost stickiness increases. Stickiness of sales and distribution and general and administration costs has been studied in another study by Anderson et al. [ 31 ]. The main hypothesis of this study is public sale and administration costs. After collecting data related to cost of general sales and administration and sales revenue costs of 7629 American companies in a 20-year period (1979–1998), the relationship between costs and sales was examined by multi-varibale regression relationship. The results of this study did not confirm the main hypothesis of the research and announce the general sale and administration costs of companies in the statistical population of the research, sticky.

The results obtained by Weiss [ 18 ] from a sample of 2520 out of 44,931 industrial companies from 1986 to 2005 show the issue that the sticky behavior of costs increased the accuracy of analysts in predicting revenue in total, considering the fact that prediction horizon and especial effects of industry have put this analysis under control. With regard to the classification of costs into sticky and non-sticky costs, the results of Weiss's research [ 18 ] show that the accuracy of analysts in forecasting revenues for firms with sticky cost behavior is on average 25 percent less than that of people who analyze for companies with non-sticky cost behavior. Obviously, the behavior of cost has a considerable influence on the accuracy of analysts' prediction.

In Kordestani and Mortazavi, research [ 30 ], the power of profit prediction was compared with other models by the model based on variability and stickiness of cost. The study showed that the accuracy of prediction of the model based on the variability of costs and stickiness of cost is significantly higher than the other models. In several domestic researches, stickiness of various costs has been studied. According to the results of Ghaemi and Nematollahi's research, the cost price of the sold goods and selling and distribution and general and administration costs are sticky. Another study from the same researcher showed that overhead costs are sticky, but the costs of raw materials, direct wages and financial costs are not sticky.

In other study, Khani and Shafiei [ 42 ] examined cost stickiness and its relationship with sales and the results of their research indicate an undeniable relationship between the amount of sales and company earnings with the level of company's costs. Although their findings indicate that costs do not increase in proportion to profit increase, but there is a significant relationship between them.

In other study, Banker et al. [ 43 ] examined the relationship between uncertainty and sticky behavior of cost. By examining administration, general and sales costs, number of employees and their working hours, they evaluated cost stickiness. The results indicate the presence of cost stickiness in the sample under investigation. Sepasi et al. [ 44 ] examined the characteristics of management behavior toward costs stickiness. Their studied a sample consisting 14,568 year-company and examined administration, general and sales costs for the years 1992–2011. The results showed behavioral changes in managers about cost stickiness so that the occurrence of cost stickiness phenomenon increases the confidence of managers.

Management of strategy and strategic cost management

Effective strategic management, plays an important role in the success of the company or organization. Increase in competition in the international arena, new technologies and changes in business processes, caused management to become more dynamic and important than before. Managers should always have a competitive attitude and for this purpose the company's competitive strategy is essential. Strategic attitude leads the manager to anticipate changes and products and their production process will be designed based on anticipated changes in demand and customer's needs. In this situation, flexibility is important.

In developed countries, most organizations use data of cost management. But the extent of their reliance on this information depends on the nature of the competitive strategy of the company. Many companies compete on the basis of the provision of goods and services at the lowest cost price. Some companies compete on the basis of being a leader in production and offering superior and differentiated products. The role of cost management is supporting corporate strategy by providing the information through which one can be successful in products development and their marketing. For achieving corporate sustainability, we suggest to use the instruments of strategic cost management in manufacturing companies . Today, managers use strategic cost management tools to accomplish strategies and achieve main success producer factors.

Instruments of strategic cost management are as below:

The most common system that used in many companies is activity-based costing system. Activity-based costing system which is specifies the resources consumed by each activity during the relevant period; and thus the cost of each activity is precisely calculated. Then the aggregated costs of any activity are assigned to the considered product or customer, depending on the product consumption or the customer use of that activity [ 45 ]. The other instrument is bench-marking. Bench-marking is a process that the companies try to choose the best practice as of the right activity in comparison with the leading companies, then given the success-builder factors, the company processes are improved to the level of performance of its competitors or even reach to a better level. For identification of internal and external failure factors in the companies, we suggest to use total quality management technique. Total quality management a new concept that emphasizes on precise measurement of the costs and identification of internal and external failure factors, through which a way to lower production (lean production) by continuous improvement in company processes is created [ 46 ].

For finding the precise systems of measurement of the cost, in-time production system and kaizen costing are useful tools for manufacturing companies. In-time production system is a system based on the volume of demand. In this system, a piece of product will be purchased or produced only when a sign of its consumer is received. This prevents the accumulation of inventory in workstations. Among the main objectives of this system we can mention improvement of quality and increase in productivity with an emphasis on the kaizen concept. Kaizen costing is a managerial technique through which managers and employees of the company become committed to perform continuous improvement program in the quality and other key factors of success. In the path of continuous improvement, the processes are re-engineered and non-value activities in the manufacturing process are removed or left behind [ 47 ].

The other instruments are target costing and value engineering. In target costing method, the costs are determined according to the product price. It means that first the companies determine the product selling prices, by analyzes of the market and then according to their expected profit, determine the cost price of the product. In other words, goal-oriented costing system is profit planning and cost management system that in that base it was the price, and the essential emphasis is on customers. Goal-oriented costing system focuses on the design stage and requires the participation of all specialized units [ 48 ]. Value engineering is suggested with the aim of examination of all activities of a project, from the formation of the first thought to the design and implementation and then setting up and utilization, is known as one of the most efficient and the most important economic methods in the field of engineering activities [ 49 ]. The purpose of value engineering is eliminating or modifying any factor that leads to the imposition of unnecessary costs, without hurting the core and essential functions of the system. Value engineering is the continuous improvement of design and implementation and it is not merely a program to reduce costs, but is a way to maximize the value of designs [ 50 ].

Implementation stages of strategic cost management

Implementation stages of strategic cost management include value chain analysis, strategic situation analysis and analysis of structural and administrative costs drivers.

Analysis of the value chain

Value chain analysis is an instrument for strategic analysis that helps companies to better understand the competitive advantage. Value chain analysis focuses on the whole value chain of the product from design to production and after-sales service. The basic concept of analysis is that by a thorough examination of each of the activities in the value chain, one can reveal the activities that the companies have the highest or lowest success in them from competition perspective, and plan accordingly.

Analysis of strategic situation

At this stage, the company determines its potential and current competitive advantage by examining valued activities and cost drivers which have been specified in the previous stage. Companies which have competitive strategy of cost leadership are strongly trying to reduce their costs to the level of cost of cost leadership. Cost leadership focuses on cost reduction only as far as it makes sure that it is the leader in price and the holder of the lowest cost in the market. Reduction of costs is usually done by increasing productivity in the production process, distribution or general and administrative expenses. In this management strategy, maintaining stability is a priority and the company is not looking for innovation and risk-taking, but is looking for offering products and services at competitive prices. In contrast, competitive strategy of differentiation, allows the companies to raise the price of products higher than that of their competitors and without significant reduction in costs, have high profitability. These companies, by creating differentiation between the products and creating new features, make customers willing to pay a reasonable price as a result of this differentiation. Using the product differentiation strategy, one can reduce the intensity of competition and no threat of product substitution happens for the manufacturer, because all customers become loyal to the brand of the product [ 51 , 52 , 53 ].

Analysis of drivers of structural and executory cost

Strategic Analysis of cost drivers helps companies in improvement of their competitive situation. Drivers of structural and executory cost are used to facilitate operational and strategic decision-making.

Driver of structural cost, has strategic nature because it includes programs and decisions which have long-term effects. In this regard, the following items are necessary to be noted:

Scale: For example, a retail company shall determine the number of new stores it opens during the year in order to achieve the strategic goals and competitive success.

Technology: New technologies can significantly reduce the company costs. For example, some manufacturing companies in developed countries use computer technology to show number of products that their customers use (especially large retailers), so that whenever the customers run out of the inventory in the warehouse, they send for them quickly.

Complexity of products: companies that produce a high variety of products, have high cost of planning and management of production and also high distribution costs and after-sales service. Such companies usually use activity-based costing to determine the degree of profitability of their products.

Administrative cost drivers, are the factors that companies can manage them in the short term through operational decisions to reduce costs. These factors include:

Work commitment: work commitment causes reduction in costs. The companies in which there is a strong correlation between the employees, can significantly reduce their operating costs.

Design of Production process: the sequence arrangement of equipment and the frequency of processes lead to accelerating the production process in the company. Production technology innovations can significantly reduce costs.

Relationships with suppliers of raw materials of the company: the companies can reduce their costs significantly through agreements with suppliers of raw materials on quality, delivery time and other characteristics of their required raw materials.

Conclusions

Today, sustainability emphasizes various aspects of the organization in economic, social and environmental terms, so the importance of this issue is very important for current and future generations. Most companies have come to the conclusion that in order to improve the efficiency and effectiveness of production sustainability, they need to monitor, measure and control the characteristics of sustainable production. Therefore, measuring the sustainability of production has become an important issue in production and operations.

The purpose of this paper is to design a model for achieving a sustainable development index in order to integrate the economic, social and environmental performance data of manufacturing industries. By understanding the limitations and shortages of resources, the approach of the manufacturing companies includes the acquisition of new production mechanisms and technologies. To achieve newer and more innovative technologies tailored to their production processes in order to reduce production costs and increase their market share, these companies have conducted costly research. One way to deal with a shortage of resource for companies is reduce their costs. Companies regardless of sizes and operational scales must take economic opportunities into account in the long run, limiting opportunities, and incorporating innovative solutions, sustainable development, and positive social and environmental impact into their business activities.

Small-business owners face an ongoing challenge in trying to balance the need to serve customers and meet long-term business objectives while at the same time controlling the cost of doing business. A strategic cost management strategy in which cost decisions are made according to the value they add to both the business and the customer is often the most effective strategy a small business can adopt. Good financial decisions come from an effective cost management strategy designed to maximize value and minimize both initial and ongoing costs. Although a great many of a business’s cost-based decisions involve purchasing, pricing and inventory management, it’s also important for every small-business owner to consider costs involved inside the business.

In a competitive world, paying attention to cost management to reduce costs and increase customer satisfaction are priorities. Today, noting the proper role of the choosing quality and quantity of production factors, choosing between user processes or capital in the production process and selection of appropriate technology, in determining the cost price and producing products that meet the price reasonable in accordance with the customer' purchasing power appear more than before.

Providing the required information of cost management is possible only by establishing a modern system of management accounting including the design and use of various management accounting tools within the organization. Among these tools, there are activity-based costing, target costing, Kaizen costing, product life cycle costing. Strategic cost management is effective by accurate evaluation and identification of costs in the creation of income, profitability and value creation for companies.

By a correct understanding of their competitive situation and by using instruments of cost management, companies can reduce unnecessary costs. Also strategic cost management, by providing more accurate data for the managers, helps them in the short and long-term decision-making to achieve their strategic goals.

Given the importance of understanding the costs for those inside and outside the organization, such as managers, capital market analysts, investors and auditors recommendations for future research are presented as follows:

Examination of the effect of the changes in sales on costs stickiness.

Study of the relationship between management optimism with cost stickiness in various industries.

Examination of the relationship between the cost structure with behavior of each expense.

Availability of data and materials

This paper has no associated data.

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Rounaghi, M.M., Jarrar, H. & Dana, LP. Implementation of strategic cost management in manufacturing companies: overcoming costs stickiness and increasing corporate sustainability. Futur Bus J 7 , 31 (2021). https://doi.org/10.1186/s43093-021-00079-4

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Design for manufacturing and assembly methods in the product development process of mechanical products: a systematic literature review

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• Volume 120 , pages 4307–4334, ( 2022 )

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The design for manufacturing and assembly (DFMA) is a family of methods belonging to the design for X (DfX) category which goal is to optimize the manufacturing and assembly phase of products. DFMA methods have been developed at the beginning of the 1980s and widely used in both academia and industries since then. However, to the best of the authors’ knowledge, no systematic literature reviews or mapping has been proposed yet in the field of mechanical design. The goal of this paper is to provide a systematic review of DFMA methods applied to mechanical and electro-mechanical products with the aim to collect, analyse, and summarize the knowledge acquired until today and identify future research areas. The paper provides an overview of the DFMA topic in the last four decades (i.e., from 1980 to 2021) emphasizing operational perspectives such as the design phase in which methods are used, the type of products analysed, the adoption of quantitative or qualitative metrics, the tool adopted for the assessment, and the technologies involved. As a result, the paper addresses several aspects associated with the DFMA and different outcomes retrieved by the literature review have been highlighted. The first one concerns the fact that most of the DFMA methods have been used to analyse simple products made of few components (i.e., easy to manage with a short lead-time). Another important result is the lack of valuable DFMA methods applicable at early design phases (i.e., conceptual design) when information is not detailed and presents more qualitative than quantitative data. Both results lead to the evidence that the definition of a general DFMA method and metric adaptable for every type of product and/or design phase is a challenging goal that presents several issues. Finally, a bibliographic map was developed as a suitable tool to visualize results and identify future research trends on this topic. From the bibliometric analysis, it has been shown that the overall interest in DFMA methodologies decreased in the last decade.

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1 Introduction

The design for manufacturing and assembly (DFMA) is a family of methods belonging to the design for X (DfX) category which goal is to optimize the manufacturing and assembly phase of a product. DfX methodologies are used to improve specific aspects of the product under development. The X is generally substituted with the optimization goal, and these methodologies are used to support the product development process (PDP). DFA is a systematic procedure aiming at the reduction of assembly time through the following actions: (i) reduction of the overall number of components in a given assembly and (ii) elimination of critical assembly tasks [ 1 ]. DFM is an engineering practice that seeks the simplification of the manufacturing process for cost reduction of a given component through the following actions: (i) selection of raw material type, (ii) selection of raw material geometry, (iii) definition of dimensional and geometrical tolerances, (iv) definition of roughness, (v) characterization of specific shape constraints based on the manufacturing process, and (vi) selection of secondary processing such as finishing [ 2 ].

DFMA methods have been around for many years. The first DFMA method is dated back to the 1980s since it was noticed that a positive impact is obtainable on the overall costs if the manufacturing and assembly phases were challenged. Among the several methods developed on this aim, three approaches have been mainly used in both academia and industry: (i) Boothroyd and Dewhurst (B&D) [ 3 ], (ii) Hitachi [ 4 ], and (iii) Lucas method [ 5 ]. Despite the quite long history of this subject, only a few papers present a literature review about DFMA methods. For instance, Gao et al. [ 6 ], Ginting et al. [ 7 ], and Wasim et al. [ 8 ] proposed a review of DFMA methods in the building sector which shows different features compared with the mechanical products considered in this review. Regarding mechanical products, four reviews were focused on DFM methods [ 9 , 10 , 11 , 12 ], six on DFA methods [ 13 , 14 , 15 , 16 , 17 , 18 ], and four on DFMA methods [ 19 , 20 , 21 , 22 ]. By the analysis of these works, three main limitations have been identified. The first one concerns the fact that the majority of reviews are dated (conducted more than 15 years ago), and missing information about current DFMA methods and trends is noticed. The second one deals with the fact that some reviews have been published in conference proceedings and only limited outcomes are provided. Finally, the third limitation concerns the review methodology. The available reviews lack a systematic approach, not allowing the reproducibility and replicability of the review process. Although DFMA methods are widely used in both industrial and academic fields, there are no recent reviews on this topic for mechanical applications.

The goal of this paper is to provide a systematic review of DFMA methods applied to mechanical products. The systematic review was conducted to collect, analyse, and summarize the knowledge acquired until today, as well as to identify future research areas, following the results of relevant research works on this subject to answer specific research questions. Two clusters of research questions were identified by the authors: general questions (GQs), and focused questions (FQs). Each cluster presents a list of questions that are used to drive the review and to identify specific topics associated with the DFMA subject. The following topics were covered by this review: (i) the industrial fields and the type of products covered by DFMA methods, (ii) the mapping of the DFMA methods in relation to the product development phases, (iii) the identification of trends and challenges for DFMA methods, (iv) the metrics used to analyse the results of DFMA methods, (v) the design tools implemented in compliance with DFMA methods, and (vi) the use of Industry 4.0 enabling technologies in the development of DFMA methods.

In the following section, Sect. 2 , the method proposed to perform the systematic mapping is described in detail along with the chosen research questions. Then in Sect. 3 , the outcome of the performed review is reported showing data used to answer the research questions. Section 4 explaining the limitations of the proposed review is presented, followed by a discussion of the obtained results in Sect. 5 . Finally, the last section, Sect. 6 , summarizes the outcome of the review and highlights future research trends for DFMA methods.

2 Materials and methods

The method used to conduct the study is composed of five phases: (i) definition of the research questions, (i) definition of the search process, (iii) definition of criteria for article selection, (iv) execution of data extraction and classification, and (v) execution of the analysis The following part of this section describes each phase in detail, including how the literature review was performed.

2.1 Definition of research questions

For the development of this review, the following questions were obtained with a top-down approach. Research questions concerning DFMA methods were divided into two clusters GQs and FQs. The first cluster gives an overview of the research field, providing specific application fields and design phases in which DFMA methods have been applied the most, including future challenges of the studies that employ DFMA methods. The second cluster analyses technical aspects of DFMA methods, such as the method type, the tool used for computational reasons, and if Industry 4.0 enabling technologies were implemented. Table 1 reports the research questions defined for this review.

2.2 Definition of search process

Since the first research activities and applications about DFMA methods are dated back to the early 1980s, this review was conducted considering all papers published between 1980 and 2021. The research process was performed on four databases: (i) Scopus, (ii) Elsevier, (iii) Taylor & Francis, and (iv) Emerald, which were considered the most coherent publishers in the engineering sciences by the authors. The queries were filtered by authors, abstract, and keywords, when possible. Table 2 summarizes the filtering items used for each database.

The definition of keywords was performed iteratively due to the high number of papers resulting from the first database querying. To obtain a manageable number of articles, three filtering steps were performed as reported in Fig.  1 . Initially, general keywords such as “Design,” “Manufacturing,” “Assembly,” and “for” were collected with the operator “AND.” Moreover, to broaden the research and mitigate possible errors, synonyms were considered (i.e., “Manufacturability,” “Production,” “Manufacture,” “Assemblability,” and “Installation”). The second step was performed to narrow results, and the two keywords “Assembly” and “Manufacturing” were combined using the operator “AND” (e.g., “Assembly AND Production,” “Assembly AND Manufacture”). Finally, the last filtering step consisted in the introduction of new keywords to reduce the overall number of results trying to target only mechanical-related articles. The acronyms “DFA,” “DFM,” and “DFMA” were added to the previous keywords with the operator “AND.”

Filtering process and refinement steps

2.3 Definition of criteria for article sorting

After the initial search process, articles were skimmed with a three-step process: (i) identification and elimination of duplicated articles, (ii) use of global exclusion criteria to select articles related to the field of interest, and (iii) use of specific criteria (SC) to select only the most representative articles. Both criteria (GC and SC) used for the exclusion process are reported in Table 3 .

A quality assessment process was not performed, and all the retrieved papers were kept for the review process. At the end of the article selection, 141 articles were kept and analysed. The overall selection process is represented in Fig.  1 .

2.4 Execution of data extraction and classification

Data extraction and classification allowed for retrieving key information from the articles selected for the analysis using a structured framework. The data extraction framework (Table 4 ) is composed of items according to the type of research question they are answering.

2.5 Execution of analysis

The execution of analysis was performed with the help of the framework provided in the previous step (Table 4 ). In relation to the general questions, the first topic concerns the identification of the specific field in which DFMA methods have been applied for years. Fields were divided into general (i.e., electronic, and mechanical) and specific (i.e., sensors, automotive aerospace, industrial). To further support this classification, the product complexity was identified. In this paper, a product is considered complex if it has a medium-long lead time and it is difficult to handle (i.e., due to weight, dimensions, or a high number of components), while a simple product has a short lead time and is made by few components (i.e., less than sixty). The second topic concerns the identification of the design phase in which DFMA is applied (i.e., conceptual design, embodiment design, and detail design). The detail design phase presents the most accurate and complete information regarding the product, while the conceptual design phase presents most generic data (e.g., functional information, product architecture). The third topic concerns the identification of future trends and challenges of DFMA methods in relation to the application field, product complexity, and design phase previously investigated.

On the other hand, in relation to the focused questions, the first topic refers to the DFMA method type, which can be quantitative or qualitative. A method is considered quantitative when it provides a numerical evaluation (e.g., the B&D DFMA method), while a method is qualitative when it provides suggestions and guidelines, not directly linked to numbers or mathematical equations (e.g., heuristics, guidelines). The second topic tackles the computational tool used to perform DFMA analysis. Three different types of tools were identified for this purpose: spreadsheets, software, and graph. The third topic analyses the application of advanced technologies with DFMA methods (i.e., the ones that currently characterize the enabling technologies of Industry 4.0).

2.6 Bibliometric analysis

A bibliometric analysis was performed to understand when and where papers regarding DFMA methods have been published. The analysis was performed considering four decades, and the overall result is shown in Fig.  2 . An exception was made for the last decade (i.e., D4) which considers a time span ranging from 2010 to 2021 to include all the latest publications. The first decade includes only four papers and it appears to be the lowest in terms of publications, while the second decade presents a high number of papers (48). The third decade presents 30 papers published for the DFMA field, and finally, the latest decade presents the highest number of papers, which is 59. Although the graph shows a scattered distribution of papers, ranging from 0 to 8 for each year, the mean value for the last three decades is approximately 4.3. This result highlights a homogeneous distribution of paper over time about DFMA.

Number of papers vs. years

Both paper types published in journals and conference proceedings have been considered. Journals guarantee a stricter review process than proceedings following the time given to reviewers and the accessibility to scientific databases. Moreover, journals present more structured and mature research than conference proceedings. Additionally, a higher number of publications on conference proceedings indicate a considerable interest, since they present ongoing activities from different practitioners.

3 Results of the literature review

In this section, results of the literature review are presented following the two main groups of research questions previously identified.

3.1 Results related to the general questions

To answer the first general question, only papers in which a case study is presented have been analysed. The aim is to identify the industry’s field in which DFMA methods have been applied and the type of product analysed as a case study. On the other hand, to answer the second and the third general questions, all papers except reviews were considered. The aim is to understand in which phase DFMA methods are mainly applied, to identify the advantages/disadvantages of each design phase and to derive future research opportunities in the DFMA field.

3.1.1 Field of application and products analysed by DFMA methods

At the beginning of DFMA method development (early 1980s), articles were focusing on the conceptualization and description of DFMA methods, providing academic and exemplary case studies. During the 1990s, the application of DFMA methods in industries increased exponentially, particularly in the mechanical field. Starting from the second decade (D2), several case studies were provided to demonstrate the applicability of DFMA in mechanical and electro-mechanical products, and the same trend was confirmed in the following decades (D3 and D4). It is worth noting that most of the publications giving case studies have been implemented in the industrial field. The reason lies in the fact that several DFMA methods available in the literature are tested on generic products made of few components (i.e., dust filters, stapler, boiler) to validate the methods and their reliability. The number of papers presenting case studies in the automotive and aerospace fields is well balanced. Products analysed with DFMA methods are varying from sub-assemblies of a car (i.e., the suspension system, brake and clutch) to aircraft systems (i.e., pilot instrument panel, contactor assembly). Only a few articles tried to tackle the assemblability of a whole product; among them, Thompson et al. [ 23 ] tried to point out the relation between DFMA rules and late design changes in high-speed product development (i.e., circulator pumps for the commercial building services market). Gerding et al. [ 24 ] tackles the problem of implementing DFMA rules in long-lead-time products (i.e., aircraft), while Barbosa and Carvalho [ 25 , 26 ] proposed DFMA rules to optimize the assembly phase of an aircraft through re-design actions. Figure  3 shows the distribution of papers according to the type of product, the general field, and the specific field of application.

GQ1 data distribution

To understand the interest of the topic over time, the publications’ year was analysed together with the type of publication (i.e., journal or conference proceeding). Results of this analysis are summarized in Fig.  4 . Papers describing DFMA applications on both complex and simple products have increased over the years. It is interesting to notice that most of the articles proposing DFMA methods for complex products have been published in the last two decades (D3 and D4). This trend may be justified by several reasons. The first one concerns the fact that more and more industries are focusing on reaching a global improvement of their product, making the application of traditional DFMA challenging since the whole system must be considered. Another major factor in the development of DFMA methods for complex products concerns the increment of processing power that allows designers and engineers to handle a high amount of data in a limited timeframe, widening the boundary of their optimization problem from sub-parts to the whole system. The study of DFMA methods applied to simple products in the last three decades has increased as well. However, for the last decade (D4) most of the papers are published in conference proceedings and they present applications of already well-known DFMA techniques on different systems. Despite these works being useful to increase the number of case studies where DFMA methods are applied, they cannot be considered as research advancement in the DFMA methods. Other works published in conference proceedings are trying to extend DFMA principles in several ways. For example, Esterman and Kamath [ 27 ] attempted to apply DFMA to the improvement of assembly lines, Wood et al. [ 28 ] and Nyemba et al. [ 29 ] provided new design rules to cope with constraint production of the developing countries, and finally Favi et al. [ 2 ], Hein et al. [ 30 ], and Gupta and Kumar [ 31 ] included new principles and criteria for multi-objective analysis (i.e., cost, sustainability).

Distribution of papers per decade in relation to simple and complex products

The overall data collected about this topic are summarized in Table 7  in the Appendix . From the performed analysis, DFMA methods have been mainly applied on simple products or sub-assemblies, in which all parts are made with traditional production technologies (i.e., fusion, sheet metal stamping and bending, forging). DFMA analysis evaluates assembly solutions adopted in the analysed products. Assembly solutions are generally bolted joints, more rarely welded or riveted joints. The main goal of these analyses is to understand if it is possible to reduce the number of components which, typically, leads to a reduction of assembly time [ 19 ]. As an outcome, the typical product analysed using DFMA techniques is a simple product assembled manually with bolted joints made of less than 60 parts. Another interesting result concerns the fact that sub-assemblies are considered rather than the whole product. This result leads to the application of DFMA methodologies in a limited context (i.e., the companies which are designing and manufacturing sub-assemblies) making effective the benefits of DFMA for suppliers. In this scenario, each module (sub-assembly) is assembled with a specific assembly technology, making the overall analysis easier to manage. For instance, a car engine is assembled with bolted joints and chassis are assembled with welding technologies. If the assembly technology varies, then the DFMA analysis becomes more challenging and, consequently, the overall final improvement might not have an elevated positive impact as the sub-systems improvements might have.

3.1.2 Product design phase challenged by DFMA methods

According to Pahl et al. [ 32 ], the PDP process can be divided into conceptual design, embodiment design, and detail design. For each phase, different information and tools are available to support designers in the definition of the product. The conceptual design phase represents the initial phase of the product development process, in which only general information (e.g., product functions, product architecture) is available. The embodiment phase represents a more mature phase of a project in which a preliminary product layout is available. Generally, this design phase is linked with the use of 3D CAD drawings. Finally, the detail design phase represents the step with a higher level of detail. Specific information is available at this phase, such as the number and type of screws, assembly procedures, assembly sequence, and takt time. In this phase, detailed drawings are made to fully describe the product for the manufacturing process. Together with the information granularity, also the cost of changes varies according to the design phase in which modifications are introduced. With the aim to analyse this topic, all papers except reviews have been considered. The analysis of the literature shows that DFMA methods are mainly used during detail and embodiment design phases (Table 7  in the Appendix ). Indeed, considering the most spread DFMA methods (i.e., B&D and Lucas method), the analysis is performed starting with detailed design information. Among the analysed papers, a large part of them tried to use DFMA methods at the embodiment phase by reducing the need for specific information. For instance, Sanders et al. [ 33 ] proposed a knowledge-based system to optimize products without detailed information, while Samadhi et al. [ 34 ] tried to develop a fully automated DFMA method, linked to a 3D CAD modeller, enabling to extract data related to the product under development. The application of DFMA methods at the late design phase is in line with the idea of DFMA since most of the methodologies have been developed as a systematic approach, whose aim is to optimize the product through different design iterations (incremental improvement through product re-design). However, several problems arise working at the late design phases such as the high cost of change. Since the beginning of the advent of DFMA methods, some studies tried to move the analysis from the detail design phase to the conceptual design phase. Among these, the paper proposed by Rampersad [ 35 ] was one of the first to investigate DFMA methods from a relational point of view, to understand how design variables affect product assembly. A more recent attempt was performed by Emmatty and Sarmah [ 36 ] that tried to merge DFA and DFM techniques with product architectures analysis. Across the collected works, only two works proposed to integrate the TRIZ methodology and the DFMA to widen the solution space, which is a typical task of conceptual design [ 37 , 38 ]. The typical output of DFMA methods in the conceptual design phase is a product architecture with optimized performance in terms of assembly. Functional modules, interconnections, and related parameters are considered in the DFMA analyses to identify installation and assembly issues. For instance, the position, the attachment points, the overall number of the functional modules, and/or the interface route among modules are some of the parameters considered in the developed DFMA methods conceived for the conceptual design phase. Hence, DFMA analysis performed at the conceptual design phase focuses on the module rather than the physical components and provides product optimization through module arrangement and layout inside the product (i.e., product architecture). When DFMA analyses are conducted at the detail or embodiment design phase, the typical output is again a product with optimized assembly performances, but the focus concerns the components/parts. DFMA tools aim at improving the product assemblability by reducing the overall number of components, minimizing the number of fixations (i.e., screws, rivets), standardizing the type of fixations, reducing the part re-orientation during the manual operations, and choosing the most appropriated manufacturing technology among others. Hence, DFMA analysis performed at the embodiment/detail design phases focuses on the physical component providing a product optimization through the improvement of component shape, features geometries, and manufacturing aspects. It is interesting to notice that in the last decade, the efforts to propose DFMA methods applicable at the conceptual design phase have been increased for both simple and complex products.

3.1.3 Future challenges to address by using DFMA methods

From the extracted data, most of the papers are dealing with the improvement of simple products at the detail design phase. The analysis shows also how the DFMA evolved integrating new objectives (e.g., ergonomic and environmental aspects) and multi-attribute analysis. On the other hand, the research activity related to DFMA methods shifted towards the analysis of complex products, and an increased interest in the conceptual design phase was noticed. To cite a few, Remirez et al. [ 39 ] tried to adapt the B&D DFMA methodology to tackle the assembly issues of a solar tracker, while Mora et al. [ 40 ] adapted the design structure matrix method to work with large size products (i.e., elevators, wind turbines, solar plants, pilot plants, or petrochemical facilities). With the same aim Formentini et al. [ 41 ] provided a method to collect design guidelines to optimize the aircraft architecture at the conceptual design phases. The transition of DFMA analysis towards the early design phases emerged as a trend to be investigated in future years. This trend emphasizes the need to shift the DFMA paradigm by establishing a systematic optimization method that may be used at the conceptual stage, when degrees of freedom are larger, to achieve the right first time design [ 19 ], before moving on to the later design phases. Another aspect that characterizes DFMA studies of products with a certain complexity is the high number of data required for the analysis and computational time needed to perform the analysis. To summarize the outcome of the literature analysis, an increasing interest in the development of DFMA methods for complex products is raising in the scientific community. However, there is no evidence stating that DFMA methods provide better benefits to complex rather than simple products. Based on the revised papers, a high number of manuscripts presented applications of DFMA methods on simple products. This trend may be justified by the fact that on simple products, DFMA results can be validated and tested through product prototypes. Moreover, the application of DFMA analysis on simple products is in line with the concept of incremental innovation. In this respect, DFMA techniques were applied to product sub-systems (or sub-assemblies, which indirectly provides an overall optimization of the product. The application of DFMA analysis on the entire product, especially when it is complex, may generate different outputs and might lead to radical innovation in terms of assembly performances. To date, there is no evidence about a direct comparison (e.g., DFMA index assessment between a complex product developed with DFMA criteria and the same product in which the DFMA principles were applied to sub-assemblies. This lack lies in the needs of industry where usually sub-systems are provided by different suppliers,thus, there is no interest in investigating the product assemblability as a whole system. This perspective is currently not addressed within the literature and represents an opportunity for further research. Another upcoming challenge for DFMA is the need to integrate DFMA analysis with other design aspects (multi-objective analysis, creating engineering design methodologies that consider multiple aspects. For instance, ergonomic analysis is important to guarantee the assembly optimization of the product. Boothroyd [ 19 ] already considered the ergonomic aspect in his approach,however, it was considered in relation to the operator in the assembly line, where small products are handled. Moving towards bigger and complex products, the assembly process requires the operator to actively adapt to the working space and environment, and different ergonomic parameters need to be considered, such as working position, the access to the place where activities are performed, and ergonomic operator posture among others [ 42 ].

3.2 Results related to the focused question

To answer the focused questions, only a proper subset of papers was analysed for each topic with the aim to explore specific aspects related to the type of DFMA methods. These specific topics concern the type of tools used for the analysis, as well as the enabling technologies used to implement DFMA in modern industries.

3.2.1 Qualitative vs. quantitative DFMA methods

DFMA methods can be clustered into different categories: qualitative and quantitative. A method is considered quantitative when it provides numbers and indicators (i.e., metrics) to evaluate the goodness of a product from the assembly and manufacturing point of view. According to this definition, quantitative methods have been widely used as engineering design tools [ 11 ]. An example of the DFMA quantitative method is the B&D method. On the other hand, a method is considered qualitative when it provides an evaluation of the product manufacturability and assemblability using design practice derived from experience. Qualitative methods are usually providing design suggestions, rules, and guidelines without the adoption of numerical metrics. Dealing with the study of qualitative vs. quantitative DFMA methods, the analysis was performed looking at all papers except the reviews and papers oriented to the plant management. Results show that three quarters of the papers are proposing quantitative approaches, while only a one quarter studied qualitative approaches. Among all, only two papers tried to provide a method that can be considered both qualitative and quantitative [ 43 , 44 ]. Table 5 reports the main types of information required to perform DFMA analysis, in relation to quantitative and qualitative methods. Despite some inputs being shared among quantitative and qualitative methods (e.g., number of parts), the main outputs are different.

From the performed analysis, the most-used inputs for DFMA indices are assembly time (s), material cost ($), and number of parts (#). DFMA indices for quantitative methods have all the same root, which is providing a score based on the identified product parameters (input data). According to the type of parameters and the developed method, the DFMA index can assume a different meaning. For instance, the most popular DFA index from the B&D approach (also known as design efficiency) is computed by the following equation [ 19 ]:

NM = theoretical number of parts is an estimation concerning the number of essential parts of the product derived by the optimization process proposed by the method,

TM = total assembly time is the overall assembly time of the product measured with experimental tests.

The DFA index gives an overall assessment of the product assemblability performance (dimensionless index). The DFA index can be applied to different products, and it is based on values derived from standardized tables. Differently from DFA index, the total grade indices allow considering both DFA (total grade of the assembly) and DFM (total grade of the part) [ 45 ]. The method identified a list of product parameters for the manufacturing assessment (billet, work material, features, machine accessibility, etc.) and for the assembly assessment (i.e., billet dimension, part handling, assembly fixtures, tolerance and clearance) providing a weight for each parameter (from 0 to 10). Following a value engineering approach, a score of 0 is assigned if the parameter is not critical for the manufacturing/assembly, while 1 is assigned if the parameter affects the manufacturing/assembly process. Total grade indices are obtained by multiplying the weight of each parameter with the score associated with the considered parameter and finally by making an overall sum. The lower the total grade of the part and the assembly is, the more efficient the product is from the manufacturing and assembly perspectives. Both DFA index and total grade of the assembly/part are quantitative.

Regarding qualitative DFMA methods, the general outcome is a list of items (i.e., rules, graph, guidelines) in which design suggestions to improve product manufacturability and assemblability are collected. For instance, the design structure matrix (DSM) is a well-known tool to represent product architectures. DSM representation helps designers to create products with enhanced manufacturing and assembly properties. Qualitative DFMA methods can also provide a performance index, which is used to assess the improvement obtained by the implemented design actions. According to the method used, the performance index is derived using different inputs (e.g., the initial number of components/final number of components, initial cost/final cost) and it provides a rough estimation of the benefits introduced by the implementation of the design guidelines.

Regardless the fact that a DFMA index is quantitative or qualitative, the analysis showed that DFMA indices can be divided into two groups: time-based and feature based. Time-based DFMA indices rely on tables to convert time-related assembly parameters into scores. Tables are derived through extensive experiments. The main drawback of these indices is the complexity to personalize these tables on a specific product (e.g., complex products). On the other hand, feature-based DFMA indices rely on tables to convert assembly-related features into scores. Tables are derived through knowledge formalization techniques. These types of indices allow personalising tables on the product analysed but require a great effort to be set up and they may be subjected to bias. As an outcome of the literature review, the definition of a general DFMA index which can be adopted for every type of product or system can present several issues. A trade-off among analysis accuracy, available time, and availability of data must be reached and the proper DFMA index selected accordingly.

Another interesting area of investigation regards the type of DFMA method versus the design phase at which it is used. Figure  5 presents the data collected from the analysis of the qualitative/quantitative DFMA methods versus the design phase.

Distribution of quantitative and qualitative methods in relation to the design phase

Quantitative methods appear to be widely used at the late design phase. This result is in line with the available information, which is mainly numerical. Moving towards the early design phase (i.e., conceptual design), a great effort was done to develop new methods to study manufacturing and assembly aspects with less information. Among the DFMA methods focusing on the early stage of the design process, the majority of them are quantitative. This is an interesting outcome since no quantitative information is available in this design phase. For instance, Jung and Billatos [ 46 ] examined some elements of intelligent design systems to assess manufacturability of a product through the development of a knowledge based expert system for assembly. The knowledge base has been acquired from design for assembly along with axiomatic design concepts with emphasis on the conceptual design stage where the structure of the product as a whole is considered. Dagman and Söderberg [ 47 ] proposed to use axiomatic design principles to analyse and improve product architecture by the assessment of manufacturing, assembly, and disassembly parameters during the early design phase. Both methodologies, which are based on axiomatic design, are quantitative and use matrices to link functional requirements with design parameters. Favi et al. [ 48 ] proposed a method to perform a multi-objective optimization in terms of assembly, materials, processes, costs, and times at the conceptual design phase. The analysis was performed at the product architecture level, using product modules and design solutions derived with the help of the morphological matrix. In the mentioned work, all parameters required for performing the DFMA analysis were supposed from an already existing product. A similar approach was proposed by Formentini et al. [ 41 ], Favi et al. [ 49 ], and Bouissiere et al. [ 50 ] for the study of product architecture assembly performances for systems installation of a commercial aircraft.

3.2.2 Tools used to support DFMA methods

Concerning the development of engineering tools able to support the DFMA analysis of mechanical products, only a subset (74) of papers addressed this topic. Three different types of tools were identified by the analysis of the literature: graph, software, and spreadsheets. Each tool was further classified according to the aim of the analysis: (i) redesign suggestions, (ii) guidelines collection, (iii) metrics computations, and (iv) method integration. Redesign suggestions tool allows at the identification of redesign actions to improve the assemblability and manufacturability of the product under analysis. Guideline’s collection tool aims at transforming implicit knowledge into explicit one. Metric computation tool consists of the automatization of the computation of assembly and manufacturing parameters, and method integration tool describes the link with other engineering methods (i.e., FEM analysis). From the performed review, a dedicated software system is the main used tool, followed by spreadsheets and graphs (see Fig.  6 ). By the analysis of the type of software, research works presenting case studies are more willing to use commercial DFMA software (e.g., B&D commercial software) than an ad hoc developed software tool. Among commercial software tools, most of them were developed for metrics computations (i.e., assembly time, required assembly steps). The same trend is noticed for the spreadsheets. Only two papers are making use of graphs as tool for DFMA analysis. For example, Wu and O’Grady [ 51 ] suggested to use Petri-Nets to model CE aspects and make the application of DFMA techniques leaner, while Hsu and Lin [ 52 ] used graphs to integrate DFA, assembly functional presentation, and problem recommendation–driven mechanism. According to the performed analysis, spreadsheets and ad hoc software appear to be the most used tools. The use of spreadsheets lies in the accessibility and straightness in their use. They are the best choice when a method is not consolidated and only a few analyses were performed. Additionally, the software has been widely used to implement the DFMA method. Two types of software have been identified in the analysis: (i) ad hoc developed software and (ii) commercially available software. Generally, the development of software implies a greater effort in terms of time than commercial software or spreadsheets. The commercial software tools identified during the review concern both design tools and simulations tools (i.e., DFMA ® Boothroyd Dewhurst Software, Tecnomatix Dynamo, and Flexible Line Balancing Software). In other cases, the analysis was performed retrieving information from CAD tool, but no information was provided regarding the DFMA software used [ 53 , 54 ]. Moreover, it is interesting to analyse the use of tools versus the type of publication. Figure  6 shows that the use of spreadsheets is higher in the conference proceeding publications than the journal ones. Spreadsheets are mainly used to perform isolated analyses, while ad hoc software tools were developed to include methodological aspects within the novel DFMA framework which are more suitable for journal publications. Table 7  in the Appendix reports a summary of the outcomes related to this topic.

Tool vs. number and type of publication

3.2.3 Industry 4.0 enabling technologies challenging DFMA methods

The advances in Industry 4.0 provide both challenges and opportunities for digital manufacturing and assembly systems. Industry 4.0 aims at the development of a new generation of smart factories grounded on the manufacturing and assembly process digitalization. Most of the Industry 4.0 enabling technologies are related to digitization, data management, and connectivity, and they are dependent on solid data acquisition technologies. For the purpose of this review, not all the enabling technologies have been considered (Fig.  7 ) due to different reasons.

Enabling technology for Industry 4.0 (blue included in the review; red excluded)

The “additive manufacturing” technology was not studied since design methods called “design for additive manufacturing” have been specifically developed to consider this technology and they are not the goal of this review. The interested reader can find further information regarding DFAM methods in the review proposed by Wiberg et al. [ 152 ]. “Real-time optimization” and “cyber-physical systems” were not considered since they are mainly focusing on plant management rather than product design. For the aim of this review only “machine learning and AI,” “virtual and augmented reality,” “intelligent/collaborative robotics,” and “Internet of Things and cloud computing” were examined. In addition, a more detailed list of tools was identified for the technology “machine learning and AI,” including (i) expert system, (ii) fuzzy logic, (iii) genetic algorithm, and (iv) constraint-network approach. Among all the papers, only a few papers addressed the technology “machine learning and AI” proposing the use of the mentioned tools for the development of DFMA methods. The common goal of the analysed works is to eliminate the need for expertise to perform an assembly oriented design choice. The use of mathematical artefacts (e.g., artificial intelligence, genetic algorithms, expert system, fuzzy logic) allowed the collection of existing knowledge and the development of an automated system for knowledge sharing. Referring to the technology “virtual and augmented reality,” the idea was to use this technology in helping designers with the mock-up creation at the embodiment design phase facilitating the analysis of assembly operations (i.e., ergonomics). As regard the technology “Internet of Things and cloud computing,” only two discussed the applicability of these technologies for the DFMA analysis. Both manuscripts tried to move DFMA analysis in a cloud environment to get access to more case studies, more data, and the possibility to share assembly/manufacturing knowledge on past projects. Finally, even though there are several papers presenting methodologies to consider automatic assembly, no papers were found for the technology “intelligent/collaborative robotics.” Automatic assembly was generally not analysed through the means of DFMA, and the design of robotic cells and lines is usually customized to build a specific product and/or product family [ 104 ]. Industry 4.0 technologies brought a new paradigm for industries and manufacturing companies including a different way to collect, process, and elaborate data, as well as the production of customized products. The idea ground pinning the adoption of these technologies for DFMA purposes is to reduce the risk of implementing wrong design actions, and it helps to select the right modification among a pool of options. For example, Internet of Things can support DFMA analysis collecting data through several sensors placed directly on the product or the assembly line. Machine learning techniques can make use of past data, and the analysis of implemented design actions to suggest the right design action to implement in a given time. Machine learning processes can be used also to drive the product optimization following a multi-objective analysis to address different design goals (i.e., DfX). The cloud computing can open new possibilities in terms of data sharing by using virtual servers to collect and process data. The idea of cloud computing is in line with the concept of open manufacturing introduced by Kusiak [ 153 ] allowing different stakeholders to share data and optimize the manufacturability of their products in different contexts and countries.

As previously introduced, virtual and augmented reality can enable the investigation of ergonomic aspects during the assemblability process and the optimization of manual assembly operations. Exploring the product in a virtual environment, it is possible to highlight ergonomic issues (i.e., wrong operator position, impossibility to access to a particular product area) and solve them before the product is finalized. Moreover, operators can be trained before the product is physically available, reducing the time required for the in-process learning curve, cost of training, and consequently time to market.

By following the bibliometric analysis, the majority of works introducing Industry 4.0 enabling technologies are dated in the second and the third decades (D2 and D3). At that time, the concept of Industry 4.0 had not yet been formalized; therefore, all these studies can be considered as preparatory for the paradigm shift brought by the advent of Industry 4.0. When the concept of Industry 4.0 was introduced (beginning of 2010), the application of enabling technologies in relation to manufacturing and assembly aspects took a different research angle (from the product to the production site, i.e., plant management and production). This outcome has been validated by performing quick research with keywords “Industry 4.0 Design for Assembly” on main scientific databases. The retrieved papers are not focused on the design aspects of product assemblability anymore, but rather on the management of the assembly line and production site. In conclusion, traditional DFMA methods were not deeply investigated in relation to the Industry 4.0 enabling technologies.

4 Limitations

The literature analysis performed and presented in this paper shows few limitations that may affect the scope of the results and deserve to be introduced. The research process was performed systematically, identifying parameters and criteria to mitigate possible bias. The main limitation is identified by the adoption of a filtering process which uses criteria defined by the authors. For example, the exclusion criteria SC1 (articles not available for download) is not scientific and repeatable. In fact, according to the type of database and the institution’s accessibility, some articles excluded by the authors may be available for other users. In addition, this review focuses on scientific articles (both journal and conference papers), not considering, for example, thesis, book chapters, technical reports, commercial tools, and patents. Since DFMA is considered an applied science in the field of engineering, some interesting works developed outside the boundaries of the academic community could be excluded from this analysis. Finally, due to the high number of articles found, no other sampling techniques (e.g., snowball sampling) have been used to derive articles other than the one described.

5 Discussion

Through the analysis of the results related to general questions, it is possible to draw a discussion about the DFMA research done during the years. The critical analysis of results showed that DFMA methods have been mainly used for products made of few components and assembled with the same technology (i.e., bolted, welded). This outcome is in line with the idea of the early DFMA methods (e.g., Lucas, B&D) where an analysis of the assembly process is required for a given product to understand if can be optimized by eliminating/merging parts. Another interesting result considers the area in which DFMA methods are applied. Since this review is focused on DFMA methods for mechanical products, most of the presented case studies refer to the mechanical and electro-mechanical fields. In this scenario, only a few papers tried to tackle complex products (i.e., long lead time, heavy products, and characterized by a high number of parts). Several limitations were observed when a traditional DFMA method is applied to complex products such as the management of a high number of information as well as the inconsistency between manufacturability and parts integration which is the cornerstone of the DFMA.

The critical analysis of results in relation to the focused questions showed that regardless of the design phase at which DFMA methodologies were implemented, a continuous effort to derive quantitative methods was done since the beginning. Quantitative indices allow determining the performance of manufacturability and assembly for decision-making purposes. In addition, the use of numerical indices leads to a possible comparison between design alternatives, assessing the benefits introduced by novel design solutions. It was observed that the use of metrics and indices is suitable for the late design phases (embodiment and detail design) when numerical parameters are available with lower uncertainty. On the other hand, the assessment of quantitative results during the early phases of the PDP (conceptual design) requires defining specific boundaries and criteria for the field of interest. This limitation may affect the design solution space and the overall optimization process. This result leads to an open question “Is it possible to create quantitative DFMA methods applicable at the conceptual design phase, without limiting the available solution space?”.

The bibliometric study revealed the evolution of DFMA approaches’ interest through time (Fig.  8 ). The analysed works covered both conference proceedings and journals, showing an active interest in the subject by industries and academia. Results show that D2 and D4 present the highest production of papers. For the D3 decade, it seems that the interest in the DFMA subject decreased. This trend is primarily caused by the change of topics and paradigms associated with DFMA, creating a pool of methods very similar but with different names (i.e., installation, system integration, design for additive manufacturing). In the recent decade (D4), there was a rise in the overall number of publications compared to the previous periods. The reason may be the increase in publication rate in the scientific world; indeed, the National Science Board reported a study showing that the global research output grew about 4% annually over the last 10 years [ 154 ]. In conclusion, it is hard to claim that the research interest in DFMA methods increase in the last decade compared with the previous ones.

Overall distribution of papers (journal and conference proceedings) per decades

A map was developed utilizing a bubble graph to analyse and show interest in the DFMA issue through time and discover future trends (Fig.  9 ). The considered topics are collected in Table 6 .

Bubble graph results (research topic share vs. research topic growth potential)

The size of the bubble represents the total number of publications for each topic during the period under consideration (i.e., decade D4). The Research Topic Share (RTS) is computed considering the overall number of papers divided for the number of papers of the last decade for a given topic. The Research Topic Growth Potential (RTGP) was computed by applying the least square method in relation to the number of publications per topic and year of the last decade (i.e., decade D4).

The graph is divided into two areas. The right side collects topics that have not been widely studied in the literature but are of high interest, while the left side reflects topics which are losing interest. According to the bubble graph, topics which have potential interest for further investigation are the topics T2 and T6 (i.e., DFMA methods applied to complex products and quantitative DFMA methods). The bubble size of T2 is small, and only a few papers are present in the literature that describes DFMA methods applicable to complex products. However, although many publications in the literature provide quantitative approaches (large bubble), this topic remains of interest, and the bubble T6 is on the right side of the graph when compared with qualitative methods (bubble T7). Another topic which is gaining interest is the development of DFMA methods applicable at early design phases (i.e., conceptual phase). This is represented by the bubble T3, which is small in size (i.e., few papers available in the literature) but located on the upper part of the right side of the graph. However, there is still a strong interest in DFMA methods applicable at late design phases (bubble T4 — embodiment and detail) confirmed by the number of papers developed on this topic. DFMA method applicable to simple products (T1) is a topic that is losing interest. Finally, it appears that the connection between DFMA methodologies and CAD systems is no longer of importance, and only a few papers in the last decade have been published on this topic. The reason could be technical and linked with the advent of the CAD systems that started to become popular at the beginning of the 1990s when numerous attempts were made to combine DFMA analysis with CAD systems. CAD tools are now widely used engineering systems for manufacturing industries, and research has shifted to other areas.

6 Conclusion

DFMA methods are widely used and well known in industries as in academia. To the best of the authors’ knowledge, no recent review on this topic was found, and the only papers that proposed a review of DFMA methods are dated and missing systematic analysis. The goal of this paper is to provide a systematic review of DFMA methods in the field of mechanical design. The review was conducted following the systematic approach. The papers were gathered from four databases (Scopus, Elsevier, Taylor & Francis, and Emerald), and a filtering approach was developed to exclude common review paper flaws. The obtained articles were categorized and analysed to answer the research questions proposed. Results show that DFMA methods have been mainly applied to simple products during the late design phase. This trend is in line with the early aim of DFMA methods, which is the optimization of product manufacturability and assemblability by considering a given technology. A few works attempted to shift the use of DFMA approaches from detailed to conceptual design phases. With this aim, it is required a change in the DFMA paradigm, moving from a systematic approach to a First Time Right method. The main tools used to do DFMA analysis are spreadsheets and ad-hoc software, which are often linked to CAD systems. Only a few authors have investigated the adoption of enabling technologies for Industry 4.0 for developing new DFMA approaches, such as artificial intelligence and virtual reality. This result leads to an important outcome which is the possibility to close the gap between design and manufacturing departments in modern industries following the Industry 4.0 paradigm. According to the articles reviewed, it is worth noting that performing DFMA analysis early in the design process could result in benefits such as increased solution space. Finally, research interest in DFMA approaches has dropped significantly in recent years, and this field needs to be revitalized. There are two possible reasons for this finding. The first one concerns the loss of appeal for young scholars in developing DFMA for consolidated manufacturing and assembly technologies. In this regard, the focus of researchers moved towards new technologies (i.e., additive manufacturing), and new challenges (i.e., system integration). The second one concerns the adoption of novel approaches able to suggest the right design the first time, proposing a multi-objective optimization of the product when the manufacturability is only one of the targets to be optimized.

The proposed work presents some limitations typical of review studies. The main limitation is identified in the filtering process. The exclusion of non-academic works (i.e., technical reports, commercial software) might have had led to the exclusion of relevant papers.

Availability of data and material

Not applicable.

Code availability

Change history, 20 july 2022.

Missing Open Access funding information has been added in the Funding Note.

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Formentini, G., Boix Rodríguez, N. & Favi, C. Design for manufacturing and assembly methods in the product development process of mechanical products: a systematic literature review. Int J Adv Manuf Technol 120 , 4307–4334 (2022). https://doi.org/10.1007/s00170-022-08837-6

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Analysis of the COVID-19 pandemic’s impacts on manufacturing: a systematic literature review and future research agenda

Andrea bacchetti.

1 RISE Laboratory, Università degli Studi di Brescia – via Branze 38, 25123 Brescia, Italy

Dmitry Ivanov

2 Department of Business and Economics, Berlin School of Economics and Law, 10825 Berlin, Germany

The COVID-19 pandemic has affected manufacturing companies and necessitated adaptations of firms’ operations. Despite the increasing interest in this subject, a scarcity of systematic analysis can be observed. The present study systematically reviews the existing research on the COVID-19 pandemic concerning the manufacturing industry. This paper aims to highlight the main impacts of the COVID-19 pandemic on the manufacturing sector from the operations management perspective, the practical adaptation actions, and future research opportunities. Open research questions and directions for further investigation are articulated and triangulated across organisational, process and technology perspectives.

Introduction

Pandemics and epidemics may have severe impacts on manufacturing and industrial operations (Dubey et al. 2021a ; Dwivedi et al. 2020 ; Ivanov and Dolgui 2021 ; Wang and Wang 2021 ). Social distancing, remote work imposition and lockdowns are examples of the drivers for the adaptation of the manufacturing activities to a “new normal” (Belhadi et al. 2021 ; Aldrighetti et al. 2021 ). In this study, we aim to provide a systematic understanding of the COVID-19 pandemic’s impacts on manufacturing from the perspective of operations management.

Our motivation for this study stems from the observation that most of the existing literature on the COVID-19 pandemic have originated from either supply chain resilience (Dubey et al. 2021b ; Queiroz et al. 2020 ; Chowdhury et al. 2021 ) or technology areas (Wang and Wang 2021 ), while the specifics of manufacturing from the operations management perspective remain underexplored. Indeed, supply chain disruption and the concept of resilience in dealing with the settings similar to the COVID-19 pandemic have been thoroughly debated (Aldrighetti et al. 2021 ; Belhadi et al. 2021 ; Ivanov 2020b ). In contrast, less emphasis has been placed on other aspects of manufacturing, such as in-factory effects and impacts on the workforce and the internal organisation (P. Chowdhury et al. 2021 ; Hosseini and Ivanov 2021 ; Obradović et al. 2021 ). The dynamics within a single enterprise have also been neglected (Farooq et al. 2021 ; Ibn-Mohammed et al. 2021 ; Remko 2020 ; Taqi et al. 2020 ). Although repurposing strategies have been lifelines for many factories during periods of forced closure, these have not been adequately discussed in the literature related to the COVID-19 pandemic and viability (Burgos and Ivanov 2021 ; Ivanov 2021a ; Ruel et al. 2021 ). Similarly, remote work is mentioned mostly in a superficial manner (Aldrighetti et al. 2021 ; Younis et al. 2021 ), frequently overseeing the issues of employees’ competencies, skills and work reorganisation (Al-Fadly 2020 ; Obradović et al. 2021 ). With a few exceptions, studies have focused more on strategies and opportunities for the future rather than on analyses of how companies have dealt with the crisis and what lessons learned can be taken for the future (Remko 2020 ; Butt 2021 ; Ivanov 2021c ; Ghadge et al. 2021 ).

With this article, we aim to fill these research gaps and intend to provide a focused analysis of the pandemic’s effects on manufacturing. Distinctively, we triangulate our analysis across organisational, process and technology perspectives. Our objective is to systemise the pandemic’s impacts on manufacturing, the actions taken or potential, and future research opportunities.

Our contribution is twofold and covers both theoretical and managerial points of view. The theoretical contribution concerns the identification of under-investigated areas that need to be considered to fill the gaps emerging from the results of this study. On the managerial side, practitioners can benefit from this research by identifying potential strategies to address and adapt to possible outbreaks in the future that may result from COVID-19 pandemic-like crises.

We offer a systematic literature review (SLR) to answer the following research questions (RQs):

• RQ1. What are the main effects of the COVID-19 pandemic on manufacturing?
• RQ2. What actions have been taken to mitigate the effects of the COVID-19 pandemic?
• RQ3. What are the main future research directions according to the analysed literature?

The rest of this article is organised into the following sections. In Sect.  2 , we explain the methodology adopted for the literature review, with a descriptive analysis of the sample. In Sect.  3 , we present our study’s results, showing the impacts of COVID-19 pandemic, the adaptation actions and future opportunities in the manufacturing industry. In Sect.  4 , we discuss the research findings and offer some future research avenues. In Sect.  5 , we elaborate on the managerial implications for practice. Section  6 concludes the paper with a summary and an outline of future research opportunities.

Methodology

Literature selection strategy.

The approach adopted for selecting the scientific literature related to the COVID-19 pandemic and the manufacturing industry is presented in this section. The method adopted in this article is the SLR, through which all evidence fitting specific eligibility criteria is collected, the existing body of knowledge is summarised, and available research is scrutinised, aimed at filling research gaps and improving awareness about a specific field of study (Petticrew and Roberts 2006 ).

In this SLR, we adopt the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach (Moher et al. 2009 ), which ensures transparency and clarity through a four-step process (Fig.  1 ).

Selection process used in the literature review, based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method

This study’s rigour and generalisability are ensured by a structured article selection process, with specific criteria through which relevant papers are included and irrelevant ones are excluded. The initial database was built through the development of search keywords according to the RQs. The first set of keywords adopted was related to COVID-19 and included ‘COVID-19’, ‘coronavirus’, and ‘SARS-COV-2*’. The second set of keywords was related to manufacturing and included ‘manufact*’ and ‘operations management’. The SCOPUS search engine was used to build the initial database. Both journal publications and conference papers were included. This process resulted in the identification of 849 papers with no duplicates. The search was conducted in February 2021. This identification is the first step of the PRISMA method.

The screening phase was carried out to include only the most relevant articles, based on the RQs of this study. In total, 583 articles were rejected, mainly because they were related to fields such as immunology, microbiology, pharmacology, physics, astronomy, agriculture, arts and humanities. Moreover, only articles published in English were included, resulting in a database of 266 papers.

To achieve the research objectives, each paper’s title and abstract were read. Only studies investigating the impact of the COVID-19 pandemic on manufacturing firms were considered, particularly regarding the various processes involved in production and logistics, strategy and organisation of work in the factory (e.g., remote work, workers’ skills). As a result, 154 publications were excluded. Therefore, the 112 remaining publications were read in full, and the papers whose contents helped us answer the RQs were selected.

The database search ended with a cross-reference analysis (snowballing) aimed at overcoming potential keyword search limitations. Six supplementary papers were identified. At the end, 87 articles were considered suitable for this study, of which 82 were journal publications and 5 were papers in conference proceedings.

The analysis was conducted according to a reference model that guided this study (Fig.  2 ). The pandemic’s effects on manufacturing, as well as the actions and countermeasures taken by companies, were identified independently of each other. The selected papers were read and analysed with the support of Mendeley and Microsoft Excel. Through colour codes, the main impacts, mitigating actions and opportunities for the future were highlighted. These contributions were then catalogued and structured in a spreadsheet through the appropriate categorisation and systematisation. At a later stage, the analysis of this information brought to the development of research agenda, revealing the main gaps that emerged and future research avenues.

Research reference model

Sample description

Descriptive analysis of journal contributions.

Table ​ Table1 1 shows the list of journals that published the articles selected for this SLR. Overall, the journals vary in their fields of coverage, demonstrating that the topic is treated from multiple points of view. Notably, the first two journals are related to the theme of sustainability (smart and sustainable manufacturing systems, sustainability), while the journals related to operations management and production have surprisingly few contributions.

Journal contributions on the topic of the COVID-19 pandemic in the manufacturing context

Methodological approaches

The papers included in this study are classified into two main categories – theoretical and empirical – according to the methodological approach adopted.

Theoretical articles are further grouped into three subcategories: 1) literature reviews, 2) concept development and 3) position papers. The articles included in the first subcategory consist of literature reviews, SLRs, structured literature reviews and analytical reviews. The second subcategory is characterised by interpretative models about the research topics and specific applications of other studies reviewed considering the events and the effects of the pandemic. The articles included in the third subcategory assume a specific position on the selected issue, regarding how it is grounded in theory; these comprise perspectives, opinion pieces and commentaries.

The category of empirical papers is also divided into three subcategories: 1) qualitative (case studies, secondary sources, Delphi method, focus groups), 2) quantitative (surveys, simulations, and mathematical and model-based analyses) and 3) mixed-method articles that combine the aforementioned methods.

As indicated in Table ​ Table2, 2 , 35.6% of the studies belong to the concept development subcategory. In most cases, these studies generally deal with traditional streams of literature that are repurposed through the lens of the COVID-19 pandemic. In many cases, the topic of the pandemic is mentioned only marginally to support the validation of the results. Even literature reviews, despite appearing to be significant in number (18.4%), show this trend. Few reviews focus purely on the investigation of the pandemic’s effects. Another interesting consideration concerns the empirical articles. If we consider only their proportion, we observe the significant presence of this type of approach. However, considering the percentage of articles using surveys or case studies exclusively, this number is not excessively high. This is reasonable since the stream related to the pandemic is in its infancy. However, there is a clear lack of scientific literature on this type of contribution, which can potentially add significant value.

Paper types and methods used

Content analysis results

Impacts of the covid-19 pandemic on manufacturing companies.

In this section, we present the main findings from our analysis of the articles selected for this study, showing the main impacts of the COVID-19 pandemic on manufacturing (Fig.  3 ).

Reference model – Impacts of the COVID-19 pandemic on manufacturing companies

Lockdown and mandatory closures

The shocks from the COVID-19 virus outbreak have induced local governments to put in place strict measures to prevent further dangerous diffusion of the contagion. Therefore, aimed at isolating cases and reducing the transmission rate in many countries, strict measures, such as mandatory national lockdown, have been implemented (Chamola et al. 2020 ). These measures have led companies to quickly and strategically respond to unexpected challenges (Jones et al. 2021 ). These restrictions have not only affected production and marketing in local areas but have also led to the closure of factories and a shortage of materials due to the global disruption of the distribution of goods (Tang et al. 2021 ). Governments have distinguished the essential sectors (those that could remain in operation) from the non-essential sectors, for which suspension and remote work have been ordered (Carletti et al. 2020 ). The response times between the emergence of the first coronavirus case and the mandatory restrictions imposed by governments have differed among countries (Xu et al. 2020 ). By the end of March 2020, almost the whole world has implemented radical lockdown measures, banning non-essential travel and mandating the shutdown of all non-essential businesses, with schools and universities closed as well. As a result, there have been significant drops in production and employment, often at record paces or surpassing the decline witnessed in the 2008 Great Recession (Sheth 2020 ). Due to the lockdown in many cities, the constrained availability of human resources, raw materials and consumables has resulted in shutdown or capability suspension in almost all industry sectors (Paul and Chowdhury 2020 ; Singh et al. 2020a ; Xu et al. 2020 ; Zhu et al. 2020 ). In particular, small and medium enterprises (SMEs) have experienced instant adverse effects due to logistical issues, reduced capacity utilisation and demand-side effects (Juergensen et al. 2020 ).

Manufacturing businesses have been indirectly impacted by the restrictions imposed on restaurants, cafes, shopping centres, and general recreational and sports activities (Juergensen et al. 2020 ; Seetharaman 2020 ). The decline in demand related to these activities has reduced the production in associated industries.

Social distancing and remote work imposition

The need to apply social distancing has led governments to think about the necessity of remote work for manufacturing businesses (Kanda and Kivimaa 2020 ; Omary et al. 2020 ). These practices have spread to limit contagions and have drastically affected consumer behaviours in terms of consumption trends (Diaz-Elsayed et al. 2020 ). Sometimes, these limitations have led to the drastic reorganisation of the urban fabric, offices, and shop spaces. Physical distancing and mask wearing have been set as rules to be respected in both indoor and outdoor venues in many countries (Shen et al. 2021 ; Telukdarie et al. 2020 ). Social distancing, together with the use of a face mask, is probably the most effective measure to prevent a surge in infection. Another measure to contain the spread of infection includes compulsory home quarantine for confirmed cases and for all those who have been in contact with a person who has contracted the virus (Gupta et al. 2020 ).

On one hand, the spread of the COVID-19 virus has shed further light on the opportunity for a substantial reorganisation of work; on the other hand, it has revealed challenges to be faced (Bolisani et al. 2020 ). Physical distancing and face masks might influence the efficiency in some workplaces that generally need reconfiguration, leading to operational challenges and worktime readjustments (Garlick et al. 2020 ; Kurita et al. 2020 ; Telukdarie et al. 2020 ; Weersink et al. 2020 ). These measures have called for setting up work-at-home capabilities, redesigning offices and manufacturing spaces, and access to the appropriate technology for remote work and tools for video conferencing (Okorie et al. 2020 ). In some cases, remote work decreases interpersonal interaction, reducing the effectiveness of coordination (Ali Abdallah 2021 ). In general, a great deal of effort has also been spent on readjusting skills to allow a rapid transition to remote work, at times posing several difficulties (Rapaccini et al., 2020 ; Sharma et al. 2020 ). Despite these difficulties, most manufacturers agree that processes will be reengineered in the future, with social distancing and remote work continuing even after the end of the pandemic (Moutray 2020 ).

Changes in consumer behaviour patterns

With the spread of the COVID-19 virus, socioeconomic activities have gradually come to a halt, with schools closed, many manufacturing activities stopped, sporting and entertainment events cancelled, tourism collapsed and unemployment increased (Baker et al. 2020 ; Basilaia and Kvavadze 2020 ; Devakumar et al. 2020 ; Kraemer et al. 2020 ; Thunström et al. 2020 ; Toquero 2020 ).

The decrease in passenger and material flows has had a strong impact on demand in the automotive and the aviation sectors (Rahman et al. 2020 ; Sjoberg 2020 ). Stay-at-home policies and the imposition of remote work have triggered a reduction in sales in the garment and fashion industry (Hilmola et al. 2020 ) and an unexpected increase in the demand for toilet paper (Paul and Chowdhury 2020 ). In contrast, most technology companies have experienced growth in demand due to the increased need to work from home, as well as to enable students to take lessons from home via distance learning (Sharma et al. 2020 ). In turn, a reduction in the demand for copier paper and printing paper due to the shutdown of colleges and universities has been reported (Liu et al. 2020 ). Similarly, the closure of gyms and sports facilities has caused a decline in the demand for professional sports equipment and a simultaneous increase in the demand for home sports training equipment (Haren and Simchi-Levi 2020 ). Another sector whose consumption trends have been strongly impacted by the spread of the COVID-19 virus is undoubtedly the food and beverage industry. For instance, beef, which is used extensively in restaurants and fast food services, has undergone a significant decline in demand (Telukdarie et al. 2020 ). Restaurant and coffee shop closures have resulted in a reduction in milk consumption, while an increased demand for snacks and baking goods has been observed globally, as people have been eating more during the day, especially during a lockdown (FAO 2020 ). For the same reason, since restaurants and bars have only been allowed to serve takeaway food, there has been a substantial increase in their demand for paper boxes, straws, paper bags and food packaging papers (Liu et al. 2020 ). For this reason, considering socio-demographic factors is essential in examining how the COVID-19 pandemic has influenced consumer demand for goods and services (Jribi et al. 2020 ), and understanding the societal response to the COVID-19 virus diffusion is a critical issue as well (Ivanov 2020a ). These major changes have put a lot of stress on manufacturing companies, particularly on the planning of production processes, which have been revised according to the new conditions that have emerged. We should also add the phenomenon of panic buying, that is, an accumulation of goods that occurs in periods of severe uncertainties (Borsellino et al. 2020 ; Ibn-Mohammed et al. 2021 ).

To cite an example, many countries have made mask wearing compulsory, which has given rise to the inevitable extraordinary demand for these protective devices (Monostori and Váncza 2020 ). Moreover, increased awareness about hygiene and sanitation has led to a huge growth in the demand for handwashing gels, sanitisers (Chowdhury et al. 2020 ; Ibn-Mohammed et al. 2021 ) and personal hygiene paper products, such as paper towels and disposable underwear (Liu et al. 2020 ).

The COVID-19 pandemic has also boosted structural changes that were already on the way, particularly by globally pushing several companies towards online services (Haapala et al. 2020 ). This forced shift to online sales has required many manufacturing companies to revise their inventory plans to ensure adequate stock coverage for customers.

Actions and future opportunities to mitigate the impacts of the COVID-19 pandemic on manufacturing companies

In this section, we present the main actions carried out by manufacturing companies and future opportunities to mitigate the impacts of the COVID-19 pandemic on manufacturing (Fig.  4 ).

Reference model – Actions and future opportunities to mitigate the effects of the COVID-19 pandemic on manufacturing companies

Manufacturing repurposing

During the COVID-19 pandemic, diverse manufacturing companies have adopted manufacturing repurposing, which is a temporary strategy that envisages the production of items not related to the core business (Ivanov 2021b ). Manufacturing repurposing calls for the adaptation of the production lines and the upgrading of all the needed capabilities to meet new demand targets (Liu et al.  2021 ; Chopra et al. 2021 ).

Governments, institutions and organisations have asked manufacturers to compensate for the shortages arising from Chinese suppliers’ insufficient capacity to meet the growing demand for many kinds of goods (Xu et al. 2020 ). Therefore, many manufacturing companies have adjusted their processes to produce some of the goods subject to shortage, such as ventilators, virus testing kits and face shields (Singh et al. 2020a ; Xu et al. 2020 ). Furthermore, chemical factories have started providing sanitisers, cleansers and oxygen (Telukdarie et al. 2020 ). The textile and fashion industry has begun to produce personal protective equipment (PPE), masks and gloves (Shokrani et al. 2020 ).

However, repurposing has not been easily implemented by all manufacturers. On one hand, companies belonging to the process industry have easily managed to introduce new products or product families; on the other hand, this strategy has turned out to be much more challenging in manufacturing discrete parts (Monostori and Váncza 2020 ). Effective repurposing requires a high relevant rate of organisational flexibility, reconfigurability and dynamism (Brown et al. 2020 ). Therefore, businesses with a diverse and transverse level of skillsets are better suited for implementing manufacturing repurposing (Okorie et al. 2020 ).

Technology is another major enabler of manufacturing repurposing. The use of digital technologies has been highlighted as theoretically crucial in reducing the time needed to redesign and repurpose (Malik et al. 2020 ; Büchi et al. 2020 ). From this perspective, some companies have accessed small-scale novel production facilities that use 3D printers to help in the implementation of repurposing strategies (Patel and Gohil 2020 ).

Remote work

With the introduction of social distancing requirements, manufacturing companies have been forced to make radical changes in their work organisation to have opportunities for continued operations. In the very beginning of the crisis, most activities had already been blocked, and smart work was implemented to reduce the risk of infection (Rapaccini et al.  2020 ). By using ICTs, such as email, video meetings and cloud file management, employees in many industries can perform their jobs while working remotely (Garlick et al. 2020 ).

Notwithstanding the critical situation of field operations, somewhat surprisingly, the conversion from office to remote work has generally occurred without major issues (Rapaccini et al. 2020 ). Therefore, the feasibility of remote work has been validated in diverse industry sectors, and in general, it is perceived as a potentially viable route in the future as well, due to the experiences gained in the course of the global lockdown (Wang et al. 2020 ). Born and spread from the need to maintain social distancing, remote work can also be viewed as a structural measure, since many manufacturing companies believe that in the future, production processes will be engineered “with social distancing in mind” (Moutray 2020 , 248). Remote work conditions, as well as safety and health practices, have also become fundamental criteria for manufacturing companies’ ranking of their suppliers (Petrudi et al. 2021 ).

However, the implementation of remote work has happened too quickly, and “a cultural revolution in the way people approach their work is still needed” (Rapaccini et al. 2020 , 234). The development of a certain flexibility in organisations is necessary, including measures, such as skills (re)training of workers to make them suitable for remote work (Okorie et al. 2020 ). Digital technologies, such as cloud computing, Internet of Things (IoT) and big data analytics, may better enable remote and autonomous work (Niewiadomski 2020 ; Telukdarie et al. 2020 ). Similarly, the adoption of visualisation technology can help in carrying out field operations where direct contact is not possible due to restrictions (Akpan et al.  2020 ).

Workplace redesign and workforce reorganisation

To minimise the probability of infection, manufacturers have had to guarantee that all workplaces are sanitised and safe to protect workers in offices and in production departments. Manufacturers have attempted to balance safety protocols in their factories with the need to minimise disruption in their operations (Moutray 2020 ). Indeed, with the advent of the COVID-19 pandemic, every company has been required to sanitise the workplace (Ali Abdallah  2021 ). PPE and new cleaning practices are thus necessary for business continuity (Garlick et al. 2020 ).

Manufacturing companies have also been forced to review their layouts and workplace configuration, ensuring social distancing where remote work is not possible, by carefully assessing the number of people on site (Telukdarie et al. 2020 ). Such changes have often reduced output and overall agility, causing inefficiency (Meenakshi and Neha 2020 ). In some cases, the use of more quantitative analyses and models has been quite effective. The use of lean methodologies in production departments can bring huge benefits in terms of reducing the number of staff members and thus promoting social distancing (Ali Abdallah  2021 ). Alternatively, the use of mathematical models makes it possible to study airflow in an enclosed environment in order to take appropriate measures to avoid stagnant air in such a location and reduce the spread of the virus (Kurita et al. 2020 ). Similarly, the adoption of robotic technologies can contribute to the cause, given the opportunity for delivering the job in a contagious or dangerous area, while not having people infected or affected (Wang and Wang 2021 ).

Along these lines, manufacturing companies have also tried to look for ways to minimise employee absenteeism due to the interim shift from traditional schooling to remote learning for their children (Moutray 2020 ). Companies realised the need to be maximally agile in workforce management (Ali 2021 ).

Following continuous government limitations associated with social distancing requires implementation changes in physical establishments, necessitating further financial investments, which are at times difficult for SMEs to sustain (Juergensen et al. 2020 ). Nonetheless, smaller companies have proven to be more agile in handling these situations (Garlick et al. 2020 ). The increasing adoption of remote work requires the versatility and intuitive problem solving of the human workforce, including digital skills, previously lost to industrial automation (Monostori and Váncza 2020 ). Even before the advent of the COVID-19 pandemic, manufacturing companies were already making efforts to help employees do their daily jobs, especially with new technologies (Schulte et al. 2020 ). The COVID-19 pandemic has therefore accelerated trends that were already ongoing, particularly the need for educational programmes to enhance and retool workforce skills and accelerate the transition to disruptive technologies (Ali 2021 ).

Business model innovation and strategic changes

The crisis brought about by the COVID-19 pandemic has led many manufacturing companies to critically review their strategic organisation (Rajesh 2020 ). On one hand, the spread of the coronavirus has caused enormous obstacles to organisations; on the other hand, it has presented companies with opportunities to recognise business model innovations to ensure business continuity or even survival (Seetharaman 2020 ). Indeed, the COVID-19 outbreak has sometimes caused significant adjustments to current business models, and manufacturing companies have been forced to reinvent themselves, thinking about new business strategies. Lockdown and social distancing have completely changed consumer behaviour patterns; for this reason, companies have generally switched to online business through e-commerce portals (Moon et al. 2021 ). In other cases, it has been necessary to implement specific alliance and networking strategies with other companies, completely revising partnership strategies (Telukdarie et al. 2020 ).

The coronavirus outbreak has also demonstrated that technology has helped industries to be reactive and mitigate the negative business impacts (Seetharaman 2020 ). Digital transformation might be the key to moving up the value-added curve and helping industries remain profitable (Priyono et al. 2020 ). Indeed, business models solidly grounded on digital technologies definitely indicate the opportunity to increase the flexibility of working-time organisation and create competitive advantage for long-term growth in the so-called new normal (Niewiadomski 2020 ).

Similarly, servitization, the transition from a product-centric to a service-centric business logic, has helped manufacturing firms face the disruption (Rapaccini et al. 2020 ). In situations characterised by disruption and both financial and economic fluctuations, a servitization strategy can play a key role as an income stabiliser (Ardolino et al. 2018 ; Eloranta et al. 2021 ). Thus, a service-based orientation of manufacturing companies has certainly helped mitigate the negative effects of the pandemic.

Discussion and research agenda

To generate concrete implications for further advancement of the literature and field practices, in this section, we aim at deducing future research directions regarding the COVID-19 pandemic and manufacturing. Figure  5 presents open RQs, following the literature gaps and divided into three specific clusters, namely organisation, process and technology, echoing the study by Ivanov et al. ( 2021a ,  b ).

Reference model – Future research directions

Organisational focus

Organisational focus refers to all those corporate choices and activities that have long-term impacts on the firm and generally define the long-term future. These actions are linked to a manufacturing company’s mission and vision, as well as to the competitive scenario in which it finds itself.

The COVID-19 pandemic has caused a major shock to manufacturing companies, with strong repercussions affecting all business processes. As a result of security regulations to reduce contagion, manufacturers have found themselves in the situation of having to take countermeasures quickly to ensure business continuity (Seetharaman 2020 ). Remote work is an example of such actions. Although the literature states that the transition to remote work has generally taken place without too much difficulty (Rapaccini et al. 2020 ), it is necessary to rethink the work organisation in order to achieve increased flexibility. Another topic that deserves more interest is undoubtedly related to skills. The diffusion of the COVID-19 virus has revolutionised ways of working, coordination, and customer–supplier relationships. In some cases, this change has not been painless, especially when there is a lot of rigidity and resistance to change. A future research direction should certainly be oriented towards understanding how the skillset of employees must be configured to cope with strong external shocks and react quickly to better face the changed context.

The COVID-19 outbreak has led many manufacturing companies to assess their strategic organisation. For example, measures taken by various governments have forced manufacturing companies to boost or even develop their online businesses from scratch, drastically revolutionising traditional sales channels, even in sectors anchored in traditional sales methods (Moon et al. 2021 ). Some companies have succeeded quite easily by exploiting a particularly innovative business model, while others have failed. The literature has neglected investigating the enablers that facilitate a company’s innovation of its business model following an external shock. Many companies have had to develop new commercial channels, while others have needed to forge alliances that would allow them to maintain business continuity during the lockdown. Manufacturing companies based on service-oriented business models have been able to exploit income stability in a period strongly characterised by volatile demand and consequently, a liquidity crisis (Rapaccini et al., 2020 ). While scholars have undoubtedly debated a lot about the characteristics and the implementation of resilient supply chains, little has been mentioned about the potential features of a resilient business model (Rajesh 2020 ; Dolgui et al.  2020 ).

Despite the emphasis on the negative effects of the coronavirus pandemic, the actions taken by governments have also led to unintended environmental benefits. A significant reduction in fuel emissions was reported in the first part of 2020 (Ibn-Mohammed et al. 2021 ). Due to the mandatory lockdown and remote work set by governments, transportation infrastructures have been utilised more sparingly, resulting in a global drastic reduction in oil usage (Hosseini and Ivanov 2021 ). Obviously, these effects are only temporary, as this is a contingent situation, and positive environmental impacts might be neglected in the struggle for economic resurgence (Borsellino et al. 2020 ). Therefore, future opportunities can also be envisioned in the implementation of a circular economy strategy to reduce environmental impacts. The COVID-19 pandemic has raised awareness about the need to ensure both the economic and the environmental sustainability of manufacturing.

Process Focus

Process focus covers all the medium- and the short-term decisions made within the constrained structure of strategic options. It may include different processes, such as production and inventory planning, logistics management, manufacturing control, to name a few.

The COVID-19 outbreak has certainly had a very strong impact across most sectors of manufacturing enterprises. The research has concentrated on certain industries, particularly the production of basic goods, such as food and beverage and medical equipment (Belhadi et al. 2021 ; Borsellino et al. 2020 ; Kumar and Kumar Singh 2021 ; Paul and Chowdhury 2020 ; Singh et al. 2020a ). However, the pandemic has also had significant repercussions on tourism, bars, restaurants, and sports facilities, such as gyms and swimming pools. The lockdown policy and stay-at-home orders have certainly forced many people to spend much more time at home than in the office. All these have necessarily affected specific manufacturing sectors, such as alcohol, sports equipment, and clothing production. Few, if any, studies have focused on these industries.

Some of the companies in the abovementioned sectors have managed to ensure business continuity through manufacturing repurposing (Ivanov and Dolgui 2020a , b ; Liu et al. 2021 ). In fact, many of them have managed to shift their production to essential items, such as PPE, including masks and sanitary products (Telukdarie et al. 2020 ). However, this adaptation requires considerable flexibility in production methods, as well as adaptability of the workforce. The identification and the study of the enabling factors that make a repurposing strategy possible are certainly under-investigated but could help practitioners understand the tactical levers to cope better with disruptive external shocks.

High dependency on remote sources and complex logistics networks have been the other major causes of the disruption in many manufacturing supply chains (Cai and Luo 2020 ). More manufacturing companies are considering localising the production of their goods, given the push towards reshoring strategies (Barbieri et al. 2020 ). This approach is further supported by the fact that an increasing number of consumers are progressively paying attention to products’ origins, as well as ethical and environmental aspects (Borsellino et al. 2020 ). However, there might be some difficulties in implementing a reshoring strategy since difficulties in execution might arise due to the highly fragmented nature of government policy frameworks (Harris et al. 2020 ). The actors involved in current supply chains are characterised by a high degree of customisation and specialisation, and this specific aspect requires much investment in time and money to be achieved (Juergensen et al. 2020 ). Consequently, it seems that this process of transformation is feasible, at least on paper, even though it requires much more effort than envisaged in the theory.

A potential research direction from this point of view is the analysis of economic performance with respect to the measures undertaken by manufacturing companies (Choi 2021 ). There is a greater level of detail for the supply chain area than for factories’ operational processes. Apart from a few exceptions (Hilmola et al. 2020 ; Rapaccini et al. 2020 ; Taqi et al. 2020 ), the impact of the COVID-19 pandemic within the factory boundaries is an under-investigated issue.

A comparative study that considers a wide range of cases in different countries could be more significant. In this respect, the choices made by national governments regarding the imposition of lockdowns and the policy of reopening have played an important role in operational processes. Therefore, a potential direction for future research is to evaluate the relation between the choices adopted by various governments and the operational performance achieved by manufacturing industries. A further indicator for comparison could be the level of development of each country under analysis, with the opportunity to analyse the differences between developed and emerging economies.

Technology Focus

Technology focus refers to the choices in investments in information and operational technologies which can help organizations solving problems and inefficiencies.

The external shock brought by the COVID-19 pandemic, with the containment measures taken by various governments, has revolutionised the operations of manufacturing companies. Unexpectedly, manufacturers have found themselves reorganising the way they work, adapting their technology infrastructure to support remote work, reviewing departmental layouts to support social distancing, and managing forced quarantine situations for their employees. These quick decisions have had strong repercussions on operational and above all, economic efficiency.

The main innovative measure introduced during the COVID-19 pandemic has certainly been remote work. This organisation of work has spread persuasively across all manufacturing sectors, changing people’s traditional work habits. Despite the strong social impact of this new practice, its benefits, inefficiencies, and obstacles have been treated superficially in the scientific literature. Likewise, the difficulties and the opportunities presented by remote work in production departments have not been adequately discussed. The few empirical contributions in the literature have in fact shown that although companies have easily managed to introduce remote work in the white-collar departments, little or nothing has been achieved for the work of blue-collar departments (Rapaccini et al. 2020 ). Similarly, there are no empirical studies regarding cases where digital technologies have managed to overcome this issue.

The coronavirus pandemic has therefore highlighted the global shortcomings and weaknesses of automation and digitalisation in manufacturing. Besides, the empirical evidence of these events is widely discussed in the literature, which has brought out the urgent need to rethink the configuration of customer–supplier relations at an overall level (Petrudi et al. 2021 ). One technology that is much discussed in the literature and potentially useful is blockchain. Blockchain has gained attraction across different sectors, even if there are still few applications in supply chain management, mostly at an experimental level. Future research contributions should address the potential applications of big data analytics and blockchain to support manufacturing and supply chain processes, filling the gaps highlighted during the COVID-19 pandemic (Dubey et al. 2020a , b ).

The acceleration in digital transformation induced by the spread of COVID-19 has affected companies, public entities and individuals, too (Almeida et al. 2020 ). During the pandemic, people have grown familiar with the use of digital technologies that enable remote work and distance education. Voice-over-Internet-Protocol (VoIP) software, such as Zoom, Microsoft Teams and Google Meet, have quickly become common tools for hosting work meetings and online classrooms, while e-commerce and home delivery, supported by Information and Communication Technologes (ICT), have increased in popularity due to stay-at-home policies (Jiang 2020 ). For what concerns manufacturing enterprises, matters are more complicated. Production activities cannot easily be conducted remotely, but digital technologies can be implemented to enable some remote operations, automate processes, allow machines to work autonomously and reduce on-site personnel (Kamal 2020 ). In a historical period strongly marked by the digital transformation of manufacturing companies and by the numerous scientific contributions related to the Industry 4.0 paradigm (Zheng et al. 2021 ), the role of technologies in fighting the pandemic has only been superficially treated (Frazzon et al. 2021 ; Ivanov et al. 2021a ). Many of the published studies have primarily examined the potential uses of technologies for supply chain resilience (Spieske and Birkel 2021 ), whereas there is a lack of studies related to the technologies for supporting factories’ operational processes.

Managerial insights

Along with the theoretical insights, some useful managerial implications can be derived from our analysis. In Sect.  5.1 , we present a concise view of our major results for practitioners. In Sect.  5.2 , we discuss how digital technologies and artificial intelligence can be employed to increase manufacturing resilience.

Pandemic impacts and response actions

In Table ​ Table3 3 we connect in detail each identified impact with the directly associated actions to allow readers to immediately capture the strategies best suited with the issues raised by COVID-19 pandemic.

Impact and action associations

First, changes in consumer behavior patterns belong to the most prominent impacts of the COVID-19 pandemic on manufacturing. The associated actions can be seen from both operative and strategic perspective. The operative perspective can be addressed by manufacturing re-purposing to respond immediately. From the long-term perspective, business model innovation and strategic changes can be driven by the pandemic and post-pandemic environments.

Second, social distancing and remote work imposition have impacting manufacturing. Workplace revision and workforce reorganization can be named as response actions to adapt to the pandemic environments. Third, governmental measures for pandemic control such as lockdowns and mandatory factory closures should be considered as the crucial impact on manufacturing. Preparedness for and adaptation to a remote work belong therefore to response actions in this area.

Digital technology and artificial intelligence roles in increasing manufacturing resilience

Manufacturing is evolving toward data- and technology-driven networks and digital ecosystems. Data analytics, additive manufacturing and Industry 4.0 allow creating end-to-end visibility based on dynamically reconfigurable material flows and digital information flows (Dolgui and Ivanov 2021 ). Robotics, artificial intelligence, cloud computing, and big data analytics have crucial capabilities to improve the manufacturing resilience.

Certain technologies, such as robots, digital twins, blockchain and additive manufacturing have received attention (Chen and Cao 2020 ; Ivanov 2021d ; Ivanov and Dolgui 2021b ; Shen et al. 2021 ; Singh et al. 2020b ), while others have been mostly neglected (machine learning, artificial intelligence and big data analytics). The introduction of specific digital technologies, such as machine learning algorithms and augmented reality systems, can make production lines adaptive to changes, resilient in correcting errors and attentive to the operators’ skills that need upgrading (Baroroh et al. 2020 ; de Giorgio et al. 2021 ).

Digital technologies and artificial intelligence role in manufacturing resilience will certainly increase in future. Both preparedness for pandemics and pandemic-like crises and reactive recovery can be facilitated by automation, end-to-end visibility, and remote manufacturing control. As such, the trends to create digitalized manufacturing have a positive impact on resilience.

Conclusions

The spread of COVID-19 has drastically affected the global economy, with a profound impact on manufacturing companies. In this study, our goals are to assess and systematise the literature on the pandemic’s impacts on manufacturing industries, investigate the actions carried out and deduce some future opportunities to mitigate the effects of future similar crises. To the best of our knowledge, this is the first attempt to consolidate relevant contributions to these themes from the operations management perspective, where increasing interest is expected soon.

According to our results, the major determinants of the pandemic’s impacts on manufacturing, adaptation actions and future research directions can be triangulated across organisational, process and technology perspectives. We have found that the major impacts of the COVID-19 pandemic on manufacturing have been lockdowns and shutdowns associated with fluctuations in supply and demand, social distancing and remote work imposition, and changes in consumer behaviour patterns. The adaptation actions to combat the pandemic have been manufacturing repurposing, remote work, layout and workplace reconfiguration, workforce reorganisation and business model innovation with associated strategic changes. Furthermore, our study’s results allow deducing some open RQs and future directions, related to organisational, process and technology dimensions.

As with any research, this study has some limitations. First, the results sought by our study strongly depended on the keywords chosen for the sample selection of the articles to be analysed. Second, only English-language articles were included in the analysis sample, and this choice may have led to the exclusion of some local studies that were relevant to the analysis of certain topics highlighted as open RQs. Third, as with any SLR, the findings and their interpretation are influenced by the experiences and backgrounds of the researchers who performed the analysis. Finally, SLRs are studies carried out when the investigated phenomenon has reached a certain maturity, while this study has been conducted in a historical period when the end of the pandemic has not yet been declared and therefore in a context that is still far from reaching a condition of normality suitable for drawing conclusions and appropriately reflecting on the observed events.

Future research opportunities stem from these limitations. More focused analyses can be individually related to organisational, process and technology levels. For example, the layout reconfigurations under pandemic-like conditions can comprise the topic of a new literature review or a conceptual study. Another future direction can be considered regarding different pandemic stages and deducing commonalities and differences in manufacturing operations management and engineering across these stages. Another interesting problem in the context of a pandemic like COVID-19 is to investigate the costs of building resilient production while allowing for some extra cost to strengthen manufacturing systems against potential long-term disruption like pandemic. This is a practically important topic to balance lean practices and resilience in manufacturing and supply chains (Ivanov 2021c ).

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