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Research on Strategic Cost Management of Enterprises Based on Porter's Value Chain Model

Shuai Ruan 1

Published under licence by IOP Publishing Ltd Journal of Physics: Conference Series , Volume 1533 , Information technology Citation Shuai Ruan 2020 J. Phys.: Conf. Ser. 1533 022056 DOI 10.1088/1742-6596/1533/2/022056

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1 Jiangxi Science & Technology Normal University, Jiangxi, China, 330000

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With the increasing competition among enterprises, modern enterprises are facing a complex internal and external situation, which requires enterprises to strictly control their own cost management. The cost management activities of enterprises have great potential. Through effective cost control, we can strictly control the cost management of each link, which can reduce our own costs and improve their competitiveness. Through Porter value chain model, enterprises can control their own strategic cost, which caters to the competitive environment of enterprises. Through the value chain analysis tool of strategic management, we can better calculate the cost management of value chain, which can break through the limitation of traditional cost management. Through the whole life cycle, we can save production, R &amp, D and design, raw material procurement, production and processing costs, which can complete the whole process control of the value chain. Firstly, this paper analyzes the concept of Porter's value chain model. Then, this paper analyzes the strategic cost management based on value chain. Finally, some suggestions are put forward.

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• Corpus ID: 209503690

Research on Corporate Strategic Cost Management Based on Value Chain Analysis

• Hui-Ling Wang , Xilin Kuang
• Published 2010
• Business, Economics

17 References

Strategic cost management: the new tool for competitive advantage, value chain analysis in interfirm relationships: a field study., accounting data for value chain analysis, strategic investment decisions: the importance of scm. a comparative analysis of 51 case studies in u.k., u.s. and german companies, interorganizational cost management and relational context, integrating life cycle costing and life cycle assessment for managing costs and environmental impacts in supply chains, the economic institutions of capitalism: firms, markets, relational contracting., the competitive advantage of nations., do better customers utilize electronic distribution channels the case of pc banking, product design and development, related papers.

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16 Strategic cost management: upsizing, downsizing, and right(?) sizing

• Published: March 2006
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This chapter examines the concept of strategic cost management (SCM). It argues that most management accounting professors and management accountants in the business world prefer the often complicated, but conceptually less complex, world of ‘management accounting’ versus the conceptually challenging world of ‘strategic accounting’. SCM's ‘softness’ is too ‘hard’ for them, forcing them towards a world view that is much more multidisciplinary and integrative than they are trained for or comfortable with. The chapter foresees very few champions for SCM in the corporate world or in academe, while a large array of forces are working against the development of such champions, either in universities, accounting firms, or lower-level corporate training programmes. A case study of the business model of one of the best capitalized competitive local exchange carriers (CLECs) — Comprehensive Home Communications (CHC) of Atlanta, Georgia — is presented.

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Cost management is a long-standing research topic in the field of management accounting. In today’s, cost management has been closely linked to the company’s strategy, and has become an important way for enterprises to build competitive advantage and create core competitiveness. In order to understand the development of strategic cost management in recent years, in order to inspire future research directions, this paper mainly selects the domestic and international literature on strategic cost management issues from 2013 to 2017, their research content, research framework, and research methods . The conclusions of the study are summarized . It is found that the strategic cost management research shows the characteristics of using empirical research and clear research framework. Finally, based on this, the future research is proposed.

Strategic Cost Management , Research Status , Literature Review

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1. Introduction

Cost management is a long-standing research topic in the field of management accounting. Today, cost management has been closely linked to the company’s strategy, and has become an important way for enterprises to build competitive advantage and create core competitiveness. The definition of strategic cost management is to identify cost drivers from the source of cost from a strategic perspective, and to manage costs in the value chain, that is, to use cost data and information to provide strategic cost information for each critical step of strategic management. In order to facilitate the formation of competitive advantage and the creation of core competitiveness, traditional cost management focuses on cost-cutting technologies and methods, while strategic cost management is not based on cost-cutting. It focuses on getting as much use value as possible with as little cost as possible. Strategic cost management is guided by cost-effectiveness. If it can enhance the competitive advantage of enterprises, it is in line with the spirit of strategic cost management to increase costs appropriately. Compared with foreign countries, China’s strategic cost management research started late. This paper attempts to make a brief review of the relevant literature on strategic cost management in the past five years at home and abroad, and summarizes the research results, hoping to provide some inspiration for future research.

This literature review is mainly divided into the following parts: First, a brief statistical analysis of the selected literature, including research content, research methods, source journals, etc.; secondly, according to different research contents, the study of strategic cost management issues. The results are sorted out and summarized; finally, on this basis, some ideas provided for future research directions.

2. Literature Selection and Related Analysis

2.1. Literature Selection

This paper selects foreign and domestic literature on strategic cost management, with a time span of 2013 to 2017. Among them, the foreign literature comes from the database EBSCO-Academic Search Premier (including more than 1500 full-text journals of SSCI), using the keyword “strategic cost management” to search, and eliminating the literature with low degree of “strategic cost management” theme. Finded 12 foreign literatures. Domestic literature comes from China national knowledge internet. In the last six years, there were 24 papers on the theme of strategic cost management, including 6 in 2013, 7 in 2014, 1 in 2015, 8 in 2016, and 2 in 2017. From this we can also see that although strategic cost management is an important research topic, it has not been studied much in recent years.

2.2. Related Analysis

Among the 24 papers, the research on strategic cost management mainly focuses on the fields of performance evaluation and cost motivation, and the research methods used include empirical research, field research and analytical research. See Table 1 for details:

It can be seen that in recent years, the most commonly used method for strategic cost management is empirical research, followed by field research and analytical research. To a certain extent, this shows that the research on strategic cost management in 2013-2017 is mostly based on the data obtained and relatively standardized. There is not much difference between domestic and foreign research methods.

Research on strategic cost management generally does not exist independently of the value chain (e.g. Kaplan and Norton, 1996, 2004). In the practice of strategic cost management, the use of differential analysis, behavior analysis and standard cost methods to analyze cost data, find ways to improve the cost to

Table 1 . Distribution of research methods (2013-2017).

improve cost performance is called executive cost management; and use organizational design, product design and process. The way in which tools such as design establish a cost structure that is aligned with corporate strategy is called structural cost management. The study of strategic cost management is also divided into two categories. One is to study whether and how companies use accounting data for Porter’s value chain analysis, such as Hergelt and Morris (1989, SMJ), Shank (1989, JMAR), Shank and Govindarajan (1992, JMAR). The other is to study the relationship between corporate strategy and cost structure, as well as the causal relationship between the level of operations and the resources required (i.e. cost drivers), such as Banker and Johnston (1993, TAR), Ittner et al. (1997, JMAR), Maher and Marais (1998, JAR). Anderson (2007) summarizes the research results in the field of strategic cost management and proposes a strategic and strategic cost management chart. In this framework, the central issues of strategic cost management are value chain analysis, cost driver analysis, and activity-based costing (Lord, 1996, MAR; Banker and Johnston, 2007). Among them, accounting scholars have the most extensive research on cost drivers (e.g. MacArthur and Stranahan, 1998, JMAR).

2.3. Research Content

Since the discussion and analysis of cost management can only be carried out under specific environmental settings, most of the research on strategic cost management is based on specific industries, such as air transport, manufacturing, and medical industries. Similarly, in the 24 selected domestic and foreign literatures, the research content is mostly concerned with the strategic cost management issues of these industries. The details will be described in detail in the third section.

3. Literature Review

3.1. Research on Strategic Cost Management in Air Transportation Industry

Traditional cost accounting systems rely on capacity-based metrics such as production, direct labor hours, and machine hours for costing and allocation. The cost of such products is often biased or even wrong, causing managers to make mistakes. Short-term business decisions, such as whether to continue production or stop production of a product, whether to continue to supply. The wrong cost system can even have significant adverse consequences for the company’s budget management, variance analysis and accountability accounting system. Therefore, companies need more accurate cost measurement and begin to implement the operating cost system. Cooper and Kaplan’s field research found that managers have begun to focus on operational metrics through interviews with several corporate executives, but their research is still lacking systematic empirical evidence [1] . Foster and Gupta (JAE) studied the manufacturing costs of a company’s 37 workshops and refined several operational metrics based on the company’s manufacturing process. Although they conducted empirical analysis on manufacturing costs and operational indicators, they did not find empirical evidence of a significant relationship between the two.

Banker and Johnston (TAR) is an empirical paper on the strategic cost management of the US air transport industry. Based on 28 US airlines, the author examines the empirical relationship between resource consumption and capacity-based cost drivers and operational cost drivers in the air transport industry, and explains the management value of operational cost drivers. The author’s research shows that the cost drivers of operations are significantly related to resource consumption, and the cost drivers of operations reflect the different competitive strategies of airlines.

The difficulty and focus of empirical research in the field of strategic cost management is usually variable selection. Because unlike other accounting branches, the definition of variables in the field of management accounting is very difficult. The measurement of variables lacks a unified and accepted standard. Researchers need to make appropriate screening and selection according to the specific problems of the research and the value chain of the company.

In the past, the literature believes that in the air transport industry, total passenger and cargo mileage is a cost-based cost driver (Sickles, JE; Sickles and Good, JE), but the author believes that the total passenger and cargo miles are only suitable for dealing directly with passengers and goods. Most of the other costs are very low. For example, fuel consumption and flight service operations are more related to aircraft size, seating capacity, flight distance, and more. Therefore, the author proposes that the effective seat mileage and effective cargo mileage should be used as the capacity-based cost drivers, and the two should be transformed and combined according to the research needs, and finally form the most important production cost driver of the paper: the capacity seat mileage.

According to the rules of Hayes and Clark (Interfaces) and Miller and Vollman (HBR) in the manufacturing industry, the operational cost drivers of the air transport industry are determined with reference to manufacturing product quality, product line diversity, manufacturing processes and production batches. Such as aircraft model, flight density, aircraft shift control, etc. The author’s analysis pointed out that since the United States canceled air traffic control, each airline adopted a different competition strategy, mainly reflected in the establishment and use of the hub center operating system. The establishment of a hub center allows airlines to use more aircraft to transport more passengers at a time, thereby reducing costs, but at the same time increasing the company’s management costs (because the aircraft will spend more time on the ground). Therefore, hub concentration and hub coverage are also operational cost drivers selected by the author.

3.2. Strategic Cost Management Research in the Medical Industry

Xu Yuanyuan (2014) studied the relationship between macro-public health care reform and micro-health care institutions’ response measures, and published it in accounting research. Xu Yuanyuan’s research shows that medical reform is a macroscopic and complex project, and it is necessary to constantly adjust various policies to explore the path of medical reform that suits China’s national conditions. Xu Yuanyuan’s research focused on the medical reforms aimed at breaking the “medical treatment by medicine”, starting from the perspective of the interaction between the design of top-level medical reform system and micro-medical services. By eliminating the clue of the drug addiction policy to the reconstruction of the pharmaceutical supply value chain, the macro-reform exploration and micro-public hospital response measures are linked together, and the strategic cost management theory is used as a guide to analyze how public hospitals respond to macro-medical reform requirements and in the reconstructed medical supply value chain environment, clear new strategic positioning, discuss the corresponding public hospital compensation, and then make recommendations on the public hospital cost driver identification, control and strategic cost management performance evaluation.

Xu Yuanyuan’s research has led to the exploration of adjustments to regular financial subsidies and incentive financial subsidies for public hospitals. According to the commonality of public hospitals, the basic medical treatment projects will be set up with a loss and no subsidy allowance, and regular hearings will be carried out to update and optimize the subsidy projects. Differentiated subsidies will be implemented for the special circumstances of different types of hospitals, and the financial subsidies and the number of services provided will be provided. The quality and related financial information are linked, and a new performance appraisal subsidy system is established. The assessment results are based on the assessment weights of the assessment indicators such as the number of visits, the average cost control rate, the drug control ratio, the patient satisfaction rate, and the medical service cost. Subsidy basis encourages public hospitals to improve the quality of medical technology and services and better serve patients.

The smooth progress of “public welfare” medical reform requires multiple measures to protect it, and the elimination of drug addiction and compensation mechanism design is only one of them. This requires the active coordination and support of the micro-subjects, a full understanding of the original intentions of the reform and policy limitations, adjustment of its strategic positioning in the reform, good feedback on policy implementation and suggestions for further policy recommendations, and more important is to do well. The basic cost management of micro-subjects, improves the quality of medical services, enhances the trust of the pharmaceutical supply chain, and helps medical reform exploration.

3.3. Research on Strategic Cost Management in Manufacturing Industry

Oyewo Babajide Michael (2013)’s study of Nigeria found that the 2008 financial crisis caused business priorities to shift from growth rates and profits to survival issues until the economy improved. One thing that can be understood is the emphasis on cost; therefore, cost management is seen as one of the best ways to maintain profitability in the face of softening sales and shrinking profits. The purpose of his research was to determine whether Nigerian companies actually use strategic cost management (SCM) technology, and the extent of their use (especially in the Nigerian manufacturing and financial services industries) to identify factors that influence the adoption of strategic cost management, and whether the strategy can be used. Cost management is used as a survival competitive strategy during the recession. Statistical procedures were performed on the collected data using the Mann-Whitney test. The study found that although Nigerian companies accept the philosophy of strategic cost management (SCM), there are challenges in the Nigerian environment that hinder their adoption and implementation. Manufacturing focus also leverages strategic cost management (SCM) tools compared to financial services companies. Nigerian companies are encouraged to adopt strategic cost management tools, and the Nigerian government has developed policies to create an enabling environment for the adoption of strategic cost management (SCM).

Oyewo Babajide Michael’s research investigates how to use strategic cost management (SCM) in the manufacturing and financial services industries as a competitive tool for corporate survival during recession [2] . Although global financing has a negative impact on global companies, the impact is not entirely negative. The recession has forced companies to focus on developing cost management strategies to ensure their survival and continued relevance in the business environment. It also forces them to adopt and adapt to existing strategic cost management (SCM) technologies. The study also found that strategic cost management (SCM) technologies are feasible, applicable, and implementable in the Nigerian environment: but they are more widely used in Nigerian manufacturing than in the financial services industry.

3.4. Research of Other Aspects

Ye Kangtao, Zhang Wei, and Zhang Yixin (2014) Research on the relationship between corporate strategy differences and accounting information value shows that when the corporate strategy deviates from the industry’s conventional strategy, the value correlation of the owner’s equity is higher, and the value correlation of net profit is the lower. After controlling the impact of corporate-level characteristics such as corporate bankruptcy risk, earnings persistence, income growth, firm maturity and stock liquidity, the research conclusion remains unchanged (this indicates that investors are also concerned about Accounting and strategic information in stock pricing process). Moreover, investors’ understanding of accounting information relies on strategic information. Therefore, increasing the disclosure of strategic information helps investors better understand financial statements, and their research helps to improve the company’s information disclosure system. And better understand the value relevance of accounting information. Xie Dongming and Wang Ping (2013) studied the relationship between ecological economic development model and corporate environmental cost strategic control, and the meaning, characteristics and driving theory of corporate environmental cost strategic control. They expounded the mechanism of strategic control of corporate environmental cost under the model of ecological economic development, and demonstrated that the modern mode based on “strategic cost control” is an optimal environmental cost control model, which can make the total environmental cost equal to the control cost. The damage cost is zero, the total environmental cost is the lowest, and the zero emission is basically realized. Finally, the product life cycle cost management, operation cost management, material flow cost accounting―quantitative analysis of resource loss and incentive evaluation of implementing environmental cost strategic control is introduced. The implementation and application of four methods, such as mechanism, in enterprise environmental cost strategy control. Xie Dongming and Wang Ping’s research laid a certain theoretical foundation for the implementation of environmental cost control in China’s enterprises under the ecological economic development model, and provided specific implementation strategies and methods for enterprises to build an ecological economic development model to achieve economic benefits. The win-win situation with environmental benefits has an important driving role, and it has more positive and practical significance for accelerating China’s “ecological civilization construction” and creating a “blue China” grand blueprint. Lapsley I and Rekers J V. (2017, MAR) studied the relationship between accounting and popular culture [3] . Specifically, by studying the novel setting of the music world, we have increased our understanding of the potential of strategic management accounting. Using a case study approach, this study challenges traditional thinking from the perspective of “strategic practice” using the process of developing popular theater portfolio activities. The results of the study show that strategy is a complex practice and an intrinsic social process: the drama producers negotiate a market path by verifying the involvement of intermediaries, contributing to and disseminating the reputation of new cultural products, thereby supporting the strategic process. Research by Khodzytska V (2014, AF) shows that control is a tool for collecting, analyzing and evaluating the necessary information about the company’s condition [4] . Managers need this information to make effective management decisions that ultimately reduce risk levels. This paper is devoted to the nature and aspects of strategic control implementation as a strategic cost management tool for companies. Focus on the main aspects of strategic control impact making effective decisions on strategic cost management. It reveals the differences between operational and strategic controls and provides the vision of the company’s strategic control structure. Liu Q. (2014) shows that strategic cost management is the cornerstone of successful implementation of strategic management, and applying innovative strategic methods will help modern enterprises to seize cost management [5] . As for the wide distribution of modern enterprise departments, the cost management issues are complex and diverse. Therefore, in strategic cost management, modern enterprises cannot simply copy other business practices; they should be operated in the company in conjunction with the specific requirements of strategic cost control [6] . In response to the specific problems of cost management, effective measures are taken to continuously improve the implementation of the business strategy of cost management, and ultimately promote the long-term prosperity and development of modern enterprises.

4. Conclusion

By reviewing and sorting out the selected literatures on domestic and international strategic cost management from 2013 to 2017, we can find the following characteristics: First, from the perspective of the research methods used, there are mainly empirical research, field research and analytical. Three research methods are studied, and the most used ones are empirical research and field research, and the objectivity is stronger. Second, from the theory involved, in addition to the theory commonly used in economics, the relevant theories of sociology, psychology, and organizational behavior are often used, and the cross-disciplinary characteristics are obvious. In addition, the theoretical sources of foreign strategic cost management literature are relatively rich, and the theoretical basis is relatively solid. In contrast, the relevant domestic literature in China appears to be somewhat weak and not profound enough. Third, from the research content, the research on strategic cost management of specific industries still occupies a major part. Some scholars have studied strategic cost management from different perspectives such as accounting information value relevance, ecological economic development, and popular culture. Compared with foreign research, in recent years, China’s research on strategic cost management has also developed rapidly, and the research results are rich.

5. Shortcomings

The content of the article is relatively simple, and the previous literature needs to be more deeply combed and grasped. The collection of documents should be more comprehensive. Mastering a comprehensive and large amount of literature is a prerequisite for writing a good review. Otherwise, it is impossible to write a lot of reviews by simply collecting a little information, and even writing articles does not become a summary at all. Note the representativeness, reliability, and scientific nature of the cited literature. There may be similar views in the collected literature, and some literatures have differences in reliability. Therefore, when citing documents, attention should be paid to the selection of documents with good representativeness, reliability.

6. Research Outlook

6.1. The Significance of Foreign Research for China

Foreign research in this area has the following two points worthy of our reference: First, strengthen the theoretical foundation. If the research based on data analysis is more reliable, the solid theoretical foundation is the icing on the cake, which will make the research more logical and improve the ideological nature of the article [7] . Second, innovative research perspective. The domestic strategic cost management literature only focuses on the exploration of different industries, and rarely touches other aspects [8] . This requires domestic scholars to be more adept at discovering problems in practice, and to conduct more extensive and in-depth research on the problems of strategic cost management in practice in light of China’s national conditions.

6.2. Prospects for Future Research

In recent years, on the theme of strategic cost management, domestic and foreign scholars have paid great attention to their research in specific environments. Although a lot of conclusions have been drawn in this respect, the direction of influence on some factors is not conclusive [9] . Moreover, it can be considered whether the determined influencing factors will still play a role in strategic cost management when the environment changes, that is, to explore the robustness of strategic cost management.

In addition, compared with previous years, the application of field research is increasing. China’s literature on strategic cost management has also begun to use field research methods to solve problems [10] . Therefore, it is expected that the development of field research will be more rapid in the future. The research on strategic cost management and even the field of management accounting pay more attention to the role of people in it [11] . The characteristics of interdisciplinary are more and more obvious, and it is necessary to contact experimental economics and society [12] . Therefore, we need to have a broader perspective.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

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Cost Management Research

Journal of Management Accounting Research, Forthcoming

64 Pages Posted: 10 Dec 2016 Last revised: 26 Oct 2017

Rajiv D. Banker

Temple University - Department of Accounting

Dmitri Byzalov

Temple University - Fox School of Business and Management - Department of Accounting

Shunlan Fang

Kent State University

University of Virginia - McIntire School of Commerce

Date Written: October 25, 2017

The traditional view of cost behavior assumes a simple mechanistic relation between cost drivers and costs. In contrast, contemporary cost management research recognizes that costs are caused by managers’ operating decisions subject to various constraints, incentives, and psychological biases. This conceptual innovation opens up the “black box” of cost behavior and gives researchers a powerful new way to use observed cost behavior as a lens to study the determinants and the consequences of managers’ operating decisions. In 2014, Banker and Byzalov presented an overview of the economic theory of cost behavior and major estimation issues. The research literature on cost management has grown rapidly in the past few years and enhanced the understanding of how managerial decisions influence observed costs. In this study, we provide a comprehensive review of recent findings and insights, with a particular emphasis on the implications of cost management for understanding issues in cost, managerial, and financial accounting, and challenges and opportunities for future research.

Keywords: managerial decisions; resource adjustment costs; asymmetric cost behavior; cost stickiness; optimistic and pessimistic expectations

Suggested Citation: Suggested Citation

Temple University - Department of Accounting ( email )

Dmitri byzalov (contact author), temple university - fox school of business and management - department of accounting ( email ).

Philadelphia, PA 19122 United States

HOME PAGE: http://astro.temple.edu/~dbyzalov/

Kent State University ( email )

Kent, OH 44242 United States

University of Virginia - McIntire School of Commerce ( email )

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Research on Strategic Cost Management in Modern Enterprises

In the environment of market economy, many enterprises have started to enter the "low profit era", the competition between enterprises is becoming more serious. Economic benefit is always the primary target in enterprise management. Strategy management thought in cost management realizes the strategic function expansion, has formed the strategic cost management. It is separated from the traditional " save " concept, but the use of cost data and information, and to confirm the optimal strategy and promote the development of the company's competitive advantage, so as to realize the transition to the modern management. The implementation of strategic cost management is the cost management accounting information through the strategic management cycle, ensconce cost analysis and cost information in strategic management, related factors and influence strategy together, through a comprehensive understanding, from a strategic height of enterprise cost structure and cost behavior control and improvement, seek the competition advantage for a long time, to provide information service for strategic management. The primary task of the strategic cost management is the focus on cost strategy space, process and performance. Further, the cost of information throughout the whole process of strategic management, by analyzing the cost behavior, cost structure, comprehensive understanding, control and improvement, seek the competition advantage for a long time. Strategic cost management is an important prerequisite for an enterprise to gain advantages in modern increasingly competitive market. This article concentrates on the strategic cost management, starting from the concept and connotation of strategic cost management in modern enterprises, discusses the problems of cost management, analyzes the characteristics and innovative passages of strategic cost management and puts forward innovative tactics of strategic management in modern enterprises. Therefore, this article introduces strategic cost management notions and advocates improving cost management consciousness, to establish long-term, extensive, comprehensive and dynamic strategic cost management models. By advanced information processing platform, to improve technical innovation level and effective cost savings for the enterprise, puts forward feasible suggestions to enhance enterprise competitive ability.

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Strategic alignment between supply chain partners and cost stickiness of suppliers

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• Published: 16 July 2024

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• Chia-Hsin Chiang 1 ,
• Helen Choy   ORCID: orcid.org/0000-0002-5018-0308 2 ,
• Yan-Jie Yang 3 &
• Shu-Ling Yeh 4  

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We investigate the relationship between strategic alignment between suppliers and their major customers and the asymmetric behavior of costs, specifically cost of goods sold (COGS) and selling, general, and administrative (SG&A) costs. Utilizing mandated disclosures regarding major customers from 1978 to 2018, we construct supplier–customer dyads to examine this connection. Our results indicate a positive association between strategic alignment in supply chain partnerships and the stickiness of COGS and SG&A costs for suppliers. This positive association is more pronounced for suppliers in the early stages of their relationships than those with long-term partnerships. Additionally, strategic alignment increases the stickiness of suppliers’ research and development (R&D) costs. Furthermore, we observe that strategic alignment correlates with an extended duration of supplier–customer relationships and improved supplier performance. Our findings hold across alternative constructs of strategic alignment, various supplier strategy types, and when accounting for characteristics of the supply-chain relationship.

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1 Introduction

Prior research in supply chain management highlights the significance of suppliers’ customer-specific investments in fostering collaborative relationships within the supply chain (Chen et al. 2017 ; Chiu et al. 2019 ; Dekker 2004 ; Dyer 1997 ). However, due to the unpredictable outcomes of these customer-specific investments and their diminished value beyond specific customer–supplier relationships (Heide and John 1990 ; Trevelen 1987 ), suppliers sometimes hesitate to commit to such investments. We approach the examination of supplier–customer commitment from a different perspective—by assessing the supplier’s willingness to maintain expenditures on specialized resources to meet the customers’ needs during a period of sales decline (Anderson et al. 2023 ). Anderson et al. ( 2003 ) propose that firms’ decisions on resource adjustments during a sales decrease involve a trade-off between the costs of operating with unutilized capacity and the adjustment costs associated with reducing and subsequently restoring committed resources. This decision reflects the suppliers’ expectations about future customer demand and is affected by the level of trust between the supplier and the customer.

This paper examines the impact of strategic alignment between suppliers and customers on suppliers’ expectations about future customer demands and their cost reduction decisions when sales volume decreases. Prior research has considered how economic factors, agency issues, and product market competition affect managerial decisions on resource commitment (Anderson et al. 2003 ; Banker et al. 2014 ; Chen et al. 2019 ). However, we are unaware of any study examining the relationship between supplier–customer strategic alignment and suppliers’ decisions on retaining spending on excess capacity and resources when faced with a sales decrease. This study aims to address this gap.

Banker et al. ( 2014 ) document the substantial influence of managerial expectations on future demand in asymmetric cost behavior, known as cost stickiness. They posit that if managers anticipate a positive future demand during a sales downturn, their optimism prompts the retention of idle capacity and excess resources, anticipating their use in future sales growth—resulting in cost stickiness. Conversely, a pessimistic outlook leads to anti-stickiness, where managers, expecting a future sales decline, curtail current resource acquisition and even reduce existing slack capacity during a sales increase. These findings underscore that cost stickiness stems from short-term resource adjustment decisions influenced by managers’ expectations regarding future customer demand (Lee et al. 2020 ).

We build upon the theory of managerial expectations underlying the asymmetric cost behavior literature and hypothesize that strategic alignment between suppliers and customers influences managerial expectations. Strategic alignment, in this context, denotes similar business strategies among trading partners within the same supply chain. When trading partners share similar business strategies, their strategic objectives, competitive priorities, and actions are more likely to complement each other (Dekker et al. 2013 ). This alignment fosters shared goals, collaborative planning, and coordinated efforts, enhancing suppliers’ ability to meet customer needs and cultivating trust and loyalty between the partners (Chang et al. 2022 ). The prospects of a sustainable, long-term collaborative relationship is pivotal to the success of both customers and suppliers, thereby increasing suppliers’ inclination to retain capacity and resources during periods of decreased sales.

In addition, strategic alignment facilitates open and transparent communication between suppliers and customers. In such aligned relationships, customers are more inclined to share pertinent information with their suppliers (Stephen and Coote 2007 ). This increased information-sharing enhances suppliers’ comprehension of their customers’ production and demand needs, fostering collaboration and instilling confidence in the potential for a long-term business relationship. In such a scenario, suppliers are more willing to make long-term investments for their clients. Recognizing the pivotal role of trust in times of uncertainty, strategically-aligned suppliers are predisposed to retaining committed resources during sales declines compared to their counterparts.

Moreover, as relationship-specific investments hold limited value outside the specific business relationship, suppliers face the risk of exploitation by opportunistic customer behavior. The establishment of inter-firm trust through strategic alignment can mitigate the occurrence of opportunistic behavior by trading partners (Bradach and Eccles 1989 ). Hence, strategic alignment serves as a deterrent, discouraging customers from engaging in ex-post opportunism or leveraging their bargaining power. This reduction in operational risk enhances suppliers’ willingness to bear the costs associated with retaining committed resources for customers during sales declines. Additionally, suppliers and customers with strategic alignment are more prone to engage in long-term relationships and multi-period projects. Such projects often necessitate a sequence of expenditures and investments after the commencement of the project for ongoing maintenance and improvement (Li and Zheng 2017 ). Hence, cost stickiness is more prevalent when suppliers anticipate a sustained long-term business relationship rather than a short-term one. Consequently, we expect that strategic alignment among supply chain partners will increase the suppliers’ degree of cost stickiness.

We use the Customer Segment Files of Compustat dataset to construct a dataset of customer–supplier dyads from 1978 to 2018. We categorize a firm’s business strategy as Prospector, Defender, or Analyzer based on the business strategy framework of Miles and Snow ( 1978 ). After identifying the business strategy of each firm, we develop a metric to measure the similarity of strategy between supplier and customer in the same supply chain—strategic alignment. Following Bentley et al. ( 2013 ), we first create a composite measure of business strategy based on six firm-level variables. For each variable, we rank the observations into quintiles. We then sum the quintile ranks across these six variables to calculate a strategy score for each company (Bentley et al. 2013 ; Bentley-Goode et al. 2017 ; Navissi et al. 2017 ). Finally, we follow Chang et al. ( 2021 ) and measure the strategic alignment of supply chain participants by subtracting the absolute value of the strategy score difference in supplier–customer pairs from its largest possible value of 24.

Our empirical results indicate that strategic alignment between supply chain partners is positively associated with the stickiness of suppliers’ cost of goods sold (COGS) and selling, general, and administrative (SG&A) costs. This result suggests that having a major customer with similar strategic objectives encourages the supplier to retain relationship-specific investments during a period of sales decrease.

For cross-sectional analysis, we find that the positive association between strategic alignment and cost stickiness is more pronounced for suppliers in the early years of their supply chain relationship. It is consistent with the argument that during the early years of the supplier–customer relationship, there is a lack of trust and cooperation between the trading parties. Strategic alignment helps build trust and enhance collaboration between trading partners in these early years.

Further, we analyze the stickiness of suppliers’ R&D spending. R&D spending, characterized by its discretionary nature and extended revenue realization cycle (Chang et al. 2022 ), is particularly susceptible to contract imperfections. Suppliers tend to invest in R&D in anticipation of long-term relationships and associated benefits, rather than expecting immediate returns. We use R&D spending to proxy for relationship-specific investments by suppliers. We find that the long-term relationship instilled by strategic alignment increases the stickiness of R&D costs, indicating that strategic alignment between supply chain partners increases the cost stickiness of suppliers’ R&D spending. Our results are robust to alternative measures of strategic alignment and employing propensity score matching to address endogeneity. We also observe that strategic alignment correlates with an extended duration of the supplier–customer relationship and improved supplier performance.

Our study makes two contributions. First, it extends the literature on cost behavior by demonstrating that strategic alignment between supply chain partners results in increased persistence of COGS and SG&A costs for suppliers. The establishment of trust and the anticipation of a long-term relationship, stemming from strategic alignment, motivate suppliers to maintain committed resources even when faced with a decline in sales.

Second, our research aligns with an emerging research stream that evaluates the impact of strategic alignment on corporate behavior (Ashfaq and Raja 2013 ; Schreiner et al. 2009 ; Vachon et al. 2009 ). This study investigates the influence of strategic alignment on suppliers’ expectations regarding future customer demand and their incentives to adjust costs during periods of sales decline. We also document that strategic alignment contributes to customer retention and the establishment of longer-lasting relationships, ultimately resulting in improved supplier performance.

The rest of this paper is structured as follows: In Sect.  2 , we present an overview of the related literature and outline the development of our hypothesis. Section  3 describes the sample and the empirical method employed in our study. Section  4 discusses our empirical results. Section  5 details sensitivity checks and additional analyses. Section  6 provides concluding remarks.

2 Literature review and hypothesis development

2.1 strategic alignment in supply chain relationships.

Over the past decade, there has been growing evidence that, in order to remain competitive, companies are moving away from arms-length market transactions towards establishing longer-term relationships with a select few suppliers (Chang et al. 2022 ). This evolution leads to a dependency between trading partners, encompassing both tangible and intangible resources and resulting in interdependence. In mutually dependent relationships, each partner contributes expertise to the collaboration and gains access to resources or competencies that they lack individually (Dyer 1996 ). Interdependence can manifest in various ways. For instance, suppliers of parts in the automotive industry make transaction-specific investments for particular transactions, computer manufacturers invest in new production lines for joint technological development with their customers, and chip manufacturers share information with their trading partners to control costs and ensure quality (Anderson and Dekker 2005 ; Dekker 2004 ; Dyer and Singh 1998 ).

However, the presence of contractual incompleteness and information asymmetry between suppliers and customers can lead to opportunistic behavior by either party. For instance, customers can opt to switch to an alternative supplier. In such scenarios, issues such as expropriation and holdup problems arising from relationship-specific investments could emerge. These problems create a situation where suppliers are hesitant to make relationship-specific investments ex-ante (Williamson 1985 ; Baiman and Rajan 2002a ; Clemons and Row 1992 ; Dwyer et al. 1987 ).

Existing research explores various mechanisms aimed at mitigating opportunistic behavior among trading partners. For example, Titman ( 1984 ) observes that firms reduce their leverage ratio to minimize bankruptcy risk. The decreased bankruptcy risk provides reassurance to trading partners, encouraging them to invest in relationship-specific assets. Hart and Moore ( 1990 ) propose that companies can employ contractual arrangements, such as joint ventures, to increase the level of relationship-specific investments undertaken by suppliers or customers. Alternatively, Allen and Phillips ( 2000 ) suggest that businesses can derive benefits from establishing a long-term partial ownership position. They provide empirical evidence that companies with corporate block owners are more likely to invest in product market relationships or other relationship-specific assets. Fee et al. ( 2006 ) document that the duration of customer–supplier relationship tends to be longer when the customer holds equity ownership in the supplier. In addition to equity ownership, Baiman and Rajan ( 2002b ) note that an improved channel for information exchange helps suppliers or customers assess the risk associated with investing in specialized assets, consequently reducing the risk involved in making relationship-specific investments (Dou et al. 2013 ).

The strategic alignment between suppliers and customers yields enduring advantages for both parties. A customer who has “a supplier that shares its strategic objectives and, as a result, may be willing to trade the exercise of bargaining power to obtain these benefits.” This alignment results in fewer coordination issues facilitated by information sharing (Zsidsin and Ellram 2003), consequently reducing contracting costs (Anderson and Weitz 1989 ). In addition, the strategic fit enhances the likelihood of the customer achieving its long-term goals. Consequently, the customer is willing to trade its immediate bargaining power for the prospect of these enduring benefits. This reciprocal arrangement not only aids the customer in reaching its long-term goals but also allows the supplier to maintain profitability when engaging with influential customers.

With higher margins and profits derived from strategically-aligned customers, the supplier is more inclined to invest in skilled labor, training, technology, and production facilities that better cater to the customers’ needs. Both the supplier and the customer anticipate a long-term relationship, shaping their decisions based on this sustained cooperation rather than short-term profit maximization.

2.2 Managerial resource commitment decisions

We use the concept of cost stickiness, as developed in the managerial accounting literature, to proxy for a firm’s resource commitment decisions. Anderson et al. ( 2003 ) document that Selling, General, and Administrative (SG&A) costs exhibit a smaller decrease when sales fall compared to the increase observed when sales rise by an equivalent amount. This asymmetry in cost behavior, termed “cost stickiness,” is attributed to managerial decisions on resource adjustments influenced by resource adjustment costs. According to Anderson et al. ( 2003 ), the phenomenon of sticky costs arises because “managers deliberately adjust the resources committed to activities.” Managers refrain from cutting slack resources when sales decrease due to the associated costs of reducing the resources (e.g., severance payments) and the subsequent expenses of reinstating them when sales rebound (e.g., costs of hiring and training new employees) (Abel and Eberly 1994 ). If managers anticipate a temporary decline in sales, they choose to retain the underutilized resources to mitigate adjustment costs.

In addition, Anderson et al. ( 2023 ) suggest that an important part of maintaining a resource-based competitive advantage involves the willingness to continue spending on specialized resources even during periods of sales and profits decline, resulting in cost stickiness. Their study find a positive association between SG&A cost stickiness and future customer satisfaction. SG&A costs encompass investments in marketing research and strategy, building customer and social relationships, developing brand equity, and human capital (Ballas et al. 2022; Enache and Srivastava 2018 ). The results suggest that retaining specialized resources throughout sales downturns,, as indicated by SG&A cost stickiness, helps sustaining relationships with customers and enhancing overall customer satisfaction. Customer satisfaction defined as “a function of resources acquired and developed by the firm to enhance the customer experience and represents an intangible assset” (Anderson et al. 2023 ; Srivastava et al. 1998 ), contributes to building and sustaining resource-based competitive advantages. Consistent with Anderson et al. ( 2023 ), we use cost stickiness as a proxy for a company’s resource commitment. The literature, including studies by Anderson et al. ( 2003 ), Balakrishnan et al. ( 2004 ), Balakrishnan and Gruca ( 2008 ) and Banker et al. ( 2011 ), documents that deliberate managerial choices aimed at maximizing firm value can induce cost stickiness.

Banker et al. ( 2011 ) suggest that SG&A costs encompass investment spending on various facets, such as brand development, research and development (R&D), information technology, marketing and distribution, and employee training. These investments are instrumental in building long-term relationships with customers. Additionally, the Cost of goods sold (COGS) includes investments in specific facilities and skilled labor to fulfill production requirements and manufacture goods. The decision to retain these facilities and labor during a period of sales decrease reflects the manager’s expectation that the sales decline is temporary and signifies their commitment to retaining resources to better meet customer demand when sales rebound. Hence, the cost stickiness observed in both SG&A and COGS can serve as an indicator of a manager’s resource commitment decision in the day-to-day operating environment.

Building on Anderson et al.’s ( 2003 ) seminal work, subsequent studies further establish that resource adjustment decisions are influenced by managers’ expectations regarding future product demand. For example, Banker et al. ( 2014 ) find that the direction of prior period sales changes shapes managerial expectations about future demand and sales. Their findings indicate that, following two consecutive periods of sales increases, managers tend to be optimistic and are more inclined to retain slack resources if sales decline in the current period. In contrast, after experiencing two prior sales decreases, managers become pessimistic and are more likely to dispose of slack resources when sales drop in the current period. These results underscore the significant role of managerial expectations in shaping asymmetric cost behavior. Chen et al. ( 2019 ) propose that managers’ expectations carry the most weight when adjustment costs are high. Constraints imposed by adjustment costs amplify the importance of managerial expectations regarding future demands. Additionally, using a panel of elections in 55 countries, Lee et al. ( 2020 ) find that cost stickiness tend to increase before elections, as “managers retain slack resources when political uncertainty is high but to be resolved soon.” Zhou ( 2024 ) document that cost stickiness decreases following debt covenant violations. Collectively, these studies suggest that management expectations serve as a primary determinant of cost stickiness. On the other hand, Du et al. ( 2024 ) suggest that digital innovation reduces cost stickiness by enhancing resource adjustment efficiency.

2.3 Hypothesis development

To the extent that managerial expectations about future demand shape capacity choices and resource adjustment decisions (Anderson et al. 2003 ; Banker et al. 2014 ), we propose that strategic alignment between suppliers and customers acts as an informal mechanism through which supply chain partners sustain their long-term collaborative relationships. A firm’s business strategy guides managerial activity, shaping expectations, goals, and facilitating the organization’s efforts to achieve its objectives. When suppliers and customers are strategically aligned, they are more likely to share common goals and act concertedly (Chang et al. 2022 ; Dekker et al. 2013 ). This alignment in goals and coordinated actions allows trading partners to fulfill each other’s business requirements, fostering similar long-term objectives and a willingness to share information for building more sustainable relationships. Increased information sharing enhances suppliers’ understanding of the production and demand needs of strategically-aligned customers. It also instills confidence in the prospects of a long-term business relationship with these customers. Consequently, the supplier is more likely to anticipate that a decrease in sales is temporary and that sales to strategically-aligned customers will rebound in subsequent periods.

When both the suppliers and customers pursue the prospector strategy, the supplier is more likely to continue its investments in R&D to improve the quality of products and manufacturing procedures. Conversely, when both the supplier and customers adopt the defender strategy, the supplier is less likely to reduce its expenditure to streamline production and improve cost efficiency. With the significant investment in labor, technology, and facilities, the cost of trimming these resources and then rebuilding them when sales increase can be higher than the costs of holding the slack resources. That is, the expected adjustment costs are significantly higher than the holding costs. Consequently, the supplier is more likely to retain slack resources when sales decrease. Therefore, cost stickiness is more likely to be observed in a supplier with strategically-aligned customers.

Moreover, relationship-specific investments diminish in value once outside that relationship. This renders suppliers’ investments riskier when customers switch to a different supplier. By strategically aligning with supply chain partners, the probability of customers engaging in ex-post opportunistic behavior is reduced. This alignment also allows the supplier to compete beyond pricing, offering a distinct dimension. These benefits of strategic alignment create an environment where suppliers anticipate more positive customer relationships. This optimism about future client relationships encourages them to maintain committed resources even during periods of declining sales.

In addition, suppliers and customers aligned strategically are inclined to engage in multi-period projects and agreements. The nature of these projects necessitates ongoing investments to uphold the relationship and sustain the initiatives. Even during periods of declining sales, suppliers are less prone to cease investments in these multi-period projects. In essence, we argue that supply chain relationships characterized by strategic alignment foster trust and enhance collaboration beyond the capabilities of a contractual arrangement. Therefore, we propose the following hypothesis:

There is a positive association between strategic alignment among supply chain partners and the stickiness of supplier cost.

On the other hand, the enhanced exchange and sharing of information between strategically-aligned trading partners can yield contrasting effects on cost stickiness. Banker et al. ( 2014 ) posit and note that demand uncertainty amplifies cost stickiness. That is, asymmetrical cost stickiness behavior can arise from imperfect information or irrational decision-making by company executives. Information sharing among supply chain partners serves to mitigate demand uncertainty, consequently reducing cost stickiness for suppliers when strategic alignment is present among these partners. Hence, the impact of strategic alignment on cost stickiness remains an empirical question.

3 Research method

3.1 measurement of strategic alignment.

We operationalize strategic alignment by quantifying the extent of similarities between suppliers’ and their customer’s business strategies. We follow the accounting literature and adopt the business strategy typology construct developed by Bentley et al. ( 2013 ) to capture each firm’s business strategy. This construct draws upon Miles and Snow’s ( 1978 , 2003 ) business strategy typology and comprises an aggregate measure derived from six key firm characteristics: (1) the ratio of R&D expenses to total sales, (2) the ratio of SG&A expenses to total sales, (3) sales growth, (4) the ratio of net PP&E to total assets, (5) the ratio of employee numbers to sales, and (6) standard deviation of the total number of employees. Each of these six characteristics is specifically designed to capture a component of the firm’s business strategy.

Some of these characteristics, initially proposed by Ittner et al. ( 1997 ), serve as reflections of a company’s strategy: (a) the ratio of research and development to sales, (b) the ratio of employees to sales, (c) the number of new product or service introduction, and (d) market-to-book ratio as a proxy for growth. In Ittner et al.’s ( 1997 ) original measure, the count of new product or service introductions relies on a proprietary dataset and is not publicly available. Bentley et al. ( 2013 ) address this data limitation by substituting this measure with the ratio of selling and administrative expenses (SG&A) to sales. Hambrick ( 1983 ) finds significant differences in this measure between prospectors and defenders. Additionally, Bentley et al. ( 2013 ) uses the one-year percentage change in sales, instead of the market-to-book ratio, as a proxy for a firm’s growth, aligning with the notion that prospectors exhibit greater growth potential than defenders (Ittner et al. 1997 ). The ratio of research and development expenses to sales is chosen to capture a firm’s inclination toward developing new products, with prospectors expected to allocate more to R&D than defenders (Hambrick 1983 ). Meanwhile, the ratio of employees to sales reflects a firm’s efficiency in producing and distributing goods and services, with defenders anticipated to hire fewer employees per dollar of sales (Ittner et al. 1997 ; Thomas et al. 1991 ).

Bentley et al. ( 2013 ) introduce two additional measures to Ittner et al.’s ratio list: (i) the standard deviation of total employees to capture organizational stability, intended to capture organizational stability; and (ii) a measure of capital intensity computed as net PPE scaled by total assets, reflecting efficiency and automation of operations. Hambrick ( 1983 ) notes that defenders tend to be more automated and efficient, or more capital intensive, while prospectors adopt “more flexible, labor intensive capacity configurations”. Together, these six measures aim to capture a firm’s business strategy.

Following Bentley et al. ( 2013 ) method, we compute all six variables using a rolling average over the prior 5 years. The five-year average aligns with capturing firms’ long-term strategic orientation and is consistent with prior research practices (Balsam et al. 2011 ; Bentley et al. 2013 ; Higgins et al. 2015 ; Ittner et al. 1997 ). We follow Bentley et al. ( 2013 ) and require only 3 years of non-missing data for each measure, provided the company has at least six consecutive years of data in Compustat. Footnote 1

We quintile-rank firms within the same two-digit SIC industry code and year for each variable. A score of 1–5 is assigned to observations from the lowest to the highest quintile rank for each variable. We then aggregate the scores of all six variables for each firm-year observation to generate the business strategy score. This score ranges from a minimum of 6 to a maximum of 30, with higher scores indicative of prospector-like characteristics and lower scores associated with defender-like features. The use of the sum of rankings, rather than ratios, is preferred to construct the strategy measure, as the six ratios cannot meaningfully be summed together. Bentley et al. ( 2013 ) document evidence that the composite measure “is greater than the sum of its parts,” justifying the use of the composite measure constructed from quintile ranks in our analyses. This approach, evaluating a firm’s ranking relative to its peers in the industry for each strategy dimension, helps minimize changes driven by macroeconomic conditions, technological advancements, and industry practices, focusing on the portion of change attributable to a company’s strategic decisions. Organizational theory also recommends that the strategic components should be evaluated relative to industry competitors (Bentley et al. 2013 ).

Next, we assess the strategic alignment between a supplier and its customers by comparing the proximity of their respective strategy scores. Following Chang et al. ( 2021 ), we calculate the absolute value of the difference in the strategy scores between a supplier and its customer. We then subtract this absolute value from the highest possible value of 24 to quantify the strategic alignment for each customer–supplier pair, denoted as “ Align .”

where the value of strategic alignment ( Align ) ranges from 0 to 24. The higher the value of Align , the greater alignment between the supplier’s and its customer’s strategies. Appendix 1 provides examples of supplier–customer s strategic alignment in the prospector, defender, and analyzer categories.

3.2 Empirical models

We employ cost stickiness models developed in the managerial accounting literature to test our hypothesis. Specifically, we adopt the baseline cost behavior model introduced by Anderson et al. ( 2003 ):

where Δlog( Cost i,t ) ≡ log ( Cost i,t )– log ( Cost i,t −  1 ) is the log-change in cost of goods sold ( COGS ) or log-change in SG&A costs for firm i in year t. Δlog( Sales i,t ) is the log-change in sales revenue approximating the firm’s activity level in year t . Dec i,t is an indicator variable equal to one when year t sales are lower than year t − 1 , and zero otherwise. The coefficient β 2 captures the degree of cost stickiness (anti-stickiness). A negative (positive) β 2 suggests cost stickiness (anti-stickiness). Additionally, the sum of coefficients ( β 1  +  β 2 ) captures the percentage decrease in costs for a 1% decrease in sales. Following Anderson et al. ( 2003 ), we incorporate various factors known to influence the degree of cost stickiness:

where strategic alignment, Align it , captures the degree to which the supplier’s and its customer’s strategies align. Additionally, we control for other variables that could influence managerial expectations about future demand. GNP t is the real gross national product growth rate during year t. Managers tend to be more optimistic in a growing macroeconomic environment, viewing sales declines as transient. This optimism may lead them to retain resources, fostering cost stickiness. SucDec it is a dummy variable equal to one if sales revenue in year t −  1 is lower than sales revenue in year t −  2 and zero otherwise. Managers are more likely to adopt a negative outlook on future demand after observing two consecutive periods of sales decreases, potentially mitigating cost stickiness.

Further, we include two firm-level controls identified in the literature as determinants of cost asymmetry: AssetInt it and EmpInt it. AssetInt it is the asset intensity (i.e., assets to sales ratio) of firm i in year t. EmpInt it is the employee intensity (i.e., number of employees to sales ratio) of firm i in year t . Firms requiring more assets or employees to support their sales activity face higher adjustment costs when cutting resources. Appendix 2 provides detailed definitions for each variable.

Banker et al. ( 2014 ) and Banker et al. ( 2013 ) point out that managers exercise discretion when making decisions about resource allocation. Therefore, we specify the slope coefficient β 1 for sales changes as a function of strategic alignment, GNP growth, asset intensity, and employee intensity:

Combining Eq. ( 1 ) with Eqs. ( 2 ) and ( 3 ), we obtain our main estimation model (4), shown below:

A negative coefficient for \(\alpha_{0}\) in model (4) suggests a baseline level of cost stickiness. Based on our hypothesis, the coefficient \(\alpha_{1}\) is expected to be negative, which implies that strategic alignment increases cost stickiness. To estimate the parameters, we employ pooled OLS regressions with industry-fixed effects (defined by two-digit SIC codes) and year-fixed effects to control for potential unobserved industry factors and time trends that affect changes in costs. Also, standard errors are clustered at the firm level (Petersen 2009 ) to account for potential correlations within firms.

3.3 Data and sample

Our initial sample consists of all publicly traded U.S. companies with available data to identify major customer–supplier relationships between 1978 and 2018. In accordance with the Financial Accounting Standards Board (FASB) and the Securities Exchange Commission (SEC) regulations, public companies are mandated to disclose customers contributing more than 10% of their total annual revenue. We extract information on each company’s major customers and the corresponding revenue generated from Compustat Segment Files. To identify customers, we follow established procedures from prior research (Chang et al. 2018 , 2022 ; Pandit et al. 2011 ). This involves matching disclosed customer names with their respective Compustat identifiers (GVKEY). We retain observations only when customers can be precisely identified. After merging the Compustat Annual Files with Compustat Segment Files, our resulting sample comprises 69,583 supplier firm-year observations, each associated with identifiable major customers.

We exclude financial service firms (SIC 6000–6999) and utility firms (SIC 4900–4940) due to regulatory and operational differences. Following Banker and Byzalov ( 2014 ), we also exclude firm-year observations where COGS t  >  SALES t and SG&A t  >  SALES t before log-change computations. Additionally, we exclude observations with missing customer sales data and those voluntarily reported customers that contribute less than 10% of sales revenue. For suppliers disclosing multiple customers, we retain the customer with the highest trading amount under the assumption that the largest customer holds the most significant influence. Footnote 2 Observations with incomplete financial data are also excluded from the analysis. Our final sample consists of 16,951 firm-year observations. To address potential outlier effects, we winsorize continuous variables at the 1st and 99th percentiles. Table 1 presents our sample selection procedure.

4 Empirical results

4.1 descriptive statistics.

Table 2 provides descriptive statistics for the variables used in our main analysis. The average (median) firm in our sample reports sales revenue of approximately $1861 ($187) million dollars, COGS of about $1211 ($107) million dollars, and SG&A costs of $346 ($37) million. The mean value of SG&A costs as a percentage of sales revenue is around 27%. These statistics are comparable with the value reported by Banker et al. ( 2014 ). On average, nearly 34% of our sample firms experience a decrease in sales revenue relative to the previous year. Approximately 20% of the suppliers exhibit a similar strategy to that of their major customers.

Table 3 reports the Spearman (above the diagonal) and Spearman correlations (below the diagonal) between the variables. The majority of correlations are statistically significant but exhibit small magnitudes, except for the large correlations between Δlog ( Sales ), Δlog ( COGS ), Δlog ( SG&A ), and Dec.

4.2 Strategic alignment and cost stickiness

Table 4 presents the results of the analysis regarding the impact of strategic alignment between supply chain participants on the stickiness of suppliers’ COGS in Columns (1) and (2) and SG&A costs in Columns (3) and (4). We use pooled cross-sectional regressions, controlling for year- and industry-fixed effects (two-digit SIC codes). The estimated coefficients and t -statistics are derived using firm-clustered standard errors, a method that addresses issues of heteroskedasticity and intrafirm error correlation inherent in panel data (Petersen 2009 ).

To assess the base-level cost stickiness in our sample, we examine the coefficient estimates of Dec* Δ Log(Sales) . As presented in Columns (1) and (3) of Table  4 , we observe a negative and significant coefficient for the interaction term Dec* Δ Log(Sales) , indicating the presence of cost stickiness in our sample. However, in Column (2), when we add the three-way interaction term Align*Dec* Δ Log(Sales) into our COGS model, the coefficient of Dec*ΔLog(Sales) is no longer significant. Instead, the three-way interaction term, Align * Dec * Δlog(Sales), takes on a significantly negative coefficient of − 0.004. These results suggest that cost stickiness in COGS is predominantly observed for suppliers that exhibit strategic alignment with their customers. Specifically, for a unit increase in the strategic alignment score ( Align ), the incremental impact on COGS for sales decrease (computed at the mean Δlog ( Sales ) of 0.073) is − 1.393% (− 0.014 + 0.005*0.073–0.004*0.073). Given the mean Δlog ( COGS ) of 7.5%, this increase in cost stickiness is deemed economically significant.

When we analyze the stickiness of SG&A expenses, we observe once again that cost stickiness is more pronounced for suppliers strategically aligned with their customers. The coefficient of the three-way interaction term, Align * Dec * Δlog(Sales), is significantly negative at − 0.008. For a unit increase in the strategic alignment score, the incremental impact on SG&A for sales decrease (computed at the mean Δlog ( Sales ) of 0.073) is − 5.1% (− 0.051 + 0.006*0.073–0.008*0.073). Considering the mean Δlog ( SG&A ) of 7.5%, this increase in cost stickiness represents 68% of the mean. These findings highlight the role of strategic alignment in influencing the stickiness of costs in production and discretionary spending of suppliers.

Regarding the control variables, the three-way interaction terms are generally consistent with findings from prior studies (Anderson et al. 2003 ; Banker et al. 2014 ; Chen et al. 2012 ). In Columns (2) and (4), the estimated coefficient of GNP * Dec * Δlog ( Sales ) is significantly positive. Previous research has documented mixed results for GNP * Dec * Δlog ( Sales ). While Anderson et al. ( 2003 ) argue that managers are more optimistic in prosperous economic climates and less likely to cut costs during a sales decline, Banker et al. ( 2013 ) report both positive and negative coefficient estimates for this term. The coefficient of SucDec * Dec * Δlog ( Sales ) is positive and significant in both Columns (2) and (4), indicating that managers are more inclined to cut spending when facing consecutive periods of sales decline. This aligns with the notion that managers perceive a sales decline as permanent when occurring in two consecutive years. The coefficients of AssetInt * Dec * Δlog ( Sales ) and EmpInt * Dec * Δlog ( Sales ) are negative but statistically insignificant for SG&A analysis.

5 Additional tests

5.1 sensitivity analyses, 5.1.1 moderating effect of strategy type.

A firm’s business strategy can play a significant role in shaping resource adjustment decisions (Ballas et al. 2020 ). We investigate whether the effect of strategic alignment on cost stickiness varies with the strategy type of strategic alignment—prospector, defender, and analyzer. Footnote 3 We introduce three terms: Align_Prospector , Align_Defender , Align_Analzer , along with their interaction terms in the model. Align_Prospector captures the effect of strategic alignment when both the supplier and its largest customer pursue a prospector strategy. Prospector firms are characterized by innovation and efforts to develop and exploit new products and market opportunities (Miles and Snow 1978 , 2003 ). Align_Prospector takes a value of 1 when both the suppler and its largest customer have a strategy score between 24 and 30 and zero otherwise. Align_Defender captures the effect when the supplier–customer pair pursues a defender strategy. Defender firms are focused on efficiency in the production and distribution of products and services (Miles and Snow 1978 , 2003 ). Align_Defender takes a value of 1 when both the supplier and its largest customer have a strategy score between 6 and 12 and zero otherwise. Align_Analyzer captures the strategic alignment effect for the analyzer strategy. It takes a value of 1 when both the supplier and its largest customer have a strategy score between 13 and 23 and zero otherwise. Table 5 Panel A presents the results. Only the coefficients of Align_analyzer and its interaction terms Align_Analyzer * Δlog(Sales) and Align_Analyzer * Dec * Δlog(Sales) are statistically significant. The insignificance of Align_Prospector, Align_Defender , and their interaction terms could be influenced by the small number of observations in these categories.

In Panel B, we conduct a similar analysis using the quintile ranking of suppliers and customers to define their strategy. In this approach, we introduce three indicator variables to capture different strategy typologies and their interaction terms with Dec * Δlog(Sales) and Δlog(Sales) in our model. The strategy constructs in Panel B are based on the quintile ranking of the strategy scores of the supplier and its largest customers within the same two-digit SIC industry and year. Align_Prospector_Q is an indicator variable that equals one if both the supplier’s and the largest customer’s strategy scores belong to the highest quintile. Align_Defender_Q is an indicator variable that equals one if both the supplier’s and the largest customer’s strategy scores are in the lowest quintile. Align_Analyzer_Q is an indicator variable that equals one if both the supplier’s and the largest customer’s strategy scores are in the three middle quintiles. Using this strategic alignment measure, Align_Analyzer_Q * Dec * Δlog(Sales) has a significantly negative coefficient for both COGS and SG&A analyses. Align_Analyzer_Q * Δlog(Sales) is statistically significant only for the SG&A analysis. In contrast, Align_Prospector_Q and Align_Defender_Q , along with their interaction terms with Dec * Δlog(Sales) and Δlog(Sales), are mostly insignificant.

In Panel C, we follow Chang et al. ( 2021 ) by classifying our sample observations into only the Prospector and the Defender groups, using the sample median of the strategy score as a cutoff. Suppliers and customers with a strategy score above the sample median are classified as pursuing the Prospector strategy, while those with a score below the median are considered as pursuing the Defender strategy.

We then construct three indicator variables. Align_PP takes a value of 1 if both the supplier and its major customer pursue the Prospector strategy and zero otherwise. Align_DD takes a value of 1 if both parties pursue the Defender strategy and zero otherwise. Our benchmark group is Align_DIFF , which takes a value of 1 when the supplier and the customer pursue different strategies and zero otherwise. We replace Align with Align_PP and Align_DD in model (4) and re-run the analyses. The results are reported in Table  5 Panel C.

The results presented in Table  5 Panel C, reveal that the coefficients of both Align_PP * Dec * Δlog(Sales) and Align_DD * Dec * Δlog(Sales) are negative and statistically significant. These negative coefficients suggest that when both the supplier and its major customer pursue the same business strategy, either the Prospector or the Defender strategy, the supplier’s COGS and SG&A costs tend to be stickier than in cases where a supplier–customer duet pursues different strategies.

5.1.2 Alternative measures of strategic alignment

5.1.2.1 three-year rolling average.

In this section, we consider alternative constructs of strategic alignment. First, we use a three-year average of the strategy ratios, instead of the five-year average, to construct a firm’s strategy and the strategic alignment measure. Footnote 4 We mandate that the company must have at least four consecutive years of data in Compustat to be included in this sample. The results using this alignment construct are provided in Table  6 . Our results are consistent with those obtained using the five-year rolling average. Therefore, our conclusion of increased cost stickiness for strategically-aligned suppliers and customers appears robust to variations in the number of years used to compute the ratios and the alignment measure.

5.1.2.2 Weighted-average strategic score of all customers

Our main analyses focused on the strategic alignment between a supplier and its largest customer. In this section, we replicate the analyses using the weighted-average strategic scores of all the suppliers’ major customers. The strategic score for each customer is computed as before. We use the portion of the supplier’s sales from a specific customer as the weight to compute a weighted-average strategic score for all its major customers. We then compute Align using the supplier’s strategic score and the weighted-average score of all its major customers.

The results using this alternative weighted-average strategic alignment measure are presented in Table  7 . Our results remain robust to this alternative approach. The coefficient of Align * Dec * Δlog(Sales) continues to be significantly negative in both the analysis of COGS and SG&A.

5.1.2.3 Residual measure of strategic alignment

Following the approach of Chang et al. ( 2022 ) and Venkatraman ( 1989 ), we compute an alternative strategic fit measure in three steps. First, we conduct regressions of the supplier’s spending on the corresponding customer’s expenditures in specific areas (e.g., R&D expenses scaled by total assets). The original business strategy measure is based on the following six firm characteristics: (1) the ratio of R&D expenses to total sales, (2) the ratio of SG&A expenses to total sales, (3) sales growth, (4) the ratio of net PP&E to total assets, (5) the ratio of employee numbers to sales, and (6) standard deviation of the total number of employees. In this step, we regress each of the above supplier’s spending/values on the corresponding customer’s expenditure/values in that specific dimension. The estimation period is limited to the duration of the supplier–customer relationship, and we require at least eight observations for each regression analysis. Consequently, the number of observations is significantly reduced. Second, we calculate the absolute values of the residuals from these regressions. Third, we take the negative of these absolute values and replace our aggregate measure of strategic alignment in model (4) with each of these residual measures. The results using these alternative measures are reported as statistically insignificant.

Bentley et al. ( 2013 ) assert that the composite strategy score is a superior measure compared to its individual components for capturing the construct of a firm’s business strategy, we aggregate the six adjusted residuals to derive an alternative measure of strategic alignment. Subsequently, we re-run model (4) using this alternative measure. The results are presented in Table  8 Panel A. Our results remain robust to this alternative measure of strategic alignment.

In the second approach, we conduct factor analysis to generate a common factor from the adjusted residuals of the six dimensions. The results using the factor analysis are provided in Table  8 Panel B. However, the results obtained from the factor analysis construct of Align are weaker compared to those using the aggregate sum in Panel A. The coefficient of Align * Dec * Δlog(Sales) continues to be negative but is statistically significant for the SG&A analysis only.

For the third approach, we quintile-rank the six adjusted residuals of each firm by industry (as defined by two-digit SIC code) and year. Observations in the highest quintile are assigned a score of 5, while those in the lowest quintile receive a score of 1. We then aggregate these scores to construct Align . The results using this Align construct are presented in Table  8 Panel C, and are found to be similar to those in Panel B.

5.1.2.4 Strategic alignment measure constructed using factor analysis of the six components of business strategy

We also follow Bentley et al. ( 2013 ) and Higgins et al. ( 2015 ) in constructing a strategy measure for each firm employing factor analysis on the raw scores in all six dimensions of business strategy. All the components load on one factor. We then adopt the two-stage analysis proposed in Venkatraman ( 1989 ) to derive a strategic alignment measure. In the first stage, we regress the suppliers’ factor scores on the customers’ factor scores. The residuals from this regression represent the strategic “misfit” between the supplier and customer pair. In the second stage, we take the absolute values of the residuals and then negate them to obtain a measure of strategic alignment. We then re-run our cost stickiness analyses using this alternative measure of strategic alignment. The results of the analyses are presented in Table  9 and we continue to observe increased cost stickiness for strategically-aligned suppliers.

5.1.2.5 Other alternative strategic alignment measures

In this section, we employ three alternative measures of strategic alignment: Align_ratio , Align_diff , and Align_rank , and re-estimate Eq. ( 4 ) (Chang et al. 2021 ). The construct Align_ratio varies with the relative magnitude of the supplier’s and customer’s scores. When the customer’s score is lower than the supplier’s score, Align_ratio is computed as the ratio of the customer’s strategic score to the supplier’s score. Conversely, when the supplier’s score is lower, Align_ratio is calculated as the ratio of the supplier’s score to the customer’s score. It takes the value of one when a supplier has the same strategy score as its major customer. Align_diff is the difference between a supplier’s and customer’s quintile ranks of their strategy scores; we then subtract the difference from its largest possible value of 4. Align_rank is a dummy variable that equals one when the strategy scores of a supplier and its major customer belong to the same quintile rank and zero otherwise. We present the regression results using these three alternative measures of strategic alignments in Table  10 . Our SG&A results are robust to these alternative measures of strategic alignment. The coefficient of the three-way-interaction terms is significantly negative in all cases. However, it is significantly negative only for Align_ratio when COGS is used as the dependent variable.

5.2 Additional analyses

5.2.1 changing effect of strategic alignment over the customer–supplier relationship.

The impact of strategic alignment can vary with the duration of the supplier–customer relationship. First, trust between suppliers and customers likely grows with the duration of their relationship. That is, the longer the relationship, the greater the trust between the two parties. The growth in trust can be particularly fast for strategically-aligned suppliers and customers. Hence, the effect of strategic alignment can increase with the duration of the supplier–customer relationship.

Second, suppliers in the early years of a customer–supplier relationship have to make considerable investments in customer-specific assets and, consequently, are more likely to suffer from a loss, especially when sales decline. However, since strategic alignment fosters trust between supply chain partners and helps sustain a long-term collaborative relationship, strategically-aligned suppliers are more willing to invest and maintain committed resources for their customers in the early stage of the business relationship than other suppliers. In contrast, suppliers in the maturity stage of their customer relationship may not need to increase or keep relationship-specific investments when sales decline. Also, trading partners have not built a strong relationship in the early stage. The relationship and trust grow with the duration of the trading. Under this argument, we anticipate the positive effect of strategic alignment on cost stickiness is stronger in the early years of the supply chain relationship than in the mature stage. Whether the first or second effect dominates remains an empirical question.

To investigate this time-series variation in the effect of strategic alignment, we rank the duration of the relationship between the supplier and its customers into five groups. If the supplier firm-year observation falls into the first and second quintiles of the duration rank, it is considered to be in the early stage of the supply-chain relationships. Conversely, those in the fourth and fifth quintiles are deemed to be in the mature stage of their relationships. Table 11 presents the results of the analysis.

As shown in Table  11 , the coefficient of Align * Dec * Δlog ( Sales ) is negative and significant for suppliers in the early stage of the relationship (Columns (1) and (3)) but insignificant for suppliers in the mature stage of the relationship (Columns (2) and (4)). These results are consistent with our expectation that strategically-aligned suppliers are more willing to invest and retain committed resources for their customers in the early stage of the relationship, even when sales decline.

5.2.2 Controlling for the effect of product market competition and economic policy uncertainty

Li and Zhang ( 2017 ) suggest that product market competition can motivate management to hold on to slack resources when sales decline. This helps to maintain the firm’s competitive advantage. We introduce the product market competition measure ( THHI ) proposed by Hoberg and Phillips ( 2016 ) into model (4). Jin and Wu ( 2021 ) find that cost stickiness, in particular the stickiness of cost of goods sold and number of employees, decreases with the aggregate economic policy uncertainty. We account for the effect of economic policy uncertainty by including the EPU index ( EPU ), developed by Baker et al. ( 2016 ) in our model. As THHI and EPU indices are available only from 1989 and 1985, respectively, there is a drop in the number of observations for these analyses.

The results in Table  12 , after accounting for the impact of these two factors, confirm the robustness of the positive association between the supplier’s cost stickiness and the strategic alignment with its customer. The first two columns present the results when we include the industry fixed effects (defined by the two-digit SIC code). The last two columns provide the results when we include the Fixed Industry Classification (FIC300 Footnote 5 ) fixed effects.

The coefficient of EPU * Dec * Δlog ( Sales ) is significantly positive, consistent with the findings of Jin and Wu ( 2021 ), indicating that cost stickiness decreases with economic policy uncertainty. This implies that, in times of heightened economic uncertainty, firms may be more inclined to reduce costs in response to declining sales. On the other hand, the coefficient of THHI * Dec * Δlog ( Sales ) is significantly negative, aligning with Li and Zhang’s ( 2017 ) conclusion that cost stickiness increases with product market competition. This suggests that in competitive markets, firms are more likely to retain slack resources to maintain a competitive advantage, contributing to cost stickiness.

5.2.3 Controlling for the characteristics of the supply chain relationship

In this section, we address the impact of customer concentration and customer bargaining power on cost stickiness. Dhaliwal et al. ( 2016 ) find that a more concentrated customer base increases a supplier’s risk and leads to a higher cost of equity. Their findings suggest that the characteristics of the supply chain relationship can affect suppliers’ costs. We account for the effect of the concentration of customer base by dividing our sample into two sub-samples: high and low customer concentration. We then run the cost stickiness analysis separately for the two sub-samples. We use the Herfindahl–Hirschman index to measure customer concentration ( CHHI ), following the approach of Dahliwal et al. ( 2016 ). Observations with a CHHI value above the median are categorized as part of the high customer concentration group, while those falling below the median are deemed part of the low customer concentration group.

Dhaliwal et al. ( 2016 ) point out that a more concentrated customer base heightens a supplier’s risk. Hence, we anticipate that suppliers facing a more concentrated customer base would be inclined to retain resources during periods of sales decline to sustain long-term customer relationships. Thus, we predict that cost stickiness will be more pronounced in the high customer concentration sub-sample.

Regarding the effect of strategic alignment on cost stickiness under different customer concentrations, we refrain from providing a directional prediction. On one hand, strategic alignment could potentially mitigate the risks associated with customer concentration. On the other hand, its impact could vary based on the specific characteristics of the supply chain relationships in high versus low customer concentration scenarios.

The results are presented in the first four columns of Table  13 . In the COGS analysis, we observe that Align * Dec * Δlog ( Sales ) takes on a negative coefficient in both the high and low customer concentration sub-samples. However, the coefficient is statistically significant solely for the low customer concentration sub-sample. Turning to the SG&A analysis, the coefficient of Align * Dec * Δlog ( Sales ) is negative and statistically significant in both the high and low customer concentration sub-samples. When we perform a statistical test to assess the difference in the coefficient of Align * Dec * Δlog ( Sales ) between the two sub-samples, we do not observe any statistically significant distinction.

In this section, we explore the influence of customer bargaining power on the relationship between supplier–customer strategic alignment and cost stickiness. Chang et al. ( 2022 ) document that strategic aligned supplier can mitigate the negative impact of customers’ bargaining power on supplier performance because the strategic fit provides the customer long-term benefits. The customers are willing to “trade the short-term benefits obtained through supplier concessions with the long-term benefits”. Nguyen et al. ( 2023 ) observes a transfer of risk taking along the supply chain. They find that when a major customer takes more risk to increase their bargaining power and rent extraction ability, suppliers also engage in more risk taking to improve their bargaining positions. Both studies suggest that customers’ bargaining power affects suppliers’ decisions and performance.

To quantify customer bargaining power ( BP_MS ), we compute the logarithm of the ratio of a customer’s market share to the supplier’s market share. Observations with BP_MS values above the median are considered as having strong customer bargaining power, while those with values below the median are classified as having weak customer bargaining power group. We expect observations with strong customer bargaining power will exhibit higher cost stickiness.

The results, as presented in Columns (5) to (8) of Table  13 , reveal that the coefficient of Align * Dec * Δlog ( Sales ) is negative in both the strong and weak customer bargaining power groups for both the COGS and SG&A analyses. However, statistical significance is observed only for the strong customer bargaining power group. This aligns with the notion that suppliers are inclined to retain excess resources during sales decline to cultivate a long-term relationship with strategically-aligned customers possessing strong bargaining power. In exchange, these customers forego short-term benefits derived from supplier concessions in favor of longer-term benefits. A statistical test assessing the difference between the coefficients in the two groups is not significant for the COGS analysis but attains statistical significance at the 1% level for the SG&A analysis.

We also repeat this analysis using two alternative measures of bargaining power. Following Nguyen et al. ( 2023 ), we consider suppliers producing durable goods and differentiated products to have a more comparable bargaining power as their customers than those producing non-durable or more standardized products. The results using these two proxies are provided in Table  13 Panel B. Contrary to the results using the relative market share of suppliers and customers, coefficient of Align * Dec * Δlog ( Sales ) is negative and statistically significant for the durable and differentiated product group. That is, the effect of strategic alignment is most significant where the customers do not have dominant bargaining power.

One potential explanation for this result is that in addition to bargaining power, these two measures also reflect the specific investments a supplier has to make to produce differentiated/durable products. For differentiated products, the specific skills of labor and specialized machinery required are likely more significant than for standardized or non-durable products. Labor with specific skills and machinery can be difficult and expensive to acquire. Hence, suppliers with strategic alignment are less likely to fire their labor or dispose their customized machinery when sales decline.

5.2.4 The effect of strategic alignment on R&D cost stickiness

Irvine et al. ( 2016 ) note that long-term business relationships enhance customers’ willingness to share information regarding technological advancements. They argue that the firm’s patent activity experiences significant increase over the life cycle of the business relationship. Given the expected longevity of strategically-aligned partnerships between suppliers and customers, it is anticipated that they will prioritize investments in technology specific to their relationship. This, in turn, amplifies the persistence of cost stickiness in R&D. Accordingly, we re-estimate model (4) with the change in the logarithm of R&D costs as the dependent variable, and report the results in Table  14 .

The coefficient estimate of Dec* Δ Log(Sales) in Column (1) of Table  14 is negative but insignificant, which does not support the base-level R&D cost stickiness. However, the coefficient of Align * Dec* Δ Log(Sales) is significantly negative (Column (2)). This implies that strategic alignment motivates suppliers to establish enduring relationships with their customers. As a result, suppliers with strategic alignment are more inclined to retain their committed R&D resources even when sales decline. This, in turn, accentuates the persistence of R&D cost stickiness among strategically-aligned suppliers.

5.2.5 The effect of strategic alignment on supplier’s performance and relationship with customers

A key motivation for a supplier to retain surplus resources amid a sales downturn is the desire to uphold its resource-based competitive advantage and preserve the strategically-aligned customer. In this section, our investigation focuses on how strategic alignment influences the supplier’s ability to sustain a long-term relationship with the customer and its subsequent impact on the supplier’s performance.

First, we examine whether the duration of the supplier–customer relationship is affected by the strategic alignment between the two parties. Following the model proposed by Raman and Shahrur ( 2008 ), we incorporate additional variables that affect the incentives of both suppliers and customers to prolong their business association.

We use the Compustat segment data to determine the duration of a supplier–customer relationship. Under SFAS 131, suppliers are obligated to disclose customers contributing over 10% of their sales. Customers falling below this threshold are not required to be disclosed. Following Raman and Shahrur ( 2008 ), we classify customers exiting the disclosure in the Compustat segment dataset as terminating the supplier–customer relationship, aligning with the concept of “customer defection” as defined in Hollmann et al. ( 2015 ).

In computing relationship duration, we assume the commencement when a supplier–customer pair initially appears in the Compustat segment dataset and conclude it when the pair is no longer present in the segment dataset. Our analysis includes supplier–customer relationships commencing post-1978, as we cannot precisely identify the initiation date for those existing in 1978 (the first year the data is available). The duration analysis extends until 2017, with predictions for relationship termination in the subsequent year. Relationships persisting until the final year of our sample are treated as right censored. Detailed results of these analyses are reported in Table  15 .

We use three different models to examine the effect of strategic alignment on the duration of the relationship between a supplier and its major customer (Allison 2010 ). The first model uses logistic regression, where the dependent variable is an indicator variable taking a value of 1 if the supplier–customer relationship concludes in the subsequent year, and 0 otherwise. In contrast, the Cox and Weibull models gauge relationship duration as the number of years the relationship lasts, treating it as a continuous variable.

Results from the logistic analysis are reported in the first column of Table  15 . The coefficient for Align is negative and statistically significant at the 5% level. This implies that suppliers with strategic alignment are less likely to terminate their relationships compared to those without such alignment. Additionally, factors contributing to a lower likelihood of termination include the customer constituting a significant portion of sales revenues, the supplier being larger and more mature. On the other hand, the probability of relationship termination increases if the supplier experiences negative free cash flows and the customer purchases a substantial portion of materials from the supplier.

Second, we estimate a Cox proportional hazards model. Introduced by Cox ( 1972 ), this model serves as a semi-parametric generalization of the Weibull model. Unlike the Weibull model, the Cox model’s baseline hazard function does not necessitate specific distributional assumptions. This makes it particularly advantageous when distributional assumptions of the Weibull model are unmet or challenging to verify, rendering the Cox model more robust (Allison 2010 ). In this model, the hazard of relationship termination is assumed to vary based on the same set of explanatory variables as in the logistic model. Given that the estimation of this model is grounded in the hazard function, a negative coefficient for a given explanatory variable suggests that a higher value of that variable corresponds to a longer relationship duration.

Results derived from the Cox model are reported in Columns (2) and (3) of Table  15 . The coefficient of Align is negative and statistically significant at the 1% level. This suggests that a closer strategic alignment between a supplier and a customer is associated with a longer duration of their relationship. The control variables are similar to those in the logistic model.

Third, we estimate the relationship duration between suppliers and their major customers using the Weibull model and report the results in Column (4) of Table  15 . In contrast to the prior two models, a positive coefficient in the Weibull model indicates that an increase in the explanatory variable is associated with a longer survival time. The coefficient of Align is positive and statistically significant at the 1% level in Column (4). This result suggests that when a supplier and a customer share a closer strategic alignment, their relationship tends to endure for a longer duration. This outcome aligns with the results reported by Chang et al. 2021 .

Beyond the examination of relationship duration, we explore whether strategic alignment influences the supplier’s performance. We anticipate that suppliers benefit from extended relationships with strategically-aligned customers, as reflected in their willingness to invest and retain slack resources during sales declines. Hence, we hypothesize that strategic alignment between the supplier and the customer can enhance the supplier’s performance on average.

To assess the impact of strategic alignment on supplier performance, we incorporate additional control variables that have been identified as influential factors, following the suggestions of (Chang et al. 2022 ). We use model (5) to estimate the effect of strategic alignment on the supplier’s performance.

where ROA_S is the suppliers’ return on assets. It is computed as the ratio of income before extraordinary items to total assets. The key explanatory variable is Align , our measure of strategic alignment. The vector of controls includes variables correlated with supplier performance. These include lagged return on assets in year t −  1 for the supplier ( ROA_S t −  1 ), the relative market share of the customer to that of the supplier ( BP/MS ), supplier’s (customer’s) firm size ( Size_S , Size_C ), supplier’s (customer’s) firm age ( Age_S, Age_C ), supplier’s (customer’s) annual sales growth in percentage ( Growth_S, Growth_C ), an indicator variable for a multi-segment supplier firm ( SEG_S ), supplier’s financial leverage ( FLEV_S ), and the duration of the supplier–customer relationship ( Duration ).

Results from the performance analysis (Table  16 ) indicate a positive association between strategic alignment among suppliers and customers and the suppliers’ performance. Our findings suggest that suppliers derive performance benefits from strategic alignment with their customers. This finding provides insights into the supplier’s willingness to bear the cost of maintaining slack resources during a sales decline for strategically-aligned customers.

Overall, the results from the above two tests consistently support the notion that suppliers derive benefits from strategic alignment with their customers. The evidence indicates that suppliers, engaged in strategic alignment, are inclined to foster longer-term relationships and exhibit better performance. This aligns with the argument that strategic alignment within the supply chain contributes to the development of a resource-based competitive advantage. The findings underscore the significance of strategic alignment in fostering enduring and beneficial relationships along the supply chain.

5.2.6 Endogeneity of strategic alignment

In our investigation, we document a significant association between a supplier’s cost stickiness and its strategic alignment with its major customer. While we control for various factors influencing a company’s cost behavior, it remains plausible that unobserved constructs drive both cost stickiness and strategic alignment. This raises concerns about potential spurious relationships. To mitigate the impact of endogeneity, we adopt the propensity score matching (PSM) procedure. This involves identifying a set of control firms (i.e., suppliers lacking strategic alignment with major customers) whose propensity scores closely align with the treatment firm (i.e., a supplier with strategic alignment).

In executing the propensity score matching (PSM), we first quintile-rank all observations based on their strategy scores within each industry and year. We then construct an indicator variable, Align_rank , which takes a value of 1 if the strategy scores of the supplier and its major customer fall within the same quintile rank and 0 otherwise. Next, we estimate a logistic regression that includes control variables as per Chang et al. ( 2022 ) model (1) to generate propensity scores, representing the likelihood that a supplier has strategic alignment with its major customer (i.e., Align_rank  = 1). The matching process involves pairing each treatment firm ( Align_rank  = 1) with a control firm ( Align_rank  = 0) possessing the closest propensity score. A one-to-one match without replacement is performed, with a caliper distance set at 0.01 to generate a matched sample (Gong and Luo 2018 ). This matching procedure yields 3783 treatment observations and matched control observations. Table 17 Panel A displays the covariate balance following the PSM, while Panel B presents the results derived from this PSM sample.

The absence of a significant difference in covariates after the PSM procedures indicates the effectiveness of the matching technique. When employing this PSM pre-treated sample in the cost stickiness analysis, the continued observation of a significant negative coefficient for Align * Dec * Δlog(Sales) underscores the robustness of our results to the PSM procedures.

6 Conclusion

This study examines the impact of strategic alignment on the cost stickiness of suppliers. Using a dataset of supplier–customer dyads from 1978 to 2018, we find that strategic alignment increases the stickiness of suppliers’ COGS and SG&A costs. This is attributed to the trust fostered by strategic alignment, motivating suppliers to cultivate long-term collaborative relationships with their customers. The willingness of strategically-aligned suppliers to bear the costs of holding committed resources during sales declines and their strengthened commitment to invested R&D expenditures substantiate the impact of strategic alignment on cost behavior.

This study also provides insights by revealing that the influence of strategic alignment on cost stickiness is more pronounced in the early stages of supply chain relationships, aligning with the idea that significant relationship-specific investments are made during this period. Additionally, the examination of cross-sectional variations, such as the concentration of the supplier’s customer base and customer bargaining power, enriches the understanding of how strategic alignment affects cost stickiness in diverse contexts. A consequence of strategic alignment is a longer duration of the supplier–customer relationship and enhanced performance.

Our study extends the literature on the impact of strategic alignment on corporate behavior (Ashfaq and Raja 2013 ; Schreiner et al. 2009 ; Vachon et al. 2009 ). By examining how strategic alignment influences suppliers’ expectations about future sales and their decisions regarding resource commitment and retention, this study provides a valuable perspective on the broader implications of strategic alignment in supply chain relationships. Future studies can further explore other potential channels that strategic alignment can influence a company’s policy and behavior.

We also perform a sensitivity test using a three-year rolling average for each ratio and require only four years of consecutive data in Compustat. Our results are robust to this alternative construct method. Results are provided in the Sensitivity Analysis section.

We perform a sensitivity analysis using the weighted-average of all major customers’ strategy scores to construct the alignment measure. Results are reported in the Sensitivity Analysis Section.

Following Bentley et al. ( 2013 ), we exclude reactors strategy from our sample as it is short-term and unstable.

For both the three-year and five-year rolling averages, we require there to be at least three observations to compute the average. Any observations with fewer than three observations in the prior three (five) years are considered as having missing values for the three-year (five-year) rolling average.

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Appendix 1: Business strategy alignment examples

This appendix provides descriptive examples of supplier–customer duet having high strategic alignment scores in our example. We classify the strategies of both suppliers and customers into Prospector, Analyzer, and Defender. Prospectors are companies with a strategy score range of 24–30; Analyzers are those with a strategy score range of 13–23; and Defenders are those with a strategy score range of 6–12.

• a In this example, the strategy score of Cummins Inc. is based on data in 2013
• b In this example, the strategy scores of Dana and Pacca r are based on data in 2010 and 2011, respectively
• c In this example, the strategy scores of Cummins Inc. and Paccar are based on data in Year 2012

Appendix 2: Variable definitions

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Chiang, CH., Choy, H., Yang, YJ. et al. Strategic alignment between supply chain partners and cost stickiness of suppliers. Rev Quant Finan Acc (2024). https://doi.org/10.1007/s11156-024-01324-x

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Framework for integration of health monitoring systems in life cycle management for aviation sustainability and cost efficiency.

1. Introduction

• Designing aircraft and components in modular formats simplifies the process of upgrades and replacements. This not only reduces waste but also allows for the adaptation to new technologies or performance enhancements without the need for complete overhauls. The use of a modular format will also allow standardized modules to be used across different types and models of aircraft. This, in turn, will significantly reduce the time required for the development and certification of new aircraft types.
• Aircraft should be designed with the end-of-life stage in mind, ensuring that components can be easily disassembled for recycling or refurbishment. This approach minimizes waste and supports the industry’s transition toward a circular economy.
• Implementing the use of sustainable materials and adopting greener manufacturing processes play a critical role in reducing the environmental impact. This includes the exploration of alternative fuels and advanced composites that offer durability and recyclability.

1.1. LCM in Aviation

1.2. advanced health monitoring systems of aircraft, 1.3. modular design in aircraft construction, 1.4. practical applications and case studies, 1.5. life cycle impact on aviation sustainability, 2. materials and methods, 2.1. methodology, 2.2. fundamentals of ahmss and integrating lcm for sustainability and cost efficiency in aviation, 2.2.1. aircraft life cycle management: principles, strategies, and challenges.

• Design. This initial stage considers environmental restrictions, materials choice, and budget control. The focus is on creating an aircraft that meets environmental standards while optimizing costs.
• Production. During manufacturing, environmental requirements are adhered to, and a recycling approach is implemented. Budget optimization is a key consideration to ensure cost-effective production.
• Operation (Initial): As the aircraft enters service, environmental limitations are observed. Green modifications may be implemented to improve sustainability, and cost-control measures are put in place.
• Maintenance. A LEAN/Continuous Improvement (CI) approach is adopted for maintenance processes. Waste control is emphasized, and cost optimization strategies are implemented to reduce maintenance expenses.
• Operation (Ongoing). Throughout the aircraft’s operational life, a green policy is followed. The use of sustainable fuels is promoted, and ongoing cost-control measures are maintained.
• Retirement. As the aircraft nears the end of its service life, environmental requirements are considered for the retirement process. A parts-out and reuse strategy is employed to maximize value and minimize waste. Cost-saving measures are implemented during this phase.
• Disposal. The final stage involves green dismantling practices to minimize environmental impact. Materials from the aircraft are recycled where possible. This stage also focuses on encashment or recovering value from the retired aircraft.
• LCM considers all stages of an aircraft’s life, from design, production, operation, and maintenance, to retirement and disposal.
• A key principle of LCM is sustainability, ensuring that operations minimize environmental impact through efficient resource use and adherence to environmental regulations.
• LCM aims to maximize financial efficiency by reducing costs at every life cycle stage without compromising safety or performance.

2.2.2. Strategies in Aircraft LCM

• Design for Sustainability, which focuses on creating airframes and components that are durable, maintainable, and recyclable, often employing modular design to facilitate easy upgrades and replacements, thereby extending the aircraft’s operational life and reducing waste.
• Sustainable Manufacturing, which scrutinizes production processes for efficiency and environmental impact, balancing quality with ecological and economic considerations in material selection, production techniques, and assembly methods.
• Operational Efficiency, which concentrates on maximizing fuel economy, optimizing routes, and managing load to reduce costs and environmental impact during the aircraft’s service life.
• Advanced Maintenance Strategies, incorporating scheduled maintenance carefully planned to ensure safety and reliability, often enhanced by the integration of advanced health monitoring systems to improve predictability and efficiency of maintenance operations.
• Life Extension Programs, including overhaul and refurbishment initiatives to revitalize aging aircraft and prolong their service life.
• End-of-Life Management, focusing on the complex process of decommissioning aircraft, aiming to recycle and reuse as much of the aircraft systems as possible, following a circular economy model.
• Continuous Improvement and Innovation, which involves ongoing research and development to incorporate new technologies and methodologies that can enhance the aircraft’s performance, efficiency, and sustainability throughout its life cycle.

2.2.3. Architecture and Functions of Advanced Health Monitoring Systems in Aviation

2.3. framework for aviation life cycle management: integrating ahmss for enhanced performance and sustainability, 3.1. the symbiosis of ahmss and modular design in aircraft construction.

• Modular design allows for the easy replacement or upgrading of individual components, rather than entire systems, making aircraft more adaptable to changes in technology and market demands.
• By facilitating targeted repairs and upgrades, modular design can significantly reduce the costs associated with aircraft maintenance and downtime.
• Modular systems can be more easily disassembled, repaired, refurbished, or recycled, leading to a reduction in material waste and promoting a circular economy within the aviation sector.
• AHMSs, with their network of sensors and diagnostic algorithms, offer constant surveillance of aircraft components, providing vital data that inform maintenance decisions and module replacements.
• By predicting maintenance needs, AHMSs enable proactive servicing of modules, which can prevent cascading failures and prolong the lifespan of the aircraft.
• The data collected by AHMSs can lead to the continuous improvement of module design, with insights into wear patterns and operational stresses informing future design iterations.

3.1.1. General Model with Decision-Making Criteria for Modular Design and AHMSs in Aircraft Construction

• Indicates that the total life cycle cost savings justify the modular investment Δ L C C > 0 .
• Demonstrates maintenance cost savings Δ M C > 0 .
• Shows clear operational efficiency improvements Δ O E > 0 .
• Measures the improvement in operational efficiency that a new system or technology brings over an existing or older system.
• Can include factors like reduction in downtime, increased throughput, better fuel efficiency, or other performance improvements.
• Translate efficiency improvements into financial savings or revenue enhancements.
• Be able to be quantified by assessing cost savings from reduced downtime, increased output, or lower operational costs due to improved efficiency.
• Involve converting the operational efficiencies into the money value or other measurable benefits.
• Suppose the traditional system has an efficiency rating of 80% due to older technology.
• The new system has an efficiency rating of 95% due to better technology and reduced machine downtime.
• Δ O E = 95% − 80% = 15%
• This 15% improvement represents the comparative gain in operational efficiency due to the new system.
• The 15% improvement in efficiency reduces downtime by 100 h annually, with each hour of downtime costing the company USD 500 in lost production.
• The annual financial benefit from improved operational efficiency would be 100 h * USD 500/h = USD 50,000.
• This USD 50,000 represents the B O E , translating the efficiency gain into a financial metric.

3.1.2. Steps to Measure Benefits from Operational Efficiency

• Calculate the reduction in hours that the system is non-operational due to maintenance or failures.
• Measure improvements due to better maintenance and operation practices.
• Evaluate the reduction in the number of maintenance events required per unit time.
• Determine any increase in the availability and usage rate of the assets.
• Convert the reduced downtime and lower maintenance frequency into direct cost savings by multiplying the reduced hours or events by the cost per h/event.
• Increased asset utilization and fuel efficiency can be translated into cost savings.
• If operational efficiency allows for more operations within the same timeframe, calculate the additional revenue generated from these operations.
• Improved operational efficiency often leads to better quality products or services and fewer penalties for non-compliance, which can be quantified if data are available.
• Calculate the return on investment (ROI) by dividing the total financial benefits (cost savings plus any additional revenue) by the total costs associated with implementing the improvements.
• Project the future cash flows from operational efficiencies over time and discount them to their present value to determine the net present value (NPV). This approach helps in understanding the long-term value of the investments in efficiency improvements.
• Develop specific key performance indicators (KPIs) linked to operational efficiency, such as Mean Time Between Failures (MTBF), Mean Time to Repair (MTTR), and Overall Equipment Effectiveness (OEE). Monitor these KPIs over time to assess improvements and correlate these with financial performance.
• Compare the performance before and after the implementation of AHMSs or other efficiency-improving technologies within various departments or sectors of the organization.
• Compare the performance with industry standards or competitors to gauge how operational efficiency gains stand relative to the market or industry.

3.1.3. Steps to Define Annual Maintenance Cost

• Traditional system maintenance is typically scheduled based on time or usage intervals, without regard to the actual condition of components unless visible signs of wear or failure are evident.
• Modern system maintenance is driven by data from AHMSs, which monitors the condition of components in real time and predicts their failure, allowing for maintenance to be scheduled only when necessary.
• Consider the man-hours required for both routine and unexpected maintenance tasks. For the traditional system, labor hours are generally fixed by schedule. For the modern system, labor hours should be reduced due to efficiency gains from targeted maintenance.
• Estimate the costs of consumables, spare parts, and other materials. In traditional systems, parts might be replaced more frequently as a precaution, while in modern systems, part replacement is condition-based, potentially reducing waste.
• Include costs associated with the use of maintenance tools and facilities. Modern systems may require specialized diagnostic equipment.
• Both systems incur overhead costs related to the maintenance operations, including utilities, facility usage, and administrative support. However, these might be lower for modern systems due to more efficient maintenance operations.
• The impact of maintenance-related downtime on operations is a crucial cost factor. Modern systems aim to minimize downtime by scheduling maintenance optimally and performing quicker, more precise repairs.
• Post-maintenance operational efficiency can affect productivity and operational costs. Modern systems may maintain higher efficiency through better maintenance accuracy.
• Aggregate the costs from all the above factors to define M a n n u a l , i for both the traditional and modern systems.
• Labor: 100 h/year × USD 50/h = USD 5000
• Materials: USD 3000
• Equipment: USD 500
• Overheads: USD 1000
• Downtime: 50 h × USD 100/h = USD 5000
• Efficiency Loss: USD 2000
• Labor: 70 h/year × USD 50/h = USD 3500 (reduced due to efficiency)
• Materials: USD 2000 (reduced due to condition-based replacements)
• Equipment: USD 1000 (increased due to specialized diagnostics)
• Overheads: USD 800
• Downtime: 20 h × USD 100/h = USD 2000 (significantly reduced)
• Efficiency Gain: −USD 1000 (savings due to increased operational efficiency)
• For traditional systems as components age, the frequency and cost of maintenance may increase due to wear and tear, leading to higher costs as tt increases. A time-dependent function allows this trend to be accurately modeled, reflecting the growing need for repairs and replacements over time.
• For modern systems with AHMSs and other technologies, maintenance costs might initially be higher due to the integration of new technologies but could decrease over time as the system becomes more efficient at predicting and mitigating wear before it leads to failures.
• Both types of systems may undergo upgrades that improve their efficiency or change their maintenance requirements. For instance, software updates in modern systems might improve predictive algorithms, potentially reducing maintenance costs over time.
• If operational intensity or conditions change, this can affect the wear rate of components and thus the maintenance schedule. M a n n u a l , i t can adapt to these changes more fluidly than a fixed model.
• Using M a n n u a l , i t aids in more accurate budgeting and financial planning, as it provides a clearer forecast based on the expected evolution of maintenance costs rather than a flat rate that might not reflect reality accurately.
• Including M a n n u a l , i t in the life cycle cost analysis provides a more nuanced understanding of how costs will develop over the asset’s life. This helps in making more informed decisions about investments, retirements, or replacements of systems or components.
• Maintenance costs might increase or decrease linearly due to predictable wear or improvements in maintenance techniques:
• If maintenance costs accelerate over time due to aging or decrease due to significant efficiencies gained from technology, an exponential model might be appropriate:
• If a major overhaul or upgrade is scheduled at a specific time, this might result in a step change in the maintenance cost function.

3.1.4. Case Study—Enhancing Cost Efficiency through AHMS-Driven Modular Architecture Selection in Aviation Equipment

3.1.5. case study—enhancing structural inspections and maintenance cost efficiency through ahms-driven modular architecture selection in aviation equipment, 3.1.6. impact symbiosis of ahmss and modular design in aircraft construction on aviation sustainability, 3.2. enhanced model with decision-making criteria for ahmss and new technologies integration.

• The modern system, despite potentially higher initial costs due to advanced technology investments, offers significant reductions in total life cycle costs through savings in maintenance and operational efficiencies.
• The integration of AHMSs with IoT and AI not only optimizes maintenance strategies but also enhances the overall operational capabilities of the aircraft, leading to fewer delays, quicker response times, and improved service reliability.
• Investing in modern technologies aligns with industry trends toward digital transformation and sustainability, positioning the organization as a leader in innovative aviation solutions.

3.2.1. Technological Components for Implementation of the Model

• IoT devices are embedded sensors and actuators that collect real-time data from various aircraft systems. These devices are critical for real-time data acquisition and communication, enabling the continuous monitoring of aircraft systems.
• AI involves the use of machine learning models and algorithms to analyze large volumes of data, identify patterns, make predictions, and provide actionable insights. AI technologies are crucial in processing and analyzing data at all stages, from local processing on the aircraft to integrative analyses at MRO centers and fleet-wide analytics.
• Blockchain technology is used to ensure the integrity and security of the data across the system. It provides a secure, immutable ledger for recording and sharing information about aircraft maintenance and operations, which helps in maintaining transparency, traceability, and compliance.
• IoT primarily facilitates real-time data capture directly from aircraft sensors, which are then used by AI for initial assessments and prioritized for action based on system requirements. Blockchain ensures that each piece of collected data can be traced back to its origin, maintaining the integrity of the data throughout the system.
• IoT ensures the data collected are transmitted in real-time and secured via encryption technologies. AI optimizes these data for efficient transmission, while blockchain ensures the transmitted data are logged securely and remain tamper-proof.
• At the local processing level, IoT handles immediate data acquisition and preprocessing, whereas AI performs detailed analysis to detect issues that can be acted upon locally. When data reach the MRO level, they are further integrated and analyzed with AI to discover broader trends or issues, with blockchain ensuring all data integration steps are secure and verifiable.
• IoT collects comprehensive data from the entire fleet, which AI uses to perform extensive analyses, predicting potential future failures and maintenance needs. Blockchain secures these analyses, ensuring that the predictions and the data they are based on are reliable and protected.
• IoT provides the operational data necessary for effective maintenance scheduling, while AI optimizes the scheduling based on predictions and operational data. Blockchain verifies that the maintenance performed matches the scheduled tasks and records this in a tamper-proof ledger, crucial for regulatory compliance.
• IoT offers the necessary real-time data to inform decisions, AI provides deep analysis and predictions to guide these decisions, and blockchain ensures that all decisions made, and actions taken, are recorded and verifiable, adding a layer of security and compliance.
• IoT captures real-time deviations in aircraft systems, AI analyzes these deviations to determine if they signify true anomalies, and Blockchain ensures that all detected anomalies are logged securely for future reference and analysis.
• IoT provides the necessary operational data, AI uses these data to determine the most appropriate type of maintenance (preventative, corrective, overhaul), and Blockchain secures these recommendations against tampering.
• IoT provides system availability data, AI optimizes the maintenance scheduling for efficiency, and Blockchain ensures that schedules are adhered to and cannot be altered without proper authorization.
• IoT monitors resource levels, AI allocates resources efficiently based on current needs and future predictions, and Blockchain records these allocations to ensure they are followed and can be audited.
• IoT devices continuously monitor the systems for performance metrics, AI predicts failures before they occur to prevent downtime, and Blockchain ensures that these predictions and the basis for them are permanently recorded.
• IoT gathers data across operations, AI analyzes these data to find inefficiencies, and Blockchain records any operational changes made based on these analyses to ensure they meet regulatory standards.
• IoT ensures all necessary data for compliance are collected, AI automates the process of checking these data against compliance standards, and Blockchain creates a permanent record of the compliance status.
• IoT collects data relevant to safety, AI predicts and mitigates potential safety issues, and Blockchain provides a secure log of all safety-related data and actions.
• IoT tracks all operational costs, AI analyzes these costs to identify potential savings, and Blockchain ensures that any financial decisions are transparent and traceable.

3.2.2. Framework to Data Presentation Requirements

• Utilize dashboards that offer clear, intuitive visualizations such as graphs, gauges, heat maps, and alert indicators that can quickly convey the status of aircraft health and operational metrics.
• Ensure that the data presented are accurate and up to date, reflecting the latest readings and analyses to avoid any misinterpretations that could lead to incorrect decisions.
• Provide real-time data streaming to ensure that maintenance teams, flight operators, and management have the most current information, enabling prompt decision-making, especially for critical operational adjustments and anomaly responses.
• Minimize latency in data presentation to enhance the responsiveness of the system, particularly critical for real-time monitoring and anomaly detection functionalities.
• Customize dashboards and reports based on user roles (e.g., technicians, engineers, management, regulatory bodies), ensuring that each user accesses relevant information necessary for their tasks and decision-making.
• Allow users to interact with the data, such as drilling down into specific metrics or adjusting parameters to view different scenarios, which can aid in deeper analysis and understanding.
• Ensure seamless integration with existing aviation management systems, such as MRO software, flight tracking systems, and logistics platforms, to allow unified and comprehensive data analysis.
• Standardize data formats across different systems and platforms to ensure compatibility and ease of data integration and analysis.
• Implement strong encryption for data at rest and in transit to protect sensitive information from unauthorized access and cyber threats.
• Utilize blockchain for certain aspects of data logging and sharing to enhance security, provide data integrity, and ensure compliance with regulatory standards.
• Maintain detailed audit trails for all actions taken based on the system’s data, which are crucial for compliance and investigation purposes.
• Provide mobile access to the system through secure apps or web portals, allowing decision-makers and technicians to view and respond to data remotely.
• Implement redundancy in data storage and presentation services to ensure availability even in the case of hardware or network failures.
• Design the data presentation layer to be scalable, accommodating increases in data volume without degradation in performance.
• Ensure the system is easy to update and maintain, with support for adding new functionalities or integrating additional data sources without significant overhauls.

3.2.3. Sustainable Aviation Life Cycle Management through an Ontology-Based Approach

• Representation of data and relationships;
• Querying and reasoning over the ontology;
• Enforcing constraints that align with the health management policies and practices.
• Modification events can lead to configuration changes captured in STC (Supplemental Type Certificate) and SB (Service Bulletin) entities.
• Defects may be discovered during operation.
• Routine Maintenance activities involve removing, installing and replacing Parts.
• The aircraft goes through a life cycle from Requirements to AD (Airworthiness Directive) to being Installed and put into operation.
• An Aircraft is made up of an Airplane and APU.
• An Airplane has a specific Type and Model.
• Maintenance activities performed on an Aircraft are logged in ICA (Instructions for Continued Airworthiness) documents.
• Parts Installed on an Aircraft may be Removed during Maintenance.
• The AD, LLP (Life Limited Parts), Engine, EO (Engineering Order), and AMP (Aircraft Maintenance Program) all capture airworthiness and maintenance requirements that must be fulfilled during the Aircraft’s service life.

3.2.4. Impact of New Technologies Integration in AHMSs on Aviation Sustainability

3.3. enhanced model with decision-making criteria for ahms-supported component utilization and life extension, 3.3.1. general model.

• Indicating that the AHMS-supported system offers total life cycle cost savings Δ L C C > 0 .
• Showing significant maintenance cost reductions Δ M C > 0 .
• Demonstrating effective extension of component lifespans Δ L E > 0 .

3.3.2. The Life Cycle Management and End-of-Life Decisions for Aircraft Components

3.3.3. case study—optimization of longevity for technical systems from economic and technical perspective, 3.3.4. impact of ahms-supported component utilization and life extension on aviation sustainability, 4. discussion.

• The study predominantly relied on simulated data and theoretical models to evaluate the impact of integrating LCM, AHMSs, and modular design in the aviation industry. While simulations are invaluable for predicting system behaviors under controlled settings, they may not fully capture the dynamic and unpredictable nature of real-world operations.
• The generalizability of the results may be limited as simulated environments typically simplify complex variables and interactions that occur in actual aviation operations.
• The study acknowledges the potentially high initial costs associated with implementing AHMSs and modular design but does not provide a detailed analysis of these costs across different types of aircraft and operational scales. This omission could affect stakeholders’ understanding and planning for such investments.
• Implementation challenges, such as the need for extensive training, changes to existing processes, and regulatory approvals, were not deeply explored. These factors are critical for real-world applications and can significantly hinder the adoption of the proposed strategies.
• This research assumes a level of standardization in the technology and practices involved, which may not currently exist in the highly fragmented aviation industry. The lack of standardization can impede the scalability of AHMSs and modular designs, limiting their broader application.
• The adaptability of the study’s findings across different regions and regulatory environments is not addressed. Regulatory disparities can influence the feasibility of implementing new technologies in global aviation markets.
• Future studies should incorporate real-world operational data to validate the models and simulations used in this study. This would provide a more accurate reflection of the practical implications of integrating AHMSs and modular design in aviation.
• Development of models to predict the investment needed and the potential return on investment for airlines considering the adoption of these technologies, including factors like maintenance savings, reduced downtime, and extended component lifespan.
• Development of industry-wide standards for AHMSs and modular design to facilitate broader adoption and interoperability across the aviation sector.
• Research on the scalability of these technologies in diverse regulatory and operational environments to guide global adoption strategies.
• Investigate the human factors involved in the transition to more technologically advanced maintenance and life cycle management systems, ensuring that these technologies enhance rather than complicate human performance and decision-making.

5. Conclusions

Author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Kabashkin, I.; Perekrestov, V.; Tyncherov, T.; Shoshin, L.; Susanin, V. Framework for Integration of Health Monitoring Systems in Life Cycle Management for Aviation Sustainability and Cost Efficiency. Sustainability 2024 , 16 , 6154. https://doi.org/10.3390/su16146154

Kabashkin I, Perekrestov V, Tyncherov T, Shoshin L, Susanin V. Framework for Integration of Health Monitoring Systems in Life Cycle Management for Aviation Sustainability and Cost Efficiency. Sustainability . 2024; 16(14):6154. https://doi.org/10.3390/su16146154

Kabashkin, Igor, Vladimir Perekrestov, Timur Tyncherov, Leonid Shoshin, and Vitalii Susanin. 2024. "Framework for Integration of Health Monitoring Systems in Life Cycle Management for Aviation Sustainability and Cost Efficiency" Sustainability 16, no. 14: 6154. https://doi.org/10.3390/su16146154

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