A Developed Model and Fuzzy Multi-Criteria Decision-Making Method to Evaluate Supply Chain Nervousness Strategies

Abstract

Nervousness is thought to be a source of confusion, instability, or uncertainty in SC systems due to disruptions and frequent changes in decisions. Nervousness persists even with consistent SCs, which arise from planning flexibility in response to changes, where responsiveness and customer satisfaction balance. Although the term “nervousness” is well known, to our knowledge no prior research has examined and explored supply chain nervousness strategies (SCNSs). This research explores supply chain nervousness strategies, factors, reduction methods, and recent trends in the supply chain’s relationship with nervousness. The main purpose of this research is to determine the comprehensive and relevant nervousness strategies in the supply chains, especially in light of the unprecedented development and change in business, economics, and technology and the fierce competition. SCN strategies are introduced in a developed model to designate SCN measurements and indicators, mitigation strategies and stages, and management strategies. The fuzzy PROMETHEE method is employed to rank the strategies based on their importance and order of implementation. The suggested method for managing nervousness is then presented with a numerical case, along with the results. The research outcomes indicate that the top five strategies for managing nervousness include planning continuity, utilizing technology, managing nervousness, improving the SC cyber system, and managing supplies. The findings assist decision makers, practitioners, and managers in focusing on SC improvement, resilience, and sustainability.

1. Introduction

Nervousness in the supply chain is primarily brought on by decisions that are hampered by external or internal activity, changes, and disruptions. SC instability has a direct impact on nervousness and is also influenced by it. Organizations, clients, the economy, and enterprises are all impacted by supply chain nervousness, whether it is favorable or negative. Income and profit volatility drive up dissatisfaction, unrest, and investment costs. Nervousness is a general characteristic of markets and economies. SCN has a significant impact on inventories, planning, production, profit, and a number of other erratic and fluctuating outcomes. The COVID-19 crises brought attention to SC worries about nervousness, its causes, remedies, and preventative actions [1]. In order to ensure the continuity of the product flow during crises, nervousness countermeasures should provide long-term economic rewards for individual producers, businesses, and the other sectors.

As a result of globalization, innovative SC models are more necessary to address the growing SC nervousness. Nervousness is brought on by variances and changes in supply and logistics systems. The bullwhip effect is one of the main effects of nervousness [2]. There are typically two main causes of nervousness: normal and abnormal changes. There are two different types of remedies, one is to lessen internal SC changes and the other is to lessen external SC changes. Measures of the nervousness impact include price, value, and timeliness. Nervousness exists even with steady SCs. Nervousness results from the planning flexibility in response to SC changes, where there is a tradeoff between responsiveness and customer satisfaction and nervousness. Nervousness planning is comparable to planning instability, in which the plans are changed to meet the varying actual requirements [3]. Planning nervousness results in disruptions in production plans, disruptions in delivery, increased inventory buffers, rescheduling, and increased planning costs.

Scholars rarely consider the global SCN systems and strategies but focus on specific nervousness aspects with limited analysis and details [4]. Because of its importance, especially with regard to unexpected disruptions due to crises like the COVID-19 pandemic, nervousness surfaced as a crucial part to be considered during SC design, planning, and the decision-making process. While the prior studies provide some good insights, there are still specific research gaps when they are applied to SCN plans and analysis.

1.1. The Current Gaps

This study outlines the work conducted so far with regard to defining and assessing SCN and outlines the gaps the current study needs to fill.

• The previous research did not consider the entirety of SC nervousness, SCN strategies, and elements and did not pay particular attention to the mitigation implementation stages, measurements indicators, and their impact on SC efficiency and SCN management strategies; in addition, the nervousness features of complex supply chain systems should be explored. Relying only on two basic nervousness parameters, namely possibility and consequences, will certainly lead to losses in nervousness information and analysis and will not differentiate between the levels of different nervousness factors when large, complicated supply chains with different nervousness events, different objectives, and interrelationships among partners are studied.
• There is a shortage of prior studies about the nervousness strategies and assessment methods, and they show weakness and disadvantages in their practical application, especially when quantitative analysis of SCN must be performed in a very risky and uncertain environment. Furthermore, the SCN information is difficult to obtain, making nervousness assessment even more challenging.
• Any SC design, plans, and strategies should include a means of dealing with an SC nervousness management system.

1.2. Research Contributions, Questions, and Objectives

The uniqueness of this research is that it examines in depth the nervousness strategies of the supply chain and the internal and external factors that increase or affect this nervousness, the nervousness indicators, the ways to execute a mitigation procedure, the methods of treatment, and the recent trends in the supply chain and its relationship with nervousness. It analyzes management strategies and their impact on the nervousness of the supply chain. Given the extraordinary advancements and changes in business, economics, technology, and science, as well as the fierce rivalry that defied all preconceived notions, this research offers a model for SCN strategies. Furthermore, the study proposes a framework to prioritize the SCN strategies based on performance measures using the MCDM technique—the fuzzy PROMETHEE method.

Therefore, in the current high level of global competition and the rise of crises such as the COVID-19 situation, it is crucial to first determine the various nervousness strategies in the supply chain and then calculate a clear priority for each strategy. The study questions were identified based on the gaps in previous studies. This simplifies the expression of the following four important research questions that this research has answered.

RQ1: What are the main nervousness strategies of the supply chain?

RQ2: What is the priority of each nervousness strategy in the supply chain?

RQ3: What are the mitigation implementation strategies, stages, and procedures?

RQ4: What measuring parameters are required to assess the SCN?

In this article, a model of supply chain nervousness strategies is proposed and developed. The model embeds factors of the evaluation, execution, and management of SCN; it also proposes protective measures in the supply chain, to mitigate future disruptions. Depending on the supply chain’s resources, management approach, and design specifications, certain pressure points in the chain may be under pressure or not. The main purpose of this research is to determine the comprehensive and relevant nervousness strategies in the supply chain. The company has the control right to adjust the factors behind SCN utilizing SCN strategic aspects. The study then uses the fuzzy PROMETHEE technique to determine the priority of the identified SCN strategies. The proposed supply chain nervousness model is based on experts’ opinions in the logistics and SC field and an extensive literature review.

Fuzzy PROMETHEE (Ranking for Organization Method for Enrichment Evaluation) is an MCDM structured technique that can be utilized to assist decision makers in selecting the best decision. It is a key method for assessing alternatives in multi-criteria decision-making issues in relation to the criteria. It is distinguished by a wide variety of preference functions, which are applied to allocate the differences between alternatives in judgments. A strategy that deals with unclear data and imperious knowledge is fuzzy logic. Fuzzy PROMETHEE can be used by decision makers to help them make decisions in situations where there is ambiguity.

Through a literature survey, it turns out that the research that deals with the nervousness of the supply chain is limited and rare. There are no studies looking at the strategies, procedures, and adaptation to deal with nervousness. This study’s main objectives are to examine the management strategies to deal with nervousness in the supply chain, to analyze the SCN impact, to introduce means of measuring SCN, and to propose an appropriate mitigation plan to address this problem. Also, the F-PROMETHEE method was used to identify the best strategies and arrange them according to the priority of their implementation and consideration by companies. Furthermore, the research establishes a model for dealing with the nervousness strategies in the supply chain so that decision makers in the supply chain can benefit from it in the light of globalization, disruptions, and instability in the supply chain; ultimately, this leads to a sustainable SC and increases the resilience, responsiveness, and competitiveness of the future SCs.

1.3. Methodology

As shown in Figure 1, the research’s methodology includes an analysis of earlier studies, a collection of the required data and information, and a definition of the gaps in the previous studies, research questions, and contributions. The method of study in this research includes an analysis of recent nervousness in the supply chain, the main factors and strategies, the measurement methods, and the execution steps. Next, the supply chain nervousness strategies model is presented to designate the SCN measurements and indicators, mitigation strategies and stages, and management strategies. The suggestions explore the utilization of the SCN strategies to provide an innovative solution to the SC problem, to minimize the effect of nervousness on the SCs, and to improve SC resilience. After that, an MCDM approach is utilized to evaluate and prioritize five strategies based on six performance measures. Expert input data are employed in the fuzzy PROMETHEE method to rank the strategies based on their importance and order of implementation. The suggested methods and instruments for managing nervousness are then presented with a numerical case, along with the conclusions and recommendations.

The findings assist decision makers, practitioners, and managers in focusing on SC implementation, improvement, resilience, and sustainability. Future empirical investigations on SCN may use the framework as a valuable construct. This paper paved the way for more nervousness research in the SC arena. To reduce the impact of nervousness on SCs it is necessary to have a mitigation plan, a nervousness management strategy, and an in-depth view of the nervous system’s management mechanism.

The rest of this study is as follows. Section 2 offers insights into the literature on SC nervousness and MCDM techniques. Section 3 discusses the SCN strategies model in terms of the measurements and metrics, mitigation implementation stages, and management strategies. Section 4 utilizes the fuzzy PROMETHEE method to complete the solutions and prioritize the SCN strategies. Section 5 assesses the strategies using a case study with numerical analysis. Section 6 outlines the findings, conclusions, and recommendations for further investigation.

2. Literature Review

Nervousness decreases effectiveness and has a detrimental effect on SC performance as a whole. The stability and resilience of the supply chain are significantly impacted by nervousness, which increases prices and causes relationships with suppliers and consumers to fluctuate [2]. There were many previous studies that dealt with nervousness in the supply chain, but they were limited to certain aspects, either in terms of the area covered or because they focused on a specific part of the supply chain. The former studies investigated the supply chain nervousness (SCN) in the MENA region [2]; analyzed the present corpus of literature to examine the relationship between segmentation and planning nervousness [5]; created a new MRP nervousness indicator that took into consideration bullwhip difficulties and MRP trembling, which were previously classified as independent problems [6]; described a thorough evaluation of the literature and offered insights into the impact of capacity restrictions on the efficacy of regulations for reducing schedule nervousness [7]; addressed the issue of high-tech OEMs’ overall nervousness about planning [8]; examined the connections between the various schedule nervousness determinants like relationships with business partners, internal operations, and the effects of schedule nervousness on those who deal with supply chain scheduling issues among the high-tech manufacturing firms [9]; and examined planning procedures and accuracy throughout a demand–supply network in a setting with frequent product and market changes [10]. The rapid technology advancements raise risks and are causes of the supply chain nervousness. Planning nervousness is a sign of an ineffective approach to gaining control over internal response determinants. Nervousness management should include planning nervousness and how it affects responsiveness and flexibility [8]. Nervousness planning and management is as important to industry leaders as cost cutting. In comparison to the previous years, supply chain leaders now focus on investigating the nervous system of global supply chains and prioritize increasing resilience.

The appropriate nervousness mitigation measures are rarely examined in the literature. On the other hand, several mitigation strategies touched upon areas like risk, vulnerability, disruptions, and crises mitigations. Previous studies prioritized risk mitigation strategies for an environmentally friendly apparel supply chain using fuzzy TOPSIS [11]; identified and ranked the elements that contribute to supply chain vulnerability using the AHP technique [12]; examined the environmental impacts of food consumption and the corresponding mitigation measures, then developed a mitigation plan [13]; suggested disruption mitigation strategies to reduce loss due to supply disruption [14]; examined the COVID-19 impact manufacturing businesses’ supply chains and the appropriate mitigation measure using the grey-DEMATEL technique [15]; sought to mitigate supply chain disturbances resulting from the impact of natural disasters like the COVID-19 pandemic [16]; considered a company’s long-term strategic plan [17]; proposed a paradigm for controlling risk and uncertainty in the pharmaceutical supply chain [18]; examined how healthcare supply chain management has an effect on strategic goals and promotes excellent performance [19]; indicated that developing technologies are necessary for sustainable supply chain management to succeed in the future disruption [20]; and examined cover supply chain management gaps and how to close them during crisis situations [21].

Supply chain management is the proactive control of supply chain operations to increase customer value and secure a long-term competitive advantage. The supply chain companies go further and further to create and function in the most effective and efficient manner [22]. The design and choice of the performance measurements will determine the overall effectiveness and procedures in logistics. Performance will not exist if there are no metrics. Human nature also includes the tendency to assume that things will get better when they are measured. The measurement system for the logistics should improve the organization’s logistics system [23]. To cope with disruptions and uncertainties, researchers identify strategic strategies within the pandemic health outbreak issue to provide insights into supply chain operations [24]; to develop a supply chain strategy based on disruptive technologies with the goal of enhancing supply chain process performance and achieving greater resilience and responsiveness in the event that further disruption events take place [25]; to identify and categorize the impacts and potential management strategies needed to mitigate the negative effects of significant supply chain disruptions [26]; to address SC sustainability challenges, reduce waste, and contribute to environmental issues [27]; and to propose a model to assist the producers in selecting the best recovery approach [28]. The COVID-19 pandemic has highlighted the crucial strategic significance of managing the supply chain and its associated processes to support and prioritize delivery of the supplies [29].

A framework was developed to consider the uncertainty by introducing the LMAW and TOPSIS methods using the fuzzy ZE-numbers structure to determine the weight of the criteria and the ranking of the CSC suppliers, aiming for decisions with the highest reliability [30]. To assess circular supply chain management strategies in the beverage industry based on social responsibility, the base-criterion technique and multi-attribute border approximation area comparison (MABAC) under fuzzy Z-extended numbers are suggested [31]. Future research should integrate MCDM with other operational research methods, link risk with mitigation strategies, and integrate multiple MCDM methods for cleaner, more sustainable production [32]. A novel hybrid decision-making process is proposed, which combines the use of Z-numbers to evaluate solutions with the step-wise weight assessment ratio analysis (SWARA) and the combined compromise solution (CoCoSo), and a new application allowing the agricultural products SC to use blockchain is suggested [33]. In light of the COVID-19 pandemic, a fuzzy analytic hierarchy process (FAHP) analysis was performed to rank the primary supply chain capabilities [34]. The fuzzy ELECTRE approach was used to manage and prioritize the SCN factor [4].

The utilization of MCDM techniques provides a methodical approach to address decision problems that encompass multiple objectives, heterogeneous criteria, and fluctuating preferences. A considerable advancement in MCDM techniques has been seen in recent years. Conventional methods have been the basis for decision making, including TOPSIS, ELECTRE, and the AHP. Nevertheless, new approaches have surfaced, such as fuzzy-based approaches to deal with imprecision and uncertainty, data-driven models that use big data analytics and machine learning, hybrid approaches that combine several approaches, and multi-objective MCDM methods that take competing objectives into account [35]. J.P. Brans developed the PROMETHEE I and PROMETHEE II, which were first introduced in 1982 [36]. Several applications utilizing this methodology were developed in the healthcare industry that created PROMETHEE III and IV a few years later. The visual interactive module GAIA, which offers an excellent graphical representation to support the PROMETHEE methodology, was proposed by the same authors in 1988. J.P. Brans and B. Mareschal also proposed two excellent extensions, PROMETHEE V and PROMETHEE VI, in 1992 and 1994 [36]. The PROMETHEE methodology has handled a significant number of effective applications in a variety of fields, including banking, manufacturing locations, manpower development, water means, investments, medication, chemistry, healthcare, tourism, principles in OR, and vigorous management. The methodology’s unique usability and mathematical qualities are primarily responsible for its success [36].

SCM fuzziness presents the recently established fuzzy models and methods for supply chain management. These comprise fuzzy PROMETHEE, AHP, ANP, VIKOR, DEMATEL, clustering, linear programming, and inference systems [37]. Many researchers utilize multi-criteria decision-making (MCDM) techniques to provide an efficient methodology for supply chain decision makers. Previous studies applied fuzzy PROMETHEE to supplier selection in order to reduce the uncertainty caused by ambiguous situations [38]; to assess hazards and categorize them using the combined fuzzy AHP and PROMETHEE methods to account for the preferences of the decision makers [39]; to create a new hybrid approach that combines the F-AHP, F-PROMETHEE, and F-TOPSIS to assess and choose the partners in a sustainable supply chain network [40]; to propose a fuzzy PROMETHEE technique to address the ambiguity and uncertainty in subjective group decision making in supply chain quality performance evaluation [41]; to use the fuzzy PROMETHEE approach to help decision makers choose the best reverse supply chain management [42]; to utilize the fuzzy PROMETHEE technique to rank the suppliers [43]; to use the fuzzy PROMETHEE approach to resolve the supplier selection issue using group decision making [44]; and to use the fuzzy rough PROMETHEE approach to choose the best suppliers, to reduce the reliance on the opinions of experts, and to minimize losses and optimize the production process [43,45]. The fuzzy PROMETHEE approach, which is one of the decision-making techniques, was used in several studies to compare vendors. Due to the simplicity they provide, fuzzy sets were utilized to eliminate uncertainty in the evaluation process and to obtain the decision makers’ spoken evaluations [46].

Nervousness is one of the important issues in the current and future SCs. With the current rapid changes in SCs, it is essential to plan nervousness management, implementation, and measures such as quantity, and time, and cost. For example, orders are frequently rescheduled in terms of timing and amount; this is considered to be a sign of SC nervousness. Organizations that take action to reduce risks are better equipped to handle disruptions in the future [47]. SCs should always use technology to stay competitive and to weather crises because it makes decision making more effective and enables decision makers to choose solutions based on their significance and commercial impact [1].

Table 1. Summary of relevant topics and studies.

Reference #	Theme of the Study
[2]	Explores supply chain nervousness (SCN) in MENA region.
[3]	Examines the components of global SC nervousness (GSCN) and uses the DELPHI-FAHP to analyze the findings and identify the variables that have the major effects on supply chain nervousness mitigation.
[4]	Suggests a framework for the factors of supply chain nervousness management that incorporates the fuzzy ELECTRE methodology to rank these factors according to their relative importance.
[5]	Analyzes the existing literature to identify any overlaps between planning nervousness and segmentation.
[6]	Examines the MRP nervousness problem in a two-echelon supply chain under first-order auto-regressive demand from a dynamic, stochastic, and economic perspective.
[7]	Explores the impact of capacity constraints on the effectiveness of policies aimed at reducing schedule nervousness.
[8]	Creates a supply chain planning and control framework to enhance responsiveness and stability in a make-to-forecast environment.
[9]	Explores the correlations between schedule nervousness determinants and their effects on individuals in high-tech manufacturing firms dealing with supply chain scheduling issues.
[10]	Investigates nervousness related to planning in a demand–supply network that is typified by the bullwhip effect, quick changes in products, and rapid market variations.
[11]	Prioritizes risk mitigation techniques for the supply chain of ecologically friendly apparel, taking into account their efficiency in lowering various risks.
[12]	Examines and ranks the variables that contribute to supply chain vulnerability.
[14]	Examines SC system’s disruption recovery strategy from the product change standpoint.
[15]	Studies mitigation techniques and useful insights gained in SCM during and after the pandemic.
[16]	Creates a mathematical model to lessen supply chain network disruptions caused by natural disasters like the COVID-19 pandemic.
[18]	Provides a framework for controlling risk and uncertainty in the pharmaceutical SC.
[20]	Reviews the literature on sustainable supply chain management trends that are moving toward ambidexterity and disruption. It does this by combining fuzzy Delphi, entropy weight, and fuzzy decision-making trials with a hybrid data-driven analysis approach based on content and bibliometric analyses.
[26]	Determines the detrimental effects of the COVID-19 pandemic on supply chains and suggests strategies to mitigate these effects within the apparel industry supply chain.
[28]	Explains a recovery strategy for supply chain disruptions that aims to change the original product types in order to deal with them. It does this by using a mixed-integer linear programming technique.
[29]	Uses supply chain integration to improve preparedness for and responsiveness to SC disruptions.
[34]	Makes sure the supply chain is resilient in the face of the COVID-19 pandemic by using the fuzzy analytic hierarchy process (FAHP) analysis to rank the critical supply chain functions.
Current study	Explores supply chain nervousness strategies, the internal and external factors affecting it, mitigation methods, treatment methods, and recent trends. Analyzes management strategies and proposes a model for supply chain nervousness (SCN) strategies, prioritizing them based on performance measures using the fuzzy PROMETHEE method.

summarizes the relevant topics and studies, and its last row represents the gaps that the research will cover.

Although the expressions nervousness and planning nervousness have been used in the last decades by researchers, they are mainly still used from the perspective of MRP and inventory management. Nervousness should be considered in other SC planning settings. There is no clear nervousness planning used by the organizations today, but they focus on subjective intuition. Also, there is no instantaneous quantifiable correlation to measure the nervous SC. Companies are likely to treat symptoms rather than the underlying root causes of nervousness. This is because the entirety of SC nervousness was formerly underemphasized in the research. Therefore, further studies should focus on quantifying, measuring, and mitigating SC nervousness. Furthermore, additional studies should focus on the connections between nervousness and the SC performance.

3. SCN Strategies Model

In this section, the proposed model of nervousness strategies in the supply chain is discussed in terms of three main aspects: nervousness measurements and indicators, nervousness mitigation implementation stages, and nervousness management strategies. As shown in Figure 2, there are many factors that lead to the realization and handling of these strategies. The three frames surrounding the model in Figure 2 outline the evaluation criteria, the strategies to manage supply chain nervousness, and the execution stages of SCN mitigation. The proposed model also shows the complementarity, integration, and interrelationships between the different parts. Below is an explanation of each of the three parts of the SCN strategies model.

3.1. SCN Measurements

SCN measurements can be classified as quantitative and qualitative, by the level of GSCN to be measured, as financial and non-financial, and as internal and external. In addition to productivity, employee morale, customer-related measures, supply chain responsiveness, and value-added employee productivity are also considered. Comparison against best practice to attain greater performance, the best practice, and altering actual knowledge are also used.

The nervousness costs are measured in terms of the total supply chain costs resulting from the interference between decisions. They are calculated as the difference between the total supply chain costs resulting from the decisions taken at a specific time (current decisions) and the total supply chain costs of previous decisions. The total supply chain costs may include the cost of production, transportation, warehousing, internal material handling, inventory, order management and processing costs, packaging costs, and stockout costs.

The interference between decisions depends on the time between the decisions or the number of decisions in a specific period of time or the decisions that affect other decisions in a short period of time. The key questions relate to how frequently the SC will review the decisions. The shorter the time frame, the more nervous it will become. Every company, regardless of the type of industry, has internal processes and external events that require new decisions or re-decisions. Measurements of the timeline to complete any decision from start to finish are critical for creating reliable baseline data that can be utilized to make decisions about SC performance. Once the baseline data are collected, the company can set goals to enhance SC efficiency in the future.

Nervousness has an impact on the quality and efficiency of the global supply chain. Decision quality in the context of GSCN can affect the partners negatively or positively. Some partners may benefit in terms of reducing costs, increasing customer satisfaction, or quickly reacting to changes in the supply chain. For others, nervousness can have negative effects on it, and this depends on the nature of the decisions taken and the criteria used to make the decision, i.e., whether the new decision depends on changes in demand, changes in supply, the total cost of the supply chain, and so on.

SC managers should understand and have a good sense of the recurring errors in their SC processes and operations. Measuring the frequency of errors can be a simple decision-making metric; however, in order to collect the data effectively, the organization must have a culture that fosters the identification of critical nervousness and risk indicators and then arranges and rank-orders them so that they can be controlled immediately based on their priorities.

Soft matters, such as employee morale, the degree of integration, decision completeness, customer satisfaction, SC visibility, leadership alignment, etc., represent the critical factors of the SC that are difficult to measure using specific formulas and calculations. Contrary to what many managers believe, the soft values and soft qualities can be measured and assessed. In most cases, decision-making metrics for these soft values necessitate the use of a proxy measurement, like expertise opinions, questionnaires, or survey scores.

While GSCN metrics might be difficult to create—especially for administrative and managerial activities—they are essential for evaluating and enhancing an organization’s procedures and operations. Therefore, the present study used measurement metrics that represent the impact of nervousness on the supply chain. These indicators include the impact of nervousness on the total SC costs, the time interference between decisions, the effect on the SC quality, the errors resulting from the SCN, and the soft values caused by the SCN.

3.2. SCN Management Strategies

There are different strategies to deal with nervousness. First, there is the reactive strategy, whereby organizations in the supply chain buffer with increased inventory levels, warehouses and stores capacity, safety lead time to restrict the costs of lower responsiveness, and the use of safety stock to reduce the impact of nervousness. Second, there is the proactive strategy, which identifies the causes of nervousness, such as increasing parts commonality and the reduction in product varieties, and leads to increased coordination, information sharing, transparency, marketers and salespersons overestimating, and internal and external integration. Procuring from suppliers abroad usually takes a relatively long time. Coordination and synchronization between functions are necessary to improve the internal process and enhance the cross-functional team. The second SCN strategy keeps a high SL and is cost-effective, while the first strategy focuses more on safety stock. Internal integration is necessary to buffer against nervousness and external integration; considering the whole SC system is also necessary to buffer against SCN events. Developing a dynamic SC, supported by proportionate and directed due diligence and monitoring, provides the organization with an edge on interruption and the competition. As shown in Figure 2, the following is a brief analysis of the five main strategies to deal with supply chain nervousness.

(I)

Plan continuity

Organizations should plan, sustain, and maintain their operations and strengthen resilience within their supply chains. It is important to fully understand potential vulnerabilities and identify business opportunities by monitoring a wide range of risks. Decision makers must create a consistent view of nervousness and centrally manage data sources rather than relying on a large number of different and disconnected datasets. Using a consistent data source will allow companies to take advantage of a common framework where everything is measured in the same way. This makes complex company-wide problems easy to understand at the highest level. Centralization will save time, resources, and confusion. Whether or not a company’s SC system has the ability to respond to epidemics, unstable policy development, government instability, or increased security risks, risk analysis can help the company to respond flexibly. By regularly monitoring these issues, organizations will understand which suppliers are most at risk and can adjust their strategy accordingly.

Once you have identified the nervousness in your supply chain, you must be wise and cutting-edge when developing mitigation strategies. Organizations should implement strategies that address specific nervousness in the supply chain. It should be a suitable and cost-effective hammer solution. Organizations must constantly innovate internally and in conjunction with their suppliers. Properly and positively communicating the steps you are taking to address nervousness may help create opportunities for revenue expansion. Analysis is a perfect tool to illustrate performance improvement.

The key steps to strengthening resilience within the supply chain include complete understanding of the supply chain, assessments of whether the vendor is critical to short-term goals or long-term competitive advantage, the supplier replacement practice, the support resilience of the suppliers, and leverage relationships.

Continuity is strongly connected to many factors like due diligence, transportation routes, low costs, logistics capacity, and economic solutions. Organizations should focus on a stable network of vendors and suppliers to assure the continuous supply. Supplier selection and partnerships are two of the essential factors for supply chain stability.

(II)

Utilize technology

It is the right time for SC technology providers and vendors to initiate an innovative business process transformation. SCM should be prepared for supply change, demand fluctuations, economic instability, and challenging situations. SCM could make a robust agile production model to face current and future challenges. Decision makers should discuss the improvement of operations, products, and services along with the redesign potential of the SC to stay competitive on an international scale.

SCM has to adapt to the current crisis by improving the SCs for more transparency; it must have the flexibility to allow the adjustments based on real-time disruptions to be adapted, speed up the technology transformation to increase visibility, improve IT capabilities and the associated online activities, such as further remote work, more digitization flows, and usage of emerging technology, and increase investments in customers’ requirements. In general, companies need to invest in assets, technology, employees, acquisitions, and others to face SC disruptions and SCN.

Autonomous systems are expected to reduce the effects of change and variations, instability, and disruptions in the SC by reducing the errors and risks of dangerous work and by improving productivity, efficiency, safety, security, customer satisfaction and responsiveness, and employee value by providing more important jobs instead of routine jobs, minimizing hazards, and gathering and analyzing data and information. The construction of a smart supply chain is closer than ever. SC visibility, nanotechnology, contribution to competitive advantage, effectiveness, goods safety, compliance, eco-friendliness, and competitiveness are important. The positioning of the companies or company locations can achieve a competitive advantage when facing the global changes and challenges. Blockchain technology provides the ability to secure data, and it is expected that in the future it will provide solutions to many cybersecurity risks. Blockchain approach is a way to introduce new values and standards to SC data transfer.

To override the current problems and improve SC performance there is a need to use smart seamless systems, ICT tools, and information solutions, including smart cargo, intelligent connected goods, exchange of data in real time, data collection, processing, and decision making, and continuous conditions monitoring; effective allocations of products, recycling, refurbishments, remanufacturing, reuse, and the environmental effect also need to be considered. Smart links and closed SCs reduce SCN and thus enhance SC sustainability.

(III)

Improve SC cyber system

SC cyber security is not an IT issue only; SC risks include sourcing, vendors, SC continuity, SC quality, transportation, and the other SC functions that require coordination between partners. The main SC cyber security principals include building a defense based on threat priorities and considering the security problem, but also the technology, people, process, and knowledge problem; physical gaps lead to cyber security problems or risks. The cyber security sources of risks include logistics and service providers, weak information security at low SC levels, hacked software and hardware when purchased from vendors, software vulnerabilities, counterfeit hardware, and 3PL data storage. Cyber security measures to minimize nervousness include buying from trusted vendors, having separate critical devices and machines from outside the network, and training users on how to protect SC information from different threats.

Information (cyber security), transportation/in transit/shipments/cargo (hijacking), and warehouses (theft) should be considered in any SC planning. Security is changing and evolving in its nature. It is important to know the fact that you cannot secure all SC part/activities everywhere all the time. The SC exposure risks depend on the company, type of goods, its location, etc. It is important to understand the risk and threat and their criticality in SC operations and activities. A WDS system is one of the main techniques used to generate awareness about transportation activities and shipping [48].

Security managers and planners should assess, prioritize, and manage the risks and use multiple layers of protection to deal with the changing security. There are many players in the SC, including vendors, suppliers, logistic providers, service providers, manufacturers, importers, exporters, brokers, consolidators, carriers, seaports, airports, hubs, and freight terminals, to name a few. Currently, organizations can use GPS and tracking and tracing technologies to improve the security of the SC. Video surveillance and license plate recognition solutions can authenticate trucks, open gates, and even generate email alerts. There is geofencing based on sensors, cameras, and customized software. It can generate an alert for the driver if there is a suspicious act in the truck while he is outside. Hotspots regarding cargo theft based on historical data help with route/reroute planning.

To secure the SC, companies may follow the following steps: review internal and external security procedures, create written security rules and controls, and provide security best practices training/sharing for staff and vendors. Organizations should close every gap in every link of the SC network and not allow the security issues to limit their business.

(IV)

Model nervousness

The process of detecting, evaluating, and managing supply chain nervousness for an organization could be known as supply chain nervousness management (SCNM). An organization can function more effectively, cut expenses, and improve customer service by putting global supply chain nervousness management techniques into place. The modeling and management of supply chain nervousness involves evaluating nervousness sources, analyzing likelihood, and presenting a strategy to avoid, mitigate, or limit the impact of nervousness sources. Supply chain nervousness is a probabilistic and undesired scenario.

The practice of identifying internal and external hazards to your supply chain is known as nervousness modeling. Eliminating circumstances that place your firm at unwarranted, heightened risk is a requirement for effective nervousness management. More than just a useful addition to your operations, supply chain nervousness modeling is crucial to your performance in situations like natural catastrophes, unpaid invoices, transportation disruptions, etc. The goal of nervousness modeling is to foresee problems and offer loss reduction if they do arise.

To stay competitive and reach a leading position in the current marketplace, organizations should be flexible, intelligent, safe, and adaptable with partners. SC is sensitive to threats as they look for competitive advantages of SCs, technology changes, and globalization. Thus, they should guarantee the continuity of the production process, product counterfeits, and tracing and tracking of the products. To increase safety and security, SC decision makers should coordinate the response, support training, assure joint inspection, and improve the information system required to secure SC operations. One of the main SC priorities is the safety and security of the SC. Security assurance is a necessity to guarantee the shipping and handling of the goods. Security increases with the increase in globalization, digitalization, and cloud computing. Radical threat is one of the major risks for the global SCs. SC security is widespread and complex and has many varied issues. SC security influences include weather conditions and unexpected events and should consider all SC players.

The majority of the threats to the business operations can be divided into four major groups: economic, environmental, political, and ethical. The management modeling of supply chain nervousness involves evaluating risk sources, analyzing likelihood, and presenting a strategy to avoid, mitigate, or limit the impact of nervousness sources.

(V)

Manage suppliers’ relationships

Communication improves as the supplier relationship grows. Suppliers are better able to meet the needs of the companies they supply because they have a deeper understanding of those companies. Effective and ongoing communication is the cornerstone of any successful relationship. Invest in regular communication with your suppliers to lay the foundation for long-lasting relationships. Establish routes for communication and take part in dialogue. Contact the provider frequently. The SC is the backbone of any firm for successful business. All partners in the network get together in a cohesive relationship manner to increase the value chain and customer satisfaction and loyalty. It is important to integrate internal and external SCs at all levels. Effective integration requires information and data integration, coordinating resource distribution and sharing, collaborative risk mitigation plan, and partner relationship linkages. To guarantee effective integration, organizations should utilize IT and information sharing, improve trust, demand change distortion, increase systems compatibility and knowledge, and reduce the costs of integration. The integration requires the alignment and commitment of all partners, to unify the SC metric, to be communicative, and to have the willingness for joint structure and integration.

Select suppliers, vendors, the SC system, and partners with a proven record of cyber security protections. Use encryption, international and national standards, and strict access to systems, maintain strong physical security, use a skillful IT team, apply frequent penetration checks, and assess your SC systems and your partners’ safety and security issues more often.

Assess suppliers to make sure you obtain the best and most competitive prices and explore the suppliers with carriers that serve your area and other carriers that can be used as alternative shippers; shippers should have freight insurance; consider a safe and secure network and plan to deal with late delivery or delays. When selecting their suppliers and service providers, organizations have to consider risk and mitigation strategies that include the potential nervousness that effects the GSC, nervousness assessments, their effect, and solution or mitigation plans.

3.3. SCN Mitigation Implementation Strategies

The third part aims to explain the main stages necessary to reduce the impact of SCN. This part analyzed the steps that help to achieve the goal. The five stages are considered as the main factors toward SC sustainability and resilience. The tools required by an organization include a GSCN strategy and the utilization of SC experts, to monitor and adjust, communicate in a timely manner, and control the nervousness. The main threats facing SCs are liquidity, GSC disruptions, increased trade barriers, and shifting consumer attitudes. On the other hand, there are several chances to benefit from the nervousness and improve their SC operations, like increased employment of digital technologies, more resilient SCs, improved user experience, and intelligent tools to increase the business outcome.

Organizations should close every gap in every link of the SC network and not allow the nervousness issues to limit their business. In order to be able to reduce the impact of (GSCN) nervousness, there are a number of steps and procedures that can help in implementing a solid plan for organizations. These procedures are summarized in five main stages, as follows:

Stage 1: Conduct SCN analysis

Identify nervousness areas and assess the likely impact should they be affected. Adopting a nervousness-based approach means a priority action plan can be defined. Review the SCN causes, sources, and effects. Assess internal and external procedures, create written rules and controls, share best practices, and determine the objectives.

Contact the key partners to be sure of their plans when facing a crisis and their responses to the disruptions. Determine alternative suppliers in case the main suppliers do not have response plans, or these plans are ineffective. Review the contractual liabilities in the case of delays, order cancellations, and low-quality goods.

Perform comprehensive assessment and planning, including a response plan, rethinking SC strategies, and product design consideration. Conduct nervousness analysis, review the estimated nervousness impact and the SC nervousness avoidance, strengthen collaboration relationships, communicate in a timely manner, and control the nervousness. Examine efficiency within your operation, the transportation and logistics, quality improvement, visibility in terms of GSC status, safety and security of goods, organizational responsiveness, network resiliency, and financial success. In the face of increasing pressure in vital industries, supply chain leaders must seek any advantage they can to improve their operations’ efficiency.

Stage 2: Assess decision making

Assess decision-making systems and establish a positive decision-making environment. Establish a team made up of representatives from each of the business main sections and allocate appropriate resources based on your initial nervousness assessment. Assign a member of the executive team as a coordinator to be the head of the decision and action plans. Determination of the GSC objectives should be encouraged, enabling the resumption of normal business functions and allowing employees to perform their tasks; how IT will support the transitions should be determined. Decision-making assessment is a key factor in mitigating the SCN and should cover three major areas:

• Response—increase inventory levels where possible for the critical goods and continue the communication with the current suppliers and try to know their magnitude of disruptions. Modify orders and delivery-based variations in demand.
• Recovery—search for new sources to increase SC resilience. Study lessons learned and cumulative experience with the key suppliers and the possible improvements. Explore methods to limit costs and speed up the recovery.
• Support systems—decision making requires the reconsideration of the GSC strategy, the demand impact specific to businesses, and the utilization of SC experts.

Stage 3: Invest in technology

New technologies help the established tools and play a crucial role in helping SC decision makers have an ongoing view of potential nervousness and can provide a framework to help them take corrective actions to better serve their customers.

AI and machine learning can better contextualize what is going on in the world. There has never been a greater need for real-time insight, particularly from the customer’s perspective. Technology like digitization makes supply chain visibility possible—removing bottlenecks and data silos and adding AI-enabled insights into supply chains. It is by obtaining total visibility of every supply chain step that organizations are able to prepare for and survive a future crisis. Visibility is not enough; knowing how to use visibility is necessary. Organizations can obtain better competitiveness and customer satisfaction if they can proactively identify issues before they become credible, rapidly switch suppliers to avoid disruption, and know of industry changes before they become mainstream. Visibility allows resilience. And if the COVID-19 pandemic has taught us anything, it is that resilience is the key to survival. There are lots of emerging technologies in the market that help companies monitor their supply chain for different types of SCN.

The internet, cloud technology, data networks, and webpages line up with IT systems and support the improvement of the work requirement. In the arrangements for distance work plans, policies, and strategies, IT systems back flexible employee schedules and activities. The increase in the remote work, online activities, and number of users will increase network instability, data risks, security issues, and network weaknesses. Organizations should invest in an IT system infrastructure, staff, and networks to assure the seamless flow of processes.

Stage 4: Develop nervousness contingency plan

A developed contingency plan can control nervousness sources, reduce supply shock, arrange for demand volatility, and ensure a safe work environment. To decrease delays and enable a timely supply, organizations need to continuously coordinate with suppliers and determine the best sourcing options in cooperation with partners. The flowing steps are used to assess and address related SC nervousness: conduct nervousness analysis, estimate nervousness impact, avoid SC nervousness, strengthen collaboration relationships, communicate in a timely manner, and control the nervousness. Nervousness management should be also an integral part of the design.

Firms should start planning strategies based on different demand scenarios and situations across end-to-end SCs. Different firms are exposed to inimitable risks, and firms need to develop plans for the best-case and worst-case scenarios. The best-case scenario is making sure everything returns back to normal with normal global and SC operations. The worst-case scenario is where nervousness remains with a continuous effect for a longer period of time.

Organizations need to work with the suppliers to assure the business continuity. In the short term, organizations should work with the current suppliers to create a business continuity plan. At the same time organizations should search and identify suppliers from different regions and different countries to diversify the SC specifically for goods with longer supply and production cycles and to protect against shortage. One of the main symptoms is the long time period between the order and delivery.

With the current COVID-19 situation, it is the best time to prepare SCs for crisis. During pandemic events, the clear understanding of the risk helps organizations create the right solutions to face the nervousness and future shocks and disruptions. Solutions can be implemented with a sequence of steps: develop contingency plans with scenarios for different demand surroundings and changes; reduce supply shock by working closely with the current suppliers and diversifying the sources; arrange for demand volatility by organizing panic buying and supporting the retailers part; ensure a safe work environment; and invest to protect SC employees and make critical items available. Solutions require communication to manage time, availability, and safety.

There are many short- and medium-term actions that a company could take to react to business and SC disruptions and challenges from the impact of SCN and then anticipate the future long-term solution. Decision makers and management are striving to know how the firms can respond to the short–medium change. The nervousness effect is spreading all over the GSCs; there are many measures that organizations can take to protect their SC operations.

Stage 5: Assure continuity

Organizations should ensure business continuity and should document the reactions to nervousness and the lessons learned. They should ensure that the information flow is maintained, that their contingencies are in place, that production and fulfillment continue to run smoothly, and that contingencies exist so that work can continue as normal.

Fast, synchronized decisions by key partners are essential to secure the best availability. The organization of SC continuity will vary depending on the SC risk management; nervousness mitigation plans; business strategies; geographic area (near suppliers, factories, and industries in the region); reliance on many suppliers; multiple sources; stock quantity to buffer against SC instability; event management and response; robust relations with key partners, especially the suppliers; visibility across the entire supply network; understanding of the risk drivers, priorities, and solutions; flexibility of the supply and distribution networks; responsiveness to SC changes; the investment in SC development; and event prediction and mitigation systems. The organizations who suffer include the ones that rely on a single supplier or regional sources only, have limited visibility of the supply network, and do not realize the stockout and inventory status, optimization system, and agile supply network to guarantee the smooth flow of products to the customers. Continuity requires enhanced forecast estimates, the establishment of tools to expect disruptions, the introduction of automation, etc.

Supply chain stability is one way to help manage nervousness and enhance productivity. Understand all tiers of the supply chain, as well as the nature and relevance of each tier, to ensure that suppliers are stable and capable of meeting needs. Audits are an essential component of the monitoring process. Companies are subject to stricter rules on matters like environmental and human rights concerns as markets become more global. Consider advancements in areas like automation and transportation when choosing a provider. The Internet of Things is an important player in the future supply chain. By integrating smart sensors into the supply chain process, tracking (and utilizing) data on order fulfillment and truck delivery will greatly improve the accuracy of forecast estimates. Be sure to determine technology, personnel, transportation, and storage capacity. Make sure to develop a plan to deal with market and supply fluctuations. Work directly with your logistics partners to understand and assess its ability to withstand challenges in the supply chain. Ask them to break down their capabilities and add action plans to foreseeable scenarios to come up with a consistent solution. Keep communication channels open and request supply reports or real-time information about supply, logistics, and inventory levels to stay up to date before problems arise. Open communication will help create better understanding between relevant parties, resolve issues faster, and help avoid costly delays due to silos.

4. Fuzzy PROMETHEE Method

The Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) is a widely used method for ranking alternatives when using several (MCDM) criteria. In 1985, Brans and Vincke [49] introduced a method that used a preference function to compare pairs of choices pairwise and to score them in a [0, 1] range. Le Téno and Mareschal (1998) initially proposed merging fuzzy set theory and the PROMETHEE approach [50]. Due to its robustness in comparing alternative performances and taking them into account in the composite rating, the PROMETHEE technique is chosen for ranking and selecting alternatives. The PROMETHEE approach has a fuzzy extension when dealing with ambiguous and subjective data, just like other MCDM techniques. Fuzzy PROMETHEE has been used in a variety of fields, including supplier selection, customer evaluations, logistics, healthcare management, and mapping landslide susceptibility, among others. There are several defuzzification techniques in the literature. This study employs the methodology and defuzzification technique developed by Chen et al. (1997) and Adalı et al. (2016) [51,52]. Quadruplets (a₁, a₂, a₃, and a₄) are the units of measurement for fuzzy trapezoidal numbers, and their membership function is specified as (Figure 3):

$${\mu }_{\tilde{A}}(x)=\left\{\begin{array}{c}0 x<a_1,\\ \left(x-a_1)/(a_2-a_1\right) a_1\le x\le a_2,\\ 1 a_2<x<a_3, \\ \left(x-a_4)/(a_4-a_3\right) a_3\le x\le a_4,\\ 0 x>a_4\end{array}\right.$$

(1)

Given any two trapezoidal fuzzy numbers, $\tilde{A}=\left(a_1, a_2, a_3, a_4\right)$ and $\tilde{B}=\left(b_1, b_2, b_3, b_4\right)$, then the Hamming distance is defined as

$$d_H(A, B)={\sum }_{j=1}^n\left|a_j-b_j\right|$$

(2)

Assume $\tilde{A}$ is the trapezoidal fuzzy number, then the defuzzified value $x_{\tilde{A}}$ of the fuzzy number A is calculated as follows: $x_{\tilde{A}}=\left(a_1+a_2+a_3,+a_4 \right)/4$ [51]. The main operations of trapezoidal fuzzy numbers used in this paper are as described by Kaufmann and Gupta (1988) [53]. The following steps summarize the fuzzy PROMETHEE method [52] and mainly include the calculations of the fuzzy criterion weights, fuzzy performance ratings, fuzzy distances (D), fuzzy preferences, and fuzzy flows.

Step 1: A group of decision makers forms a committee. Each decision maker’s fuzzy rating can be represented as a trapezoidal fuzzy number ${\tilde{R}}_k$ with membership function ${\mu }_{\widetilde{{\stackrel{~}{R}}_k}}(x)$ in a decision committee with k experts E_k, (k = 1, 2, … k).

Step 2: Next, evaluation criteria are established, and workable options are developed. It is intended that there are m alternatives ($A_m$) and n criteria ($C_n$).

Step 3: Suitable linguistic variables and their equivalent trapezoidal fuzzy numbers are selected. They are employed to assess the relative weighted importance of the various criteria and to rank alternatives according to numerous criteria.

Step 4: Combining fuzzy ratings and criterion weight, the fuzzy ratings (${\tilde{x}}_{ij}$) of the alternatives are combined as follows:

$${\tilde{x}}_{ij}=(a_{ij}, b_{ij}, c_{ij}, d_{ij})$$

(3)

$$a_{ij}=\underset{k}{\mathrm{m}\mathrm{i}\mathrm{n}}\left\{a_{ijk}\right\}, b_{ij}=\frac{1}{K}{\sum }_{k=1}^K{b}_{ijk}, c_{ij}=\frac{1}{K}{\sum }_{k=1}^K{c}_{ijk}, d_{ij}=\underset{k}{\max }\left\{d_{ijk}\right\}$$

(4)

Then, the fuzzy weights (${\tilde{w}}_{ij}$) of each criterion are aggregated as:

$${\tilde{w}}_j=(w_j^l, w_j^p, w_j^q, w_j^u)$$

(5)

$$w_j^l=\underset{k}{\min }\left\{w_{jk}^l\right\}, w_j^p=\frac{1}{K}{\sum }_{k=1}^K{w}_{jk}^p, w_j^q=\frac{1}{K}{\sum }_{k=1}^K{w}_{jk}^q, w_j^u=\underset{k}{\max }\left\{w_{jk}^u\right\}$$

(6)

Step 5: The fuzzy decision matrix is also made as:

$$\tilde{D}=\left[\begin{array}{c}{\tilde{x}}_{11}\\ {\tilde{x}}_{21}\\ ⋮\\ {\tilde{x}}_{m1}\end{array}\begin{array}{c}{\tilde{x}}_{12}\\ {\tilde{x}}_{22}\\ ⋮\\ {\tilde{x}}_{m2}\end{array}\begin{array}{c}\cdots \\ \cdots \\ \dots \\ \dots \end{array}\begin{array}{c}{\tilde{x}}_{1n}\\ {\tilde{x}}_{2n}\\ ⋮\\ {\tilde{x}}_{mn}\end{array}\right]$$

(7)

$$\tilde{W}=\left[{\tilde{w}}_1,{\tilde{w}}_2, \dots ,{\tilde{w}}_n \right]$$

(8)

where ${\tilde{x}}_{ij}=(a_{ij},b_{ij},c_{ij},d_{ij})$ and ${\tilde{w}}_j=\left(w_j^l,w_j^p,w_j^q,w_j^u\right)$; $i=\mathrm{1,2},\dots ,m;j=\mathrm{1,2},\dots ,n$ can be approached by a positive trapezoidal fuzzy number.

Step 6: The following formula for linear normalization is used to normalize the fuzzy decision matrix. Where $\tilde{R}$ represent the normalized fuzzy decision matrix and ${\Omega }_B \mathrm{a}\mathrm{n}\mathrm{d} {\Omega }_C$ represent the benefit and cost criteria index sets, respectively.

$$\tilde{R}={\left[{\tilde{r}}_{ij}\right]}_{m\times n} i=\mathrm{1,2},\dots ,m; j=\mathrm{1,2},\dots ,n$$

(9)

$${\tilde{r}}_{ij}=\left[\frac{a_{ij}}{d_j^{*}}, \frac{b_{ij}}{d_j^{*}}, \frac{c_{ij}}{d_j^{*}}, \frac{d_{ij}}{d_j^{*}}\right] d_j^{*}=\underset{i}{\max }{d}_{ij}; j\in {\Omega }_B$$

(10)

$${\tilde{r}}_{ij}=\left[\frac{a_j^{-}}{d_{ij}}, \frac{a_j^{-}}{c_{ij}}, \frac{a_j^{-}}{b_{ij}}, \frac{a_j^{-}}{a_{ij}}\right] a_j^{-}=\underset{i}{\min }{a}_{ij}; j\in {\Omega }_C$$

(11)

Step 7: The values in the normalized fuzzy decision matrix are multiplied by the importance weights of the assessment criterion to create the normalized decision matrix. The definition of the weighted normalized fuzzy decision matrix $\tilde{V}$ is:

$$\tilde{V}=\left[\begin{array}{c}{\tilde{V}}_{11}\\ {\tilde{V}}_{21}\\ ⋮\\ {\tilde{V}}_{m1}\end{array}\begin{array}{c}{\tilde{V}}_{12}\\ {\tilde{V}}_{22}\\ ⋮\\ {\tilde{V}}_{m2}\end{array}\begin{array}{c}\cdots \\ \cdots \\ \dots \\ \dots \end{array}\begin{array}{c}{\tilde{V}}_{1n}\\ {\tilde{V}}_{2n}\\ ⋮\\ {\tilde{V}}_{mn}\end{array}\right]$$

(12)

where ${\tilde{V}}_{ij}={\tilde{r}}_{ij}$(.)${\tilde{w}}_{j}i=\mathrm{1,2},\dots ,m;j=\mathrm{1,2},\dots ,n;$ where ${\tilde{w}}_j$ denotes the important weight of criterion j.

Step 8: The two alternatives g and f are compared using the Hamming distance approach for each criterion. The maximum between two fuzzies is initially calculated. Therefore, to obtain ${\mathrm{m}\mathrm{a}\mathrm{x}(\tilde{V}}_{gj}, {\tilde{V}}_{fj})$, their least upper bound is calculated. Next, the Hamming distances ${d(\mathrm{m}\mathrm{a}\mathrm{x}(\tilde{V}}_{gj}, {\tilde{V}}_{fj}),{\tilde{V}}_{gj})$ and ${d(\mathrm{m}\mathrm{a}\mathrm{x}(\tilde{V}}_{gj}, {\tilde{V}}_{fj}),{\tilde{V}}_{fj})$ are calculated. ${\tilde{V}}_{gj}\ge {\tilde{V}}_{fj}$ in the case of ${d(\mathrm{m}\mathrm{a}\mathrm{x}(\tilde{V}}_{gj}, {\tilde{V}}_{fj}),{\tilde{V}}_{fj})\ge {d(\mathrm{m}\mathrm{a}\mathrm{x}(\tilde{V}}_{gj}, {\tilde{V}}_{fj}),{\tilde{V}}_{gj})$. Otherwise, ${\tilde{V}}_{gj}<{\tilde{V}}_{fj}$ in the case of ${d(\mathrm{m}\mathrm{a}\mathrm{x}(\tilde{V}}_{gj}, {\tilde{V}}_{fj}),{\tilde{V}}_{fj})<{d(\mathrm{m}\mathrm{a}\mathrm{x}(\tilde{V}}_{gj}, {\tilde{V}}_{fj}),{\tilde{V}}_{gj})$. Afterwards, the preference function can be built as [54]:

$$P_j\left(g,f\right)=\left\{\begin{array}{c}{d(\mathrm{m}\mathrm{a}\mathrm{x}(\tilde{V}}_{gj}, {\tilde{V}}_{fj}),{\tilde{V}}_{gj}) {\tilde{V}}_{gj}<{\tilde{V}}_{fj}\\ {d(\mathrm{m}\mathrm{a}\mathrm{x}(\tilde{V}}_{gj}, {\tilde{V}}_{fj}),{\tilde{V}}_{fj}) {\tilde{V}}_{gj}\ge {\tilde{V}}_{fj}\end{array}\right.$$

(13)

If g is better than f then Pj(g, f) > 0; otherwise, Pj(g, f) = 0.

Step 9: Calculating the fuzzy preference index yields the outranking relation’s value $j=\mathrm{1,2},\dots ,n$.

$$\tilde{\pi }(g,f)=\frac{{\sum }_{j=1}^n{\left[{\tilde{w}}_j{P}_j\left(g,f\right)\right]}_j}{{\sum }_{j=1}^n{\tilde{w}}_j}$$

(14)

Step 10: For the purpose of rating the alternatives, the leaving and entering flows are computed as follows:

$${\widetilde{ɸ}}^{+}={\sum }_{\begin{array}{c}f\ne g\\ f=1\end{array}}^m\tilde{\pi }(g,f); {\widetilde{ɸ}}^{-}={\sum }_{\begin{array}{c}f\ne g\\ f=1\end{array}}^m\tilde{\pi }(f,g)$$

(15)

Step 11: Net flow is computed using the formula ${NF (ɸ)=\widetilde{ɸ}}^{+}-{\widetilde{ɸ}}^{-}$.

Step 12: Finally, each alternative’s preference rank is assessed.

5. Numerical Analysis

In this research, the appropriate management strategies are identified for the nervousness in the supply chain. To this end, and to apply the proposed method using fuzzy PROMETHEE, a group of decision makers consisting of seven experts identified five strategies and six performance measures for use in the study and analysis. The five strategies (Alternatives) selected in this study are A1: Plan continuity (PC), A2: Utilize technology (UT), A3: Improve SC cyber system (IC), A4: Model nervousness (MN), and A5: Manage supplier relationships (MR). The performance measures (criteria) designated include C1: costs (C), C2: time (T), C3: quality (Q), C4: error (E), C5: soft matters (S), and C6: flexibility (F). The linguistic weighting variables displayed in

Table 2. The linguistic variables for the weights of the criteria and performance rating.

The Linguistic Variables for the Importance Weights of the Criteria		The Linguistic Variables for the Performance Rating
Linguistic Variables	Fuzzy Number	Linguistic Variables	Fuzzy Number
Very Low (VL)	(0,0,0.1,0.2)	Very Poor (VP)	(0,0,1,2)
Low (L)	(0.1,0.2,0.2,0.3)	Poor (P)	(1,2,2,3)
Medium Low (ML)	(0.25,0.3,0.4,0.5)	Medium Poor (MP)	(2,3,4,5)
Medium (M)	(0.4,0.5,0.5,0.6)	Fair (F)	(4,5,5,6)
Medium High (MH)	(0.5,0.6,0.7,0.8)	Medium Good (MG)	(5,6,7,8)
High (H)	(0.7,0.8,0.8,0.9)	Good (G)	(7,8,8,9)
Very High (VH)	(0.8,0.9,1,1)	Very Good (VG)	(8,9,10,10)

were used by seven decision makers to evaluate the significance of the criteria. Additionally, they assessed the ratings of the alternatives in relation to each criterion using the language rating factors presented in Table 2.

Table 3. The important weights assigned to the criterion by the experts.

Ek	LV/TrFN	C1: C	C2: T	C3: Q	C4: E	C5: S	C6: F
E1	LV	M	MH	VH	VH	VH	H
	TrFN	(0.4,0.5,0.5,0.6)	(0.5,0.6,0.7,0.8)	(0.8,0.9,1,1)	(0.8,0.9,1,1)	(0.8,0.9,1,1)	(0.7,0.8,0.8,0.9)
E2	LV	M	H	H	M	H	MH
	TrFN	(0.4,0.5,0.5,0.6)	(0.7,0.8,0.8,0.9)	(0.7,0.8,0.8,0.9)	(0.4,0.5,0.5,0.6)	(0.7,0.8,0.8,0.9)	(0.5,0.6,0.7,0.8)
E3	LV	VH	H	H	VH	H	VH
	TrFN	(0.8,0.9,1,1)	(0.7,0.8,0.8,0.9)	(0.7,0.8,0.8,0.9)	(0.8,0.9,1,1)	(0.7,0.8,0.8,0.9)	(0.8,0.9,1,1)
E4	LV	H	M	M	VH	ML	MH
	TrFN	(0.7,0.8,0.8,0.9)	(0.4,0.5,0.5,0.6)	(0.4,0.5,0.5,0.6)	(0.8,0.9,1,1)	(0.25,0.3,0.4,0.5)	(0.5,0.6,0.7,0.8)
E5	LV	M	H	H	M	H	VH
	TrFN	(0.4,0.5,0.5,0.6)	(0.7,0.8,0.8,0.9)	(0.7,0.8,0.8,0.9)	(0.4,0.5,0.5,0.6)	(0.7,0.8,0.8,0.9)	(0.8,0.9,1,1)
E6	LV	H	MH	VH	MH	VH	H
	TrFN	(0.7,0.8,0.8,0.9)	(0.5,0.6,0.7,0.8)	(0.8,0.9,1,1)	(0.5,0.6,0.7,0.8)	(0.8,0.9,1,1)	(0.7,0.8,0.8,0.9)
Weight		(0.4,0.67,0.68,1)	(0.4,0.68,0.72,0.9)	(0.4,0.78,0.82,1)	(0.4,0.72,0.78,1)	(0.25,0.75,0.8,1)	(0.5,0.77,0.83,1)

displays the important weights assigned to the criterion by the decision makers and the calculations of the criterion weights. It shows the linguistic values (LV), the equivalent trapezoidal fuzzy numbers (TrFN), and the criterion weights where the aggregated fuzzy rating $\tilde{\mathrm{R}}=(\mathrm{a},\mathrm{b},\mathrm{c},\mathrm{d})$ is calculated based on Equations (3)–(6).

Using the linguistic evaluations displayed in

Table 4. The experts ratings for alternatives with respect to each criterion and the fuzzy decision matrix.

Alternative/Criteria	C1		C2		C3		C4		C5		C6
A1	MG,VG,MG,G,G,VG,VG		VG,VG,G,MG,G,F,G		G,MG,G,F,G,VG,F		VG,VG,G,MG,G,MG,VG		VG,VG,MG,G,MG,MG,G		VG,MG,G,G,G,VG,VG
A2	VG,VG,G,MG,F,MG,G		MG,G,MG,G,F,G,VG		MG,G,MG,G,MG,VG,MG		VG,MG,G,G,F,G,MG		G,G,MG,VG,MG,G,MG		G,MG,VG,VG,G,G,G
A3	MG,G,G,G,MG,G,MG		MG,VG,G,G,G,F,G		F,G,F,G,VG,F,G		MG,G,VG,G,G,MG,F		MG,G,G,G,VG,G,G		F,G,MG,VG,MG,MG,G
A4	G,VG,MG,G,G,F,F		VG,G,MG,G,F,MG,VG		G,MG,G,G,VG,F,G		G,VG,MG,MG,F,F,VG		VG,VG,MG,G,MG,F,F		G,MG,F,G,VG,F,G
A5	VG,VG,G,MG,G,G,MG		MG,G,G,MG,MG,VG,G		MG,G,VG,F,VG,MG,F		VG,VG,MG,MG,G,F,VG		MG,G,G,G,F,G,G		MG,G,G,G,VG,G,MG
Alternative/Criteria		C		T		Q		E		S		F
PC	1,1	(5,7.43,8.57,10)	1,2	(4,7.29,8,10)	1,3	(4,6.86,7.29,10)	1,4	(5,7.43,8.57,10)	1,5	(5,7.43,8.29,10)	1,6	(5,7.71,8.71,10)
UT	2,1	(4,7,7.86,10)	2,2	(4,7,7.57,10)	2,3	(5,6.86,7.71,10)	2,4	(4,7,7.57,10)	2,5	(5,7.14,7.86,10)	2,6	(5,7.71,8.43,10)
IC	3,1	(5,7.14,7.57,9)	3,2	(4,7.29,7.71,10)	3,3	(4,6.71,7,10)	3,4	(4,7,7.57,10)	3,5	(5,7.71,8.14,10)	3,6	(4,6.71,7.43,10)
MN	4,1	(4,6.86,7.29,10)	4,2	(4,7,7.86,10)	4,3	(4,7.29,7.71,10)	4,4	(4,6.57,7.43,10)	4,5	(4,6.57,7.43,10)	4,6	(4,6.86,7.29,10)
MR	5,1	(5,7.43,8.29,10)	5,2	(5,7.14,8.14,10)	5,3	(4,6.57,7.43,10)	5,4	(4,7,8.14,10)	5,5	(4,7.29,7.43,9)	5,6	(5,7.43,8,10)

, the trapezoidal fuzzy decision matrix is then built, and each criterion’s fuzzy weight is determined. To calculate the performance ratings of the alternatives with respect to each criterion, Table 4 shows the seven experts’ (E₁–E₇) ratings, respectively, under the five criteria. Next, the fuzzy ratings for each of the six criteria are aggregated. Then, the fuzzy weights of each criterion are calculated using Equations (3)–(6). The second part of Table 4 also shows the fuzzy decision matrix, Equations (7) and (8).

Table 5. Sample calculations of A1C1.

Ek	aij	bij	cij	dij
MG	5	6	7	8
VG	8	8	10	10
MG	5	6	7	8
G	7	8	8	9
G	7	8	8	9
VG	8	8	10	10
VG	8	8	10	10
Aggregated value	5	7.428571	8.571429	10

shows a sample calculation of the first alternative with respect to the first criterion A1C1 (1,1).

Using Equations (9)–(12),

Table 6. Normalized fuzzy decision matrix.

Alternative/Criteria	C				T				Q
PC	0.5	0.74	0.86	1	0.4	0.73	0.8	1	0.4	0.69	0.73	1
UT	0.4	0.7	0.79	1	0.4	0.7	0.76	1	0.5	0.69	0.77	1
IC	0.5	0.71	0.76	0.9	0.4	0.73	0.77	1	0.4	0.67	0.7	1
MN	0.4	0.69	0.73	1	0.4	0.7	0.79	1	0.4	0.73	0.77	1
MR	0.5	0.74	0.83	1	0.5	0.71	0.81	1	0.4	0.66	0.74	1
Alternative/Criteria	E				S				F
PC	0.5	0.74	0.86	1	0.5	0.74	0.83	1	0.5	0.77	0.87	1
UT	0.4	0.7	0.76	1	0.5	0.71	0.79	1	0.5	0.77	0.84	1
IC	0.4	0.7	0.76	1	0.5	0.77	0.81	1	0.4	0.67	0.74	1
MN	0.4	0.66	0.74	1	0.4	0.66	0.74	1	0.4	0.69	0.73	1
MR	0.4	0.7	0.81	1	0.4	0.73	0.74	0.9	0.5	0.74	0.8	1

shows the normalized fuzzy matrix, and

Table 7. Weighted normalized fuzzy decision matrix.

Alternative/Criteria	C				T				Q
W (Ci)	0.4	0.67	0.68	1	0.25	0.63	0.67	0.9	0.4	0.74	0.77	1
PC	0.2	0.5	0.58	1	0.1	0.46	0.54	0.9	0.16	0.51	0.56	1
UT	0.16	0.47	0.53	1	0.1	0.44	0.51	0.9	0.2	0.51	0.59	1
IC	0.2	0.48	0.51	1	0.1	0.46	0.52	0.9	0.16	0.5	0.54	1
MN	0.16	0.46	0.5	1	0.1	0.44	0.53	0.9	0.16	0.54	0.59	1
MR	0.2	0.5	0.56	1	0.13	0.45	0.55	0.9	0.16	0.49	0.57	1
Alternative/Criteria	E				S				F
W (Ci)	0.25	0.66	0.73	1	0.25	0.73	0.79	1	0.4	0.73	0.79	1
PC	0.13	0.49	0.63	1	0.13	0.54	0.65	1	0.2	0.56	0.69	1
UT	0.1	0.46	0.55	1	0.13	0.52	0.62	1	0.2	0.56	0.67	1
IC	0.1	0.46	0.55	1	0.13	0.56	0.64	1	0.16	0.49	0.59	1
MN	0.1	0.43	0.54	1	0.1	0.48	0.59	1	0.16	0.5	0.58	1
MR	0.1	0.46	0.59	1	0.1	0.53	0.59	0.9	0.2	0.54	0.63	1

shows the weighted normalized fuzzy matrix, where W(Ci) represent the criterion weights, as calculated in Table 3.

The next steps are used for the calculations of the fuzzy preference values.

Table 8. The difference matrix.

	C1	C2	C3	C4	C5	C6
D12	0.093	0	0.064	0.12	0.041	0.017
D13	0.06	0.147	0.054	0.093	0.213	0.067
D14	0.127	0.06	0.082	0.04	0.058	0.066
D15	0.013	0.1	0.04	0.092	0.1	0.19
D21	0	0.022	0	0	0	0
D23	0.14	0.022	0.115	0.163	0.357	0.211
D24	0.1	0.015	0.186	0.135	0.202	0.21
D25	0.141	0.077	0.131	0.196	0.142	0.334
D31	0	0.025	0.025	0	0	0
D32	0.056	0	0	0	0	0
D34	0.098	0.114	0.071	0.034	0.087	0.095
D35	0.041	0.107	0.024	0.081	0.127	0.219
D41	0	0	0.04	0.028	0	0
D42	0.082	0	0	0.003	0	0
D43	0	0.014	0	0.017	0	0
D45	0.042	0.011	0.011	0.109	0.115	0.201
D51	0	0.032	0.027	0.04	0	0
D52	0.01	0	0	0	0	0
D53	0.025	0.069	0.008	0	0.1	0
D54	0.04	0.356	0.009	0.1	0	0

displays the distances obtained by the Hamming distance method through Equation (13) between two alternatives g and f with regard to each criterion. When the first and second alternatives are compared, for instance, and the first criterion is taken into account, the first alternative’s distance from ${\mathrm{m}\mathrm{a}\mathrm{x}(\tilde{V}}_{12}, {\tilde{V}}_{21})$ is 0.093, whereas the second alternative’s distance from ${\mathrm{m}\mathrm{a}\mathrm{x}(\tilde{V}}_{12}, {\tilde{V}}_{21})$ is 0. This indicates that, in terms of the first criterion, the second alternative is chosen over the first alternative. Table 8 shows the distance between g and f regarding each criterion.

For example, consider the first criterion C₁, i.e., the cost (C). Each pair of alternatives is compared pairwise for this criterion. Consider the pair strategic management plan continuity PC–utilize technology UT. The fuzzy value for PC corresponding to C₁ is (5,7.43,8.57,10). The fuzzy value for UT corresponding to C₁ is (4,7,7.86,10). We enter the difference between these two fuzzies into the matrix of differences. The difference matrix provided in Table 8 has entries for each pair of alternatives that have been examined in this manner for each criterion.

Using Equation (13), the difference matrix is then created by conducting pairwise comparisons of all the alternatives across all the criteria after the fuzzy numbers have been allocated to each alternative for each criterion. For each set of options for each criterion, the fuzzy number difference is determined. The same standard fuzzy operations are employed.

Using Equation (14), the value of the preference function and the weights of the criterion are used to construct the fuzzy preferences indices, as displayed in

Table 9. Fuzzy preference index.

	PC	UT	IC	MN	MR
PC	-	(0.022,0.052,0.059,2.511)	(0.039,0.1,0.112,0.264)	(0.029,0.067,0.075,0.012)	(0.03,0.071,0.08,0.185)
UT	(0.001,0.003,0.004,0.008)	-	(0.063,0.18,0.202,0.476)	(0.064,0.125,0.142,0.266)	(0.071,0.163,0.184,0.431)
IC	(0.003,0.008,0.009,0.02)	(0.004,0.008,0.009,0.024)	-	(0.033,0.078,0.087,0.103)	(0.041,0.095,0.108,0.25)
MN	(0.005,0.011,0.013,0.029)	(0.006,0.016,0.017,0.046)	(0.019,0.038,0.044,0.098)	-	(0.034,0.078,0.089,0.207)
MR	(0.007,0.015,0.017,0.042)	(0.001,0.002,0.002,0.004)	(0.011,0.032,0.036,0.086)	(0.034,0.082,0.092,0.215)	--
SUM	(0.016,0.037,0.041,0.1)	(0.032,0.078,0.086,2.585)	(0.132,0.35,0.394,0.924)	(0.16,0.353,0.396,0.597)	(0.176,0.407,0.46,1.073)

.

Then, using Equations (15) and ${NF (ɸ)=\widetilde{ɸ}}^{+}-{\widetilde{ɸ}}^{-}$, the fuzzy flows are calculated for each possibility. Equations (3) and (4) defuzzify the fuzzy leaving and entering flows. Defuzzification produced crisp leaving and entering flows, which are displayed in

Table 10. Fuzzy flows for every alternative.

	Positive Flow	Negative Flow	Net Flow	Rank
PC	0.942	0.049	0.894	1
UT	0.625	0.695	−0.07	2
IC	0.22	0.45	−0.23	4
MN	0.192	0.377	−0.184	3
MR	0.175	0.529	−0.354	5

as a result. The net flows are calculated and displayed in Table 10’s last column for a complete ranking.

6. Results and Analysis

Nervousness in the supply chain results in confusion, tension, and distrust. In addition to that, there is an increase in costs, less effectiveness, and lack of competition. SCN impacts the planning process and bullwhip effect and leads to continuous change in partner plans and decisions. Extra inventory, pricing instability, storage space, and demand fluctuations could have an impact over the entire SC. In addition, a company has a loss of trust and credibility with consumers, especially in light of the company’s commitment to continuously supply goods to their customers. The changes also affected suppliers, insurance companies, logistics parties, partners, transportation, and freight companies. This research explores supply chain nervousness strategies, the internal and external factors affecting it, and the mitigation methods, treatment methods, and recent trends. It analyzes management strategies and offers a model for SCN strategies. It also proposes a performance-based framework using the fuzzy PROMETHEE method.

Figure 4 demonstrates the ranking of alternatives using the fuzzy PROMETHEE approach. The alternatives were arranged as follows: A1, A2, A4, A3, and A5, which means that the final ranking of nervousness management strategies is: Plan continuity (PC)–Utilize technology (UT)–Manage nervousness (MN)–Improve SC cyber system–Manage suppliers (MS).

Plan continuity is selected as the best way to appropriately manage strategies for the nervousness in the supply chain, as it allows companies to maintain business continuity, document nervousness reactions, maintain information flow, and have contingencies in place to ensure smooth production and fulfillment. Organizations must ensure supply chain continuity through quick, synchronized decisions, considering factors like risk management, business strategies, geographic area, supplier reliance, stock quantity, event management, and responsiveness to changes. Utilizing emerging technology is ranked second, as new technologies and established tools will significantly assist SC decision makers in assessing potential nervousness and implementing corrective actions to enhance customer service. Manage nervousness is ranked third because supply chain nervousness management involves the process of detecting, evaluating, and managing supply chain nervousness in an organization to enhance efficiency, reduce costs, and enhance customer service, considering it a probabilistic scenario. Improve SC cyber system principal is placed at the fourth level, as it involves a defense based on threat priorities, considering technology, people, processes, and knowledge problems. Finally, manage suppliers was ranked last, as most organizations currently consider it crucial to select and manage suppliers in order to ensure competitive pricing, assess suppliers, explore alternative carriers, consider freight insurance, secure networks, and plan for late delivery. Select suppliers are capable of risk mitigation strategies, considering nervousness effects and potential solutions.

Subsequently, the outcomes were verified by specialists who concurred with the ranking outcomes generated by the MCDM fuzzy PROMETHEE method. The solutions to manage nervousness are prioritized, as shown in Table 10 and Figure 4. As a result, it is evident that PC–UT–MN–IC–MS ranks highest among the SCNMS. Alternative PC is the greatest, followed by UT and MN, then MS and IC, according to the survey data, expert opinions, and the views of several managers and executives of the SC firms. All the results are nearly identical, with the exception of IC and MS, where MS is favored over IC.

7. Conclusions

Globalization has led to the need for innovative supply chain management (SCM) models to address the growing SC nervousness caused by variances in supply and logistics systems. Nervousness can result from planning flexibility, balancing responsiveness and customer satisfaction, and can lead to disruptions in production, delivery, inventory buffers, rescheduling, and planning costs. Scholars often focus on specific nervousness aspects; thus, research gaps in applying these insights to SCN plans and analysis still exist.

This research proposes a model to analyze the SCN strategies. The study introduces measurements indicators, nervousness mitigation strategies, and management strategies for SCN. A nervousness management system should be part of any SC planning and strategies. The model is extended to consider nervousness reduction implementation procedures to improve SC performance. The study plan includes a review of prior studies, the drivers of SCN and their impact on decision making, measurement of strategies, and strategic solutions to decrease the impact of nervousness on the supply chain. The recommendations look into how modern technology might be used to give an innovative solution for the SC. Then, in light of globalization, the fourth industrial revolution, and digital transformation, a model is offered to explain the nervousness strategies and examine their impact on the global supply chain’s efficacy and efficiency.

This research is distinctive in that it thoroughly examines supply chain nervousness strategies, the internal and external factors that contribute to or mitigate this nervousness, nervousness indicators, methods for implementing mitigation actions, and methods for handling nervousness. It also looks at current supply chain trends and how they relate to nervousness. It examines managerial strategies and how they affect the supply chain’s nervousness. This research provides a model for SCN strategies in light of the remarkable developments and changes in business, economics, technology, and science as well as the severe competition that exceeded all expectations. The study also suggests a framework for ranking SCN strategies according to performance metrics using the MCDM technique—the fuzzy PROMETHEE method.

To best of our knowledge, the research is the first to explore the phenomenon and facts associated with the whole SCN systems and to provide nervousness mitigation directions and methodology to resist the ongoing and future crisis. This study aims to bridge the research gap in nervousness assessment of SC studies on quantitative assessment and prioritization of nervousness strategies under uncertainty. The study utilizes the latest information from different sources to examine the strategies on the SCs that lead to SCN. During emergencies managers need to respond and make critical decisions in a rapidly changing environment which might be uncertain. Decision making requirements and inputs are important for global crises (such as COVID-19) to realize the role of SC to meet the principal competency, create strategic decisions, and translate them into actionable, implementable plans. SCs have no choice but to evolve, and organizations need additional innovations and preparations for the future. Continuous improvements require decision inputs, solutions, and evaluation tools. Decision making requires the revisiting of global supply chain nervousness, understanding of the strategies specific to business, and utilization of supply chain experts. The study’s outcomes can be used to create a thorough plan for reducing nervousness as well as a taxonomy for boosting SC resilience.

The results show how the fuzzy PROMETHEE technique is effective in ranking the strategies. The numerical calculations of the case study outcomes rank the strategies based on their importance and priority of implementation. It indicates that the top five nervousness management strategies are in this order: plan continuity, utilize technology, manage nervousness, improve SC cyber system, and manage supplies. This research provides a model for the SCN strategies especially in light of the unprecedented progress and change in business, economics, technology and science, and the intense competition that has crossed all known limits.

The research offered in this paper can assist managers, decision makers, and business experts in identifying the most effective methods for implementing SCN extenuation programs in their SCs. In addition, this work allows managers to analyze these strategies by determining their relative importance in the implementation of SCN mitigation at the strategic level within the organization. The work also helps industry managers to prepare strategies and courses of action for application, eliminate the causes of SCN deployment, and successfully manage SCN decision-making initiatives in their GSCs.

The study has a number of limitations. A fuzzy PROMETHEE-based analysis framework was used in this study; it identified five main strategies and six performance metrics as criteria in an SC context. Other strategies were not categorized or listed. The conclusions presented in this article were mostly based on the experts’ input. Therefore, rigorous execution of the evaluation methods is required. The results therefore should not be applied generally. To increase the adoption and implementation efficiency of the SCN methods in various industry sectors, the proposed fuzzy PROMETHEE-based analysis model may also be applied. The model also has significant restrictions when it comes to addressing some behavioral issues like politics and power, ethics, and social responsibility.

Future studies could be extended to include a deeper, detailed study of the supply chain nervousness plans. Other research may include other strategies and supply chain management policies in the presence of nervousness, as well as planning for nervousness so that it is part of any future SC plans. On the other hand, emphasis can be placed on tonometers and mathematically linked to the performance indicators of the supply chain. Additional work can investigate and compare various methods for the defuzzification of trapezoidal fuzzy numbers and include the pseudocode analysis and conventional complexity assessment. Further study can also be applied to specific supply chains, such as those related to food, agriculture, and medical systems. Finally, future research may consider the relationship between nervousness strategies and future SC resilience.