Supply chain management (SCM) is a fundamental system that combines many aspects of creation and circulation to increase production intensity [1]. SCM has its roots in operation and logistics management. The main concerns during the early stages of SCM are planning procedures for transporting items from one location to another. The concept of SCM has evolved over the past few decades to include advanced technologies and globalization, moving away from an individual company focus toward integrated supply chains [2]. To ensure that items are manufactured and delivered effectively to meet client needs, all processes from procurement to delivery are coordinated, to reduce costs and enhance service delivery. SCM entails the effective integration of manufacturers, suppliers, and warehouses [3]. Sophisticated supply chains are defined by complex interdependencies among enterprises that demand cooperative partnerships to facilitate effective information exchange and decision-making [4]. The SCM process is relevant to a range of industries, including pharmaceuticals, where machine learning models optimize shipping processes [5], and retail, where flexibility is crucial for controlling product availability and customer expectations [6].

SCM offers many benefits but also has drawbacks, particularly with regard to readily adjusting to market changes. In this ever-changing world, supply chain partners must continuously innovate and be strategically aligned. The SCM losses stemming from risk are presented in Table 1.

Networks and corporations are also trying to withstand the effects of rare catastrophic events, whereby scientists, legislators, and businesses continue to grapple with the immediate and long-term effects of environmental changes in SCM [7]. The Amazon rainforest fires are an excellent example of such disasters. The source of these hellfires and complex web of interwoven sociopolitical aftermath that follows are of human origin [8, 9]. Artificial intelligence, process automation, supplier relationship management, supply chain development, and full-on-demand access to all the data facilitate, assess, and enhance individual supply chains [10, 11]. Procurement solutions may also be used to provide a closed environment that stops fraud and unlawful spending by one-time vendors who may not follow the law or the high standards of their firms [12]. The construction and application of business intelligence data are meaningless without analysis and application to planning, strategy or decision-making. Transparency in the supply chain is the first step. Data gathered from supplier performance, expenditure statistics, and process improvement activities may assist in strategically fine-tuning the supply chain [13, 14]. The less critical commodities are products, services, costs, vital items, capacity, and backup providers. Strategic redundancies are utilized when required to support contingencies for events such as trade disputes, political instability, and natural calamities.

SCM protocols promote optimal flexibility, responsiveness, enhanced connections with essential suppliers, optimal pricing, and conditions, as well as an optional inventory system of potential sellers capable of meeting demands during emergencies [15]. Noncompliant vendors are identified and removed from the supply chain before they jeopardize company earnings, production, or reputation [16, 17]. Data breaches, denial-of-service attacks, and industrial space can result in irreversible losses such as loss of trust, litigation, and competitive advantage, in addition to financial costs [3, 18]. Every supply chain network involves risks, from the smallest local market to the international supply networks of multinational companies. By recognizing the possible threats to one’s supply chain and creating supply chain risk management plans, the risks can be mitigated, and the success of a business can be ensured both locally and globally. The hesitant fuzzy-technique for order preference by similarity to ideal solution (HF-TOPSIS) approach is used to rate SCM solutions based on overall appropriateness. The proposed method is applied to select the SCM factors and alternatives from a dataset formed by expert opinion and synthetic data vault [19].

To summarize and incorporate previous research on SCM, an overview of the primary ideas, patterns, and gaps in the literature is provided below. Operations that turn raw materials into completed commodities and control the flow of goods and services are included in SCM. Every step of the production process is covered by SCM, from obtaining raw materials to shipping the finished product to the client. The three main goals of SCM are to ensure customer loyalty, reduce expenses, and advance expertise [20, 21]. Supply chain incorporation emphasizes the importance of the different parts of the supply chain functioning together. Considerable amount of research has been conducted on the impact of integration on performance, flexibility, and customer satisfaction.

Pan et al. [22] mentioned that a technique that promotes fresh advancement and restricts scope mining for networks can quickly degenerate into an environment-attacking plague with sufficient contemporary usage. The lack of natural resources is especially acute for pharmaceutical companies, as they rely on 80,000 different plant species to supply more than 25% of today’s pharmacological plant-based medicines [23]. Thus, a systematic SCM is required in the healthcare industry.

Carr [24] mentioned that production and transportation expenses may increase when regions known to have vital resources are destroyed because substitutes need to be found and investigated from locations that are more remote and difficult to reach. Businesses with few competitive advantages may discover that they have lost their advantages as labor and supplies become more expensive, thereby increasing expenses [25]. Chiu et al. [26] defined finance and used mean fluctuation strategy to control risk. Over the past few years, this crucial concept has been increasingly used to address the stochastic store network problem. A well-known technique for risk analysis in stochastic operational models of supply networks is the mean-variance theory. Park et al. [27] mentioned that SCM has been examined from several angles although the importance of the global supply chain has not been sufficiently examined as a tactic to overcome major interruptions to the supply network. The responses of contemporary Japanese initiatives to recent earthquakes, tsunamis, and atomic accidents were the primary focus of this study. Using case studies of Japanese manufacturing enterprises, the study investigated how supply networks are restored following significant natural disasters and humanitarian crises. It also examined the lessons that supply chains can teach us about disaster preparedness and recovery. Dispersion, mobility, and the fundamental qualities of supply chain information design have also been discussed [28].

Odulaja et al. [29] thoroughly examined how contemporary supply networks adjust and prosper in an unpredictable and volatile environment. This study defines supply chain resilience, investigates strategies for safeguarding supply chains from geopolitical shocks, and thoroughly examines the impact of these disruptions on supply chain dynamics. A multifaceted view of supply chain volatility is simpler with this method because it uses historical perspectives, presents patterns, and provides insights into the future [30]. MacDonald and Corsi [31] mentioned that disruptions tend to occur despite the management’s best efforts, usually resulting in lost sales and large financial losses, with a negative impact on shareholder value and operating performance. However, the management of a break from the site of identification to full recovery has received little attention. Notably, the entire process is not well-understood, making it important to acquire a deeper understanding of the elements that impact the recovery process, the ways in which these factors interact to influence management decision-making, and a company’s real capacity for recovery [32]. The results of this investigation illuminate the connections and interplay between different elements, providing support for a series of hypotheses that can serve as a foundation for further investigations in the following areas: the reason for the disruption, its origin, and effectiveness of the recovery procedure.

A summary of the major trends and a list of important facilitators and obstacles in supply management were provided by Storey et al. [33] who explained that supply management is still in its infancy, both theoretically and practically. This study raises several questions regarding how supply strategy and supply chain management are currently considered. This highlights the enormous gaps that exist between theory and reality. Certain developments may lead to improved prospects for SCM. Fuzzy sets have been extended to include intuitionistic and hesitant fuzzy sets [34], which are commonly used to address decision-making issues. Each of these additions provides a more detailed description of the membership values and parameter functions. A recently developed hesitant fuzzy-analytic hierarchy process (HF-AHP) approach was used for multi-criteria supplier selection. A company’s success may be greatly impacted by strategic moves such as mergers, acquisitions, and joint ventures [35]. Businesses that wish to prosper in fiercely competitive situations must make wise and prompt strategic decisions. Furthermore, the numerical assessment of these properties is usually difficult and inaccurate. The goal of this study is to develop a multi-role dynamic model that considers the complexity and ambiguity of important decisions. Based on the weights assigned to the components by the interval type-2 fuzzy AHP, HF-TOPSIS determines the optimal approach [4, 36, 37]. The next section explains the dependent factors and supply chain alternatives. The applied methodology is explained using a mathematical model.

Numerous variables influence the efficacy, robustness, and efficiency of SCM. These factors are linked and play a crucial role in how the overall supply chain is presented [12, 15] (Figure 1). An overview of crucial characteristics and possible decisions that enhance SCM is provided below.

3.1 Factors

Natural disaster [A1]: Natural catastrophes, such as flooding, earthquakes, or wind damage, can cause major damage to manufacturing facilities. These events may stop operations or force the plant to close temporarily, delaying production [38].

Earthquake [A11]: In Japan, just-in-time deliveries pose challenges to businesses. In addition to the physical consequences of the earthquake, research indicates that businesses in the supply chain suffer the most from the loss of suppliers and consumers. The 2011 Tohoku earthquake and tsunami damaged national property in the amount of $210 billion and disrupted global supply chains. Unable to obtain or transfer the necessary parts, Toyota, General Motors, and Nissan temporarily suspended operations in the United States and Japan [39].

Fire [A12]: When natural disasters impact all facets of life and business, they simultaneously disrupt the availability of labor, raw resources, and transportation. Extreme weather patterns caused by climate change have the potential to cause unanticipated disasters such as fierce fires, which can destroy an inventory network by altering the accessibility of secret weapons through changes in the ocean level, modifying sporadic weather patterns, or destroying areas that support vital resources through the eradication of plants and animals [40].

Flood [A13]: The next link in the chain is the transportation system. If a disruption occurs, the completed items and raw materials are delivered later. Floods, landslides, hurricanes, and typhoons can block ports, obstruct roads, and make it difficult for factories to receive supplies of raw materials [41, 42].

Hurricane [A14]: Delays are often caused by concerns about strong winds and heavy precipitation, with transportation companies likely to reroute or cancel cargo entirely. Such postponements not only result in a belated appearance but may also prevent products from arriving [43]. Fuel is typically required immediately after the occurrence of a strong hurricane. Infrastructure problems also arise in supply chains during hurricane seasons. Overcrowded or closed bridges, extensions, and roadways can cause delays in aviation, trains, and cargo freight. These delays result in supply chain backlogs and temporary product shortages [44].

Geopolitical uncertainty [A2]: Uncertain operating conditions caused by local or global disruptions can result in supply chains experiencing higher costs, greater complexity, and lower efficiency. Tariffs, sanctions, and other measures can increase regulatory costs and disrupt access to suppliers, markets, and essential supplies. Political or military unrest can affect important transportation lanes, causing companies to search for alternate routes [45]. These problems are best demonstrated in the latest battle between. Russia and Ukraine. Considering that both countries are net exporters of agricultural products including wheat, corn, and sunflower oil, a protracted war may compel temporary suppliers to search for new products. Although Russia is a significant provider of metals, fertilizers, and petroleum products to the United States, Ukraine is the primary producer of neon gas, which is vital to the semiconductor sector [46].

Tariff [A21]: Business approaches to global supply chain strategies are changing because of trade tariffs and their repercussions, to address the important concerns of clients and governments. In a world in which economies are interwoven, the imposition of trade tariffs may force enterprises to review their operating plans [47].

Economic condition [A22]: Financial conditions impact and are influenced by inventory network executives. Variables such as growth, customer demand, international trade agreements, and transportation costs may affect the efficiency and sufficiency of supply chains. Organizations must adapt to these financial conditions to remain vigilant and serious. Well-functioning SCMs help associations improve their operations and respond to changing demands from the business sector, ensuring that labor and goods can be delivered quickly [48].

Trade dispute [A23]: Supply chain disputes are often complicated and require swift cross-border resolution. When used in conjunction with other alternative dispute resolution (ADR) options, international arbitration offers an adaptable, effective, and efficient framework for resolving these disputes, thereby saving considerable time, money, and business relationships if performed correctly [49].

Disruptive events [A3]: Any event disrupting the production, sale, or distribution of goods is considered a store network disruption. Disasters such as pandemics, territorial conflicts, and apocalyptic disasters may disrupt a store network [50].

Climate catastrophe [A31]: Current factors that contribute to supply chain disruptions are connected to climate change. Past disruptions in trade caused by natural disasters and ongoing ecological changes prompted nations and organizations to examine the adaptability of supply networks [51].

Meteorological events [A32]: Storms, floods, and landslides can harm the transportation infrastructure, including roads, bridges, and ports. These disruptions can cause delays, and rerouting, increasing expenses by impeding the efficient flow of goods and supplies [50].

Hydrologic catastrophe [A33]: Hydrological disaster management includes changes in the Earth’s water value and the distribution and movement of water under the surface and in the environment [51].

Geophysical event [A34]: Events related to geophysics, such as earthquakes, floods, and other catastrophic events, pose serious risks to executives in the production network. A flexible and robust humanitarian supply network is required for efficient administration during such incidents. Important strategies include creating a simple production network, identifying vulnerabilities, and using risk assessment tools to reduce the risks associated with geophysical events [52].

Business challenges [A4]: Business issues in supply chain management include rising hazards, unforeseen delays, cost control, and the need for effective partner-to-partner coordination and data synchronization. These challenges might make it more difficult for an organization to react swiftly to changes in the market and client needs [53].

Labor disputes [A41]: Numerous factors have led to labor conflicts and strikes worldwide, leading to concerns about unemployment and job development in recent years. For a very long time, U.S. rail workers have been on the verge of going on strike to organize their working conditions. In 2022, port and rail worker strikes in Austria and workers assembling in other countries, complicated negotiations [54].

Change in customer requirements [A42]: A competitive environment with rapidly shifting customer expectations and fierce competition distinguishes the operations of current businesses. Some of the factors behind these changes include expanding acceptance of e-commerce, need for faster delivery, and a growing focus on environmental sustainability. Companies are under extreme pressure to meet and even surpass their logistical performance criteria, to adjust to changing consumer trends [55].

Regulatory changes [A43]: Natural rules are usually included for administrative consistency, forcing groups to adopt sustainable activities. Encouraging responsible supply chain management may take many forms, such as promoting eco-friendly packaging and reducing carbon footprint when environmental standards are followed [56].

3.2 Alternatives

Systematic supply chain management [C1]: In a network of deliberate production rather than separate management of different skills, the SCM board can help resolve conflicts by focusing on a combination of important issues, adopting a coordinated approach supported by information that has been analyzed, which leads to more sophisticated navigation and intricate production network operations. This study systematically evaluated SCM challenges and current practices, highlighting the need for effective methods to improve store network execution [57].

Risk identification model [C2]: This model enables systematic risk identification within a supply chain by mapping and assessing the main product value chains. This methodology enables enterprises to identify vulnerabilities at various nodes within a store network, thereby enhancing the ability to effectively monitor and mitigate these risks. This concept can be used to increase the reliability and resilience of supply networks, particularly in unpredictable situations [58].

Predicted outcome model [C3]: Future-focused models predict variations in expenses associated with labor, raw materials, and other components of the production network, assisting with budgetary planning. By simulating various operations and their anticipated outcomes, firms can make well-informed decisions regarding cost-cutting initiatives [59].

Develop a response strategy [C4]: Executives in the production network can effectively handle challenges by ensuring flexibility and preparedness in the face of disruptions. This includes applying tactics such as having reinforcement providers, customizing transportation plans, and enhancing communication throughout the supply chain. By foreseeing possible problems and adopting preventative actions, businesses can lower risks and preserve their operational effectiveness [60].

Regularize decision-making [C5]: Regularizing decisionmaking in supply chain management may aid in solving a variety of difficulties, including improving efficiency, agility, and the ability to rank concerns in order of significance. By implementing data-driven procedures and uniform rules, supply chain managers can manage complexities and enhance operations. Considering explicit scenarios is essential in best utilizing regularized procedures [61].

Mitigation strategy [C6]: Supply chain management problems can be successfully addressed using mitigation techniques by implementing preventative measures and creating emergency plans. These approaches aim to reduce risk, enhance preparedness, and ensure that inventory networks operate smoothly and efficiently [7].

Multi-criteria decision-making (MCDM) in SCM can be of help in several real-world issues, to arrive at the best judgment [22]. The AHP is a structured approach, suitable for MCDM activity [3]. The present study proposes a successful approach that employs AHP for decision requirement analysis and TOPSIS for function identification to address the problem of identifying the most advantageous aspects of SCM [4, 62]. To obtain more accurate results, this study used hesitant and hesitant fuzzy approaches [63]. TOPSIS is simple in computation, whereas MCDM provides more intricate methods [20, 64]. The following metrics were compiled to ascertain the value of the sub-techniques or approaches, as listed in Figure 2. The process is outlined below.

Step 1: Create a tree structure for the multi-level problem.

Step 2: Match the correlation lattices used to address semantic improvements [62].

Step 3: Convert the assessments using reluctant fuzzy wrapping [16] and Eq. (1).

where W = (w1, w2, wn). The comparative balance vector that complies with the (i = 1) criterion is called S, with relevance of nW = 1 and B_j comparable to that of A₁, A₂, A_n. The hesitant fuzzy limitations of the trapezoidal figures are c = (A, B, C, D), as in Eqs. (2)–(4), subsequently determined using Eq. (5).

The primary and secondary weights must then be established for each property using Eqs. (6) and (7) independently [32].

The second type of weights (W2=(w12,w22,…,wn2)) are

With the support of μ1=r-(j-1)r-1s, μ2r-(j-1)r-1, where r represents the priority number; i and j represent the secondary numbers.

Step 4: Using Eqs. (8) and (9), the pairwise comparison matrix is completed.

Step 5: Defuzzification is performed using Eq. (10).

The consistency ratio (CR) [20] is estimated using Eqs. (11) and (12):

Step 6: The geometric mean is calculated as follows [65]:

Step 7: The assumed weights are then evaluated using Eq. (14) [65]:

Step 8: Furthermore, clear defuzzification of the HF figures is performed using Eq. (15) [66]:

Step 9: The weights are then normalized as follows [27]:

Selecting the optimal solution using HF-TOPSIS is the next step. One popular MCDM technique [67] that experts use to select the optimal answer for practical issues is TOPSIS [68]. The answer closest to both the ideal negative and positive scenarios can be obtained using TOPSIS [69, 70]. In this study, properties defining the mechanism were ranked using the HF-TOPSIS approach [66]. This technique is based on measuring distances in the middle of G1s and G2s. The distance is specified as env (G1s) = [Lp, Lq] and env (G2s) = [Lp*, Lq*] when the envelopes are provided. The technique can be expressed by Eq. (17) as follows:

Step 10: The preliminary stages for this process are as follows:

Select the following concern by taking Q alternatives (C = {C₁, C₂, . . . , C_E}) and n criteria or characteristics (C = {C₁, C₂, . . . , C_n}):

The specialists are stated using e_x, and the number of practitioners is K.

X˜l=[HSijl]Q×n is a hesitant fuzzy assessment matrix, in which HSijl .is the approximation mark for alternative I(Ci) against criterion j(aj) stated by expert e_x.

The scale for the HF-TOPSIS methodology is as follows:

Let Scale = {Nothing, Extremely Bad, Poor, Moderate, Excellent, Very Excellent, Perfect} be the language that generates its relative linguistic words without regard to context, and let the CH represent an expressed or linguistic term set. Similarly, the rankings of the two experts, e1 and e2 are calculated for the two characteristics, R1 and R2.

The hesitant fuzzy coating for the related comparable phonetic articulation is the next coating to appear [4].

Step 11: The following phase combines the specific calculations of practitioners (X̃¹, X̃², . . . , X̃^K) and constructs a combined assessment matrix X = [xij], where xij represents the calculations of Ci in contrast to aj and is accurately represented as xij = [Lpij, Lqij], as follows:

Step 12: Let α_c stand for the cost criteria, with lower values in aj indicating choice, and let α_b stand for the help feature or criterion, with higher values of aj reflecting preference. Using a substantial hesitant fuzzy linguistic set, the positive ideal solution is scientifically represented as C̃⁺. C˜+=(F˜1+,F˜2+,…F˜n+), where F˜j+=[Fpj+,Fqj+] (j=1,2,3,…,n) and negative HFLTS ideal solution is indicated as C̃⁻ and scientifically symbolized a C˜-=(F˜1-,F˜2-,…,F˜n-), where F˜j-=[Fpj-,Fqj-] (j=1,2,…,n), as expressed by Eqs. (19)–(21).

Additionally, V˜pj+,V˜qj+,V˜pj- and V˜qj- are described for the cost and benefit criteria such that

Step 13: Using Equs. (22) and (23), we obtain the positive and negative ideal difference matrices (V⁺ and V⁻), respectively.

Step 14: Using Eqs. (24)–(26), the comparative closeness score is determined for each option under consideration.

Step 15: Possibilities are ordered according to their relative proximity ratings. The next phase uses HF-AHP-TOPSIS to analyze the data and produce outcomes.

In the process of evaluating the priority of the selected SCM risk models, some of the features were used to classify these models. In order of priority, the models at the top of hierarchies A1–A4 were ranked and run through specific AHP phases. To facilitate understanding, the hierarchy covered in the preceding section of the study illustrates the placement of certain technologies, together with the inherited sub-layered traits that correspond to them. Using a hierarchy based on the literature and adopting the described AHP approach, the effects of SCM risk analysis models were measured on expert data records, using the synthetic data vault (SDV) library [19, 71]. The pairwise comparison matrices are presented in Table 2.

Verbal ideas were converted into quantitative values by aggregating the triangular fuzzy numeric (TFN) values via Eqs. (1)–(9), using the standard Satya scale [72]. Next, consistency and irregular files were computed using the conditions in Eqs. (10) and (11), respectively.

A random index of less than 0.1 was used for the pairwise comparison matrix representation. Tables 2–4 provide detailed descriptions of the same data. From C1–C6, six unique options were selected for SCM risk analysis [7, 43–47]. Because these six SCM alternatives cover a variety of segments, several methods can be used to interpret the evaluation and assessment results. After the final chance analysis, the effects of the alternatives were ascertained using various methods. Eqs. (1)–(5) and (10) were used to evaluate and gather input from the procedural data of the three activities. The standardized hesitant fuzzy-based judgment matrix was calculated using the weighted normalized hesitant fuzzy decision matrix. To achieve this, Conditions (16)–(18) and Eqs. (19)–(26) were employed for the calculations. The gap and closeness coefficients are shown in Table 5 and Figure 3.

The acquired weights were then subjected to a sensitivity analysis. Fifteen elements were collected to assess the responsiveness of the tests. The level of satisfaction (CC-i) in each preliminary was determined by varying the merits of the issues while retaining those of various components via both the HF-AHP-TOPSIS method and reluctant fuzzy AHP-TOPSIS technique. The expected outcomes are presented in Table 6 and Figure 4. Alternative option one (C1) provided an exceptional level of satisfaction based on the real performance (CC-i).

Throughout this investigation, the researchers evaluated the validity of the findings and symmetricity in several ways. Fuzzy possibilistic C-meansHF-AHP-TOPSIS and normal AHP-TOPSIS are similar in terms of information collection and estimation approaches. Performing preliminary fuzzy AHP-TOPSIS fuzzification and defuzzification was allowed. The differences between the standard AHP-TOPSIS and HF results are shown in Figure 5. Although the conclusions of these studies differ, they are essentially the same. The Pearson connection method was used to investigate the relationships among the outcomes of the experimental analyses. The results are comparable, as shown by the 0.89176 Pearson correlation between the classical AHP and hesitant fuzzy AHP results. Table 7 presents all the data, which demonstrate a strong correlation between the hesitant fuzzy and classical AHP results. Studies using the same dataset but with other SCM criteria corroborate these results.

Our findings also demonstrated that from the perspective of influential factors, the identified variables and their relationships using an expert gamble analysis of SCM skills, were spectacular. Nadeem et al. [4] employed the entire AHP-TOPSIS approach for HF. This is because in contrast to the use of a tree structure, the AHP technique uses a hierarchical structure. Accordingly, the scientists’ decision to retain plan methods while participating in the foundational phase of the present evaluation had a positive impact on the results. System security cannot be assessed concurrently using the HF-AHP-TOPSIS approach in the context of design policy efforts.

These results illustrate the unique characteristics of SCM in the retail, healthcare, information technology, media, and information sectors. The HF-AHP and HF-TOPSIS methodologies were fully utilized in this study. This is because the HF-AHP approach differs from the previous methods in that it employs an AHP instead of a tree structure. SCM affected planning tactics as part of the momentum investigation underlying the stages, which had a significant effect on the results. Applying the SCM risk analysis approach concurrently across different economic sectors is not possible. The main goal of this study was to ascertain that the SCM risk strategy determines the supply chain, which has an effect on the different SCM techniques. This study aims to assist SCM experts, managers, and organizations in determining the SCM approach that makes the most sense for rational improvements in industries. To examine the effects of different SCM parameters, a multistandard navigation framework was combined with the hesitant HF-AHP and HF-TOPSIS approaches. The SCM procedures and elements are important for a company. Compared with the other SCM factors, A14 (hurricane) > A12 (fire) > A34 (geophysical event) > A43 (regulatory changes) > A42 (change consumer requirements) > A32 (meteorological events) > A13 (flood) > A41 (labor disputes) > A31 (climate catastrophe) > A11 (earthquake) > A33 (hydrologic catastrophe) > A23 (trade dispute) > A21 (trade tariff) > A22 (economic conditions). Hurricanes received the highest weight in SCM factor assessment through HF-AHP. People and organizations must consider the factors affecting the analysis in the management or development phase of the supply chain. SCM alternatives were evaluated using the HF-TOPSIS technique as follows: C1 (systematic supply chain management) > C4 (development response strategy) > C2 (risk identification model) > C5 (regularized decision-making) > C3 (predicted outcome model) > C6 (mitigation strategy). Systematic SCM was the most often used SCM option. This was followed by the development of a response strategy that utilized the selected SCM components. The SCM mitigation strategy was ranked the lowest in alternative risk assessment.

The evaluation of SCM factors and alternatives through a hierarchical structure relates the impact of these factors with selected alternatives. It not only identifies the selection priority but also provides guidance to organizations. This study offers a thorough review of several SCM improvement strategies across various sectors. Despite its restricted application, this evaluation can be significant for businesses working in creative and goal-oriented developmental contexts. Workers in SCM frequently encounter a wide range of novel difficulties. Multi-standard navigation balancing innovations may be more reasonable for resolving multi-standard navigation concerns, even when combining the HF-AHP and HF-TOPSIS methodologies for assessing the influence of SCM innovation on diverse industries. The reaction and analysis study focused on outcomes that would serve as future references.

The basis for this priority evaluation analysis is to select the SCM approach for the different areas of an organization. The combined HF-AHP approach statistically evaluates and weighs several SCM-dependent elements, with hurricanes having the highest weight and economic situations having the lowest. The results of this study may be helpful to experts who choose the SCM approach. Specialists can use these results to enhance SCM priorities and provide recommendations. However, understanding the specific limitations of this study is important because they need to be considered in future evaluations. A disadvantage of SCM is its capacity to collect data. Although fully understanding and evaluating these massive volumes of data may be challenging due to their complexity, advancements depend on them. Despite these drawbacks, the conclusions of this study are still relevant and have the potential to improve SCM risk assessment. Future investigations can solve this issue by focusing on certain data subsets that are most pertinent to the study’s goals and using more efficient data-gathering techniques, or analytical procedures. By recognizing and overcoming these constraints, future research can expand existing findings and offer further insights into the evolution of the SCM framework. Improved criteria for this examination may be offered to experts to help them refocus on forward growth. The limitations of this assessment should be considered in future studies. The limitations of this study are as follows:

Understanding the amount of information that contributes to the outcome might be challenging. What would make a difference in the information held by professionals?

Many more controllable and useful SCM-dependent factors might have been required in the assessment.

The data studied in this article show how SCM factors impact a product’s SCM, considering the majority of SCM variables and alternatives. This study presents the most important aspects of previous SCM modeling results and offers insights into different SCM mitigation approaches and their future impact. In the future, awareness and analysis experiments should be conducted to increase the accuracy of the results.

There are no potential conflicts of interest to declare relevant to this article.

This work was funded by the University of Jeddah, Jeddah, Saudi Arabia (Grant No. UJ-23-DR-246).

The author is grateful to the University of Jeddah for technical and financial support.

Table 1. SCMlosses due to various associated risks around the world.

Table 2. Hesitant fuzzy pairwise comparison matrix (HPCM) at level 1.

Table 3. At level 1, combined hesitant fuzzy possibilistic C-means (FPCM) criteria.

Table 4. Overall weights.

Table 5. Closeness coefficients of numerous alternatives.

Table 6. Sensitivity examination.

Table 7. Comparative analysis.