Evaluating Wheat Suppliers Using Fuzzy MCDM Technique

Abstract

Wheat has significantly impacted food security in numerous countries. Like the Middle East and North Africa (MENA) region, Jordan’s daily diet contains a sizable amount of wheat. Further, Jordan is dealing with several issues, including rapid population growth, water scarcity, widespread urbanization, and limited agricultural wheat production. Thus, it imports most of its wheat and wheat products. Moreover, the method of selecting suppliers in Jordan is unique, as private importers import for the benefit of the government, and thus, the selection of suppliers is carried out by importers. This study aims to examine the various supplier selection approaches to determine Jordan’s primary wheat suppliers and rank them according to specified criteria. The fuzzy-VIKOR approach was used to assess, select, and rank the best wheat suppliers in Jordan. The findings suggest that Romania is the best supplier of wheat for Jordan. It is recognized as the most affordable and trustworthy supplier since it is nearby, has strong links through official channels, and is flexible. Suppliers are subject to change or adjust their offerings as a result of changes in the global economy, wheat prices at the source, exchange rates, transportation and handling costs, crises, and national export levies. This study will help importers, decision-makers, and others concerned with wheat imports as a strategic commodity identify and select suppliers.

1. Introduction

Wheat is a basic product in most countries, especially in the Middle East and Jordan, where the food table is packed with wheat derivatives. It serves as a regular meal in the form of bread, pasta, sweets, or many other wheat products. Therefore, it is the main and most basic commodity that the government manages through the import, storage, and distribution of wheat. According to the Ministry of Industry and Trade (MITS) [1], the average monthly consumption of wheat is approximately 90,000 tons. The concerned government authorities, namely the MITS, periodically initiate invitations to bid/request for proposal (RFP) for the purchase of wheat under specific conditions and specifications in cooperation with the Ministry of Agriculture (MOA) [2] and the Jordan Standards and Metrology Organization (JSMO) [3]. The tender quantity depends on the available storage capacity, consumption, warehouse management, strategic food security plans, and storage conditions. The tender is usually delivered to the port of Aqaba or to the storage warehouses, that is, carriage and insurance paid (CIP). Therefore, the tender includes the costs of purchase, shipping, transportation, handling, insurance, and other fees and costs. The import of wheat in Jordan is different, where the concerned authorities in the government issue an invitation to tender (ITT) and the applicants have to choose the suppliers according to the announced conditions, meaning that the government does not directly choose the supplier, but rather, the applicant or bidder chooses it.

The conditions of the tender should be clearly indicated in advance and include specifications, quality, timing, the solvency of the supplier, necessary procedures, contractual and financial matters, and the necessary tests and acceptance conditions. Post-award of the tender, the concerned authorities should also follow the specifications for storing and distributing wheat according to the principles set for this purpose. Furthermore, it should ensure the availability of a strategic stock of this product for certain periods, usually six months to one year at least.

The main wheat specifications include the origin, protein, test weight, moisture, virtuousness, fall number, wet gluten, soft grain admixture, foreign matter, and grain admixture [4,5]. The key processing quality parameters are grain hardness, protein concentration and quality, and gluten strength. Wheat varieties depend on the producers’ classifications, which generally include durum, hard white, soft white, hard red winter, hard red spring, and soft red winter. Previous studies have not been carried out either for Jordan or the MENA region that rate or choose the top wheat providers [6,7,8,9,10,11,12]. Hence, this study aims to fill this gap by answering the following research questions: What are the approaches and stages of supplier selection? Who are the wheat suppliers providing quality products at the lowest price with flexible delivery? What criteria should be used to evaluate suppliers? Who are the best wheat suppliers in Jordan based on the current international environment and situation?

One of the primary challenges in decision-making is to pick the optimal alternative after considering many selection criteria. Multi-criteria decision-making (MCDM) techniques are commonly used for handling a variety of decision-making criteria. Due to these techniques’ ability to compute, they have been used extensively in the supply chain field.

The advantage of the VIKOR technique is that it can select a compromise option that represents the views of the majority of decision-makers. The key phase of this strategy is selecting and sorting the results according to multiple sets of criteria. The VIKOR approach was developed to address problems in MCDM, including complex, conflicting, and unrelated criteria. It is used to gather decision-makers’ viewpoints on supplier selection (SS) as a group MCDM problem in the form of linguistic words. With the original (provided) weights, it determines the compromise ranking list and the solution. When there are competing criteria, this strategy focuses on ranking and choosing from a group of choices. While there are many methods used to select the best providers, VIKOR was chosen here because it has been previously used in selecting suppliers and as described in the literature review section.

The main goal of this study was to employ MCDM techniques to identify the main wheat suppliers in Jordan and prioritize suppliers based on the identified attributes. The government is responsible for importing, managing, and storing wheat, which is a key food commodity in Jordan. This study aimed to explore alternatives for wheat providers based on accepted norms. To identify suppliers of wheat or other goods, products, or materials efficiently and lower the risks involved in the selection process, this study presents a thorough framework for SS. The fuzzy-VIKOR multi-criteria decision-making approach was utilized to evaluate, select, and rank the best wheat suppliers in Jordan. A numerical case study was used to identify and analyze the main wheat suppliers to Jordan and select the best supplier based on the identified attributes.

The rest of the paper is organized as follows: Section 2 presents the literature review on agri-food supply chains (SCs), wheat suppliers, wheat supplies to Jordan, the MCDM approach, and the gaps in previous research studies. Section 3 describes the methodology and solution approach in terms of the SS framework, including strategies and plans, and the proposed fuzzy-VIKOR technique, which is used to prioritize suppliers based on selection criteria. Section 4 underlines how the proposed technique can be employed using a real-world example, followed by the presentation of the analysis and the results. Section 5 concludes the study and provides directions for future research.

2. Literature Review

The agri-food supply chain includes the transfer of agricultural products from the point of production to consumers. The agri-food SC encompasses all the phases of agricultural food production and processing, including production, processing, storage, trading, distribution, and consumption. The agri-food industry plays a vital role in political and economic growth and development. It has a significant impact on sustainability in terms of meeting human needs, fostering employment and economic growth, and protecting the environment [13]. All parties involved must work to reduce logistics costs and gain a competitive advantage in the global market. Consequently, it is crucial to develop excellent agricultural and food logistics [14]. Further, following COVID-19, agricultural supply chains (ASCs) have been exposed to sudden disruptions [15]. Therefore, ASC management is extremely important as it is vulnerable to errors [16]. The future of ASC management can consider organic agriculture, agricultural technological advancements, the Internet of Things (IoT), blockchain technology, and smart farming [17].

Wheat is one of the most important agricultural crops worldwide, particularly because it is consumed directly by people rather than being utilized as livestock feed. Technologies and management techniques to increase output in a sustainable way while simultaneously supplying more nutrient-dense food are urgently needed by wheat farming and wheat-based food industries to fulfill the demands of a growing global population [18]. The supply chain for wheat is controlled by a small number of nations in North America and Europe [19]. Wheat has been greatly affected by the COVID-19 outbreak because crop harvesting and lockdown disrupted the supply chain and prices [20]. Wheat cultivation has a significant role in the sustainability of the wheat supply chain [21]; therefore, for its success, the supply chain must be committed to and involved in sustainable collective innovations [22].

Careful selection of suppliers can reduce procurement costs, improve supply quality and reliability, and increase the company’s profit margins by lowering upstream supply chain risk. To choose the best suppliers, a decision-maker must compromise between tangible and intangible criteria [23]. Companies must find and optimize strategic supplier networks to select cooperative networks and boost supply network competence [24]. Supplier selection is a difficult problem that involves several criteria. In any supply chain, choosing a supplier and making a purchase choice are crucial because they present opportunities to save expenses and boost revenues [25]. Industry 4.0 and digitalization are making it increasingly necessary for managers to make judgments on their suppliers quickly and accurately. MCDM approaches are one of the various decision-support technologies available to managers [26]. Creating an integrated supply chain for wheat-based products would involve both long-term decisions about choosing a supplier and setting up new silos and short-term decisions about how to distribute wheat and its products. Any SS model should choose the suppliers, decide on the quantity of imports, distribute wheat, and produce products from it [27]. An EBM assessment method can be utilized to evaluate the production efficiency of companies on a micro level [28]. Further, utilizing data analytics and IoT as a component of meat and poultry farm green supply chain inventory optimization to evaluate and identify obstacles that must be subjugated through essential suppliers’ collaborations can also be considered [29]. Moreover, introducing a two-stage, multi-criteria supplier selection model for an uncertain automotive SC can also be contemplated. The MCDM approach blends the gray complex proportional assessment and the spherical fuzzy analytical hierarchical process [30]. Another novel MCDM that can be utilized to select the best suppliers is the BCM. It is used to solve the problem of an incomplete pairwise comparison matrix and calculate the missing comparison values. It has been proven that the new techniques are successful in identifying the best alternative solutions [31,32].

Solutions for complex systems can be accomplished using the multi-criteria optimization and compromise solution (VIKOR) approach. When there are competing criteria, this strategy focuses on ranking and selecting from a group of choices. The fuzzy-VIKOR algorithm was developed using fuzzy operations and methods to rank fuzzy numbers. Resolving the issue of evaluating and selecting potential suppliers has recently emerged as a crucial strategic consideration for corporate organizations. The VIKOR technique was created to address challenges in MCDM with competing and incommensurable criteria. It is used to collect the opinions of decision-makers in the form of linguistic terms for SS as a group MCDM problem [33]. To address the issues with SS, researchers developed a hierarchical MCDM model based on fuzzy set theory and the VIKOR method [34]. It chooses the best supplier using a model that combines fuzzy-VIKOR with an artificial neural network [35], adjusts the VIKOR approach for intuitionistic fuzzy data for supplier evaluation and selection while including both the subjective and objective weights of the criterion [36], and uses fuzzy-VIKOR to evaluate and choose suppliers while taking both broad and resilient factors into account [37].

Jordanian businesses should concentrate on supply chain procedures by choosing suppliers based on quality, allowing two-way communication of grievances and ideas to improve product quality, and involving suppliers in planning and developing new goods [38]. Successful supply chain performance must be implemented and maintained, coupled with appropriate coordination and information exchange through the various stages of the value chain [39]. Strategic relationships with suppliers have the biggest impact [40]. Knowledge management mediates the ties among suppliers, technological innovation (TI), and customers [41]. Jordanian businesses’ supply chain flexibility must be improved because it has a significant impact on customer satisfaction [42].

SCs should constantly use technology to survive conflicts and competitions. Supplier diversification is a crucial element, and the pandemic and government actions are likely to usher in a new era of SC localization and regionalization. Increasing SC visibility and automation require network agility and partners’ integration [43]. The MENA supply chain is susceptible to numerous disruptions and instabilities that result in unexpected interferences with decisions and make the SC uneasy. Nervousness decreases effectiveness and has a detrimental effect on SC performance. Stress has a significant negative impact on the stability and resilience of the supply chain, thus raising prices and changing the relationships between suppliers and consumers [44]. The Middle East has long served as a hub for international trade, promoting economic expansion and stimulating the diversification of new markets. The Middle East is not risk-free. Because of its location near sanctioned nations and its centuries-old commercial ties, supply chain actors have had to manage an expanding spectrum of environmental, legal, regulatory, and geopolitical risks as an indispensable part of doing business [45]. The biggest hurdles in the Middle Eastern SC are culture, regulatory environment, lack of government backing, and top management [46]. MENA supply chain workers rely heavily on the do-it-yourself strategy, which deviates from global trends and appears to have a detrimental influence on service levels, competitive advantage, and profitability [47].

Approximately 37% of the calories consumed in the MENA region are from wheat. The MENA region is the world’s largest net importer of wheat [6]. The organization of the industry is crucial for research on food security in the region and how countries maintain a sustainable supply of wheat because of the region’s substantial reliance on imported wheat [7]. As the majority of Jordan’s wheat and barley are imported via the Black Sea, the Russian War in Ukraine directly affected Jordan. In 2022–2023, Jordan was projected to produce 30,000 tons of wheat, which is less than the two-week supply of the nation’s anticipated 960,000 tons of yearly consumption [8]. 34% of Jordan’s total wheat supply from both domestic production and imports was lost or squandered, costing the nation approximately USD 105 million annually and contributing to significant losses in natural resources [9]. Grain prices will rise further because of Russia’s decision to leave the Black Sea Grain Accord and the global oil crisis. Wheat prices reached an all-time high after the Russia-Ukraine War. Every month, the kingdom consumes 90,000 tons of wheat. Jordan imports nearly 95% of the strategic grains it requires [10]. Fueled by fear of war, Jordan’s wheat imports in 2022/2023 are expected to reach 1.3 million tons [11].

Jordan faces many difficulties, including rapid population expansion and heavy urbanization. Owing to Jordan’s strategic location, environmental factors, including water scarcity and low soil quality, are harsh realities that cannot be changed. The nation is experiencing a severe water deficit because of population pressure, which has accelerated urbanization. Despite the increasing need for food, agriculture is not a promising alternative [12]. Numerous factors affect the choice of supplies, such as the cost of the product in issue, the number of producers, the cost of inputs, technological advancements, the cost of alternative products, and erratic variables such as weather. Although agriculture does not ensure food security in Jordan, it makes the process exorbitant and ultimately unsustainable. To the best of our knowledge, there are no studies concerned with ranking or selecting the best wheat suppliers in Jordan or the MENA region. Therefore, this study seeks to address this gap based on the current situation. Additionally, this study seeks to identify alternatives to wheat suppliers based on approved standards, especially because the government is responsible for importing, storing, and managing wheat, which is considered a strategic commodity for food in Jordan.

3. Methodology and Solution Approach

As shown in Figure 1, the research methodology consists of four main parts. The first part includes the review and collection of data and information through previous studies, official data on the websites of various official agencies, personal interviews, and the inputs of the expert teams formed for evaluating providers. The second part deals with the development of the framework for the selection of suppliers and consists of the identification of suppliers and the definition of general criteria, guidelines, stages, steps, and pillars of the SS process. The third part represents the application of the proposed method to select the best provider and arrange the suppliers according to priority. It contains the definition of MCDM and the proposed fuzzy-VIKOR method and reports the steps and equations accompanying each step. The last section contains a description of the case study, which represents the selection of the best wheat suppliers to Jordan through the application of figures and the analysis of results. In all these stages, there was input from a team of experts who contributed to identifying the main suppliers, determining the criteria, and conducting evaluations of the suppliers, based on which the necessary calculations were performed and the best suppliers were selected.

3.1. Suppliers’ Selection Approach (SSA)

The SSA is a comprehensive framework that can be utilized as a guide to effectively select suppliers of wheat, other goods, products, or materials, and reduce the risks associated with the SS process. The supplier selection process is a great tool for developing productive working relationships with vendors, in addition to helping organizations find the lowest-priced products. Apart from the main issue of basing selection on cost, there are other considerations as well, such as quality, reliability, punctuality, adaptability, safety, ethical principles, and environmental impact. Provider evaluations can be divided into two categories: quantitative and qualitative. Location, financial standing, facilities and capacities, technological capability, and quality standards were all considered quantitative, whereas on-time delivery of goods, data sharing, and communication were considered qualitative.

Figure 2 shows the import procedures and selection of the main wheat supplier, which include five main stages: Establishing requirements, defining the selection criteria, identifying possible suppliers, evaluation and selection, and implementation and monitoring. Each stage contains a set of factors, procedures, and steps that govern or contribute to effective provider selection. Four main pillars must be considered when choosing suppliers for a critical product that impacts society: sourcing, planning, strategy, and development; supplier/contractor management; storage capacity and economy; and SC capability management. Sourcing is the process of evaluating, choosing, and managing suppliers to obtain the required products and services. As the name implies, sourcing is concerned with developing sources through which an organization can obtain its goods. Supply planning is the entire planning process that includes establishing the specifications for the product and vendor and then creating an RFI, RFQ, or RFP to invite bids. The plan should include a procedure for finding, assessing, and working with suppliers. A significant portion of an organization’s financial resources is expended during the SS process, which is essential for the success of any organization. A good plan should contemplate distribution and procurement operations according to demand forecasts while considering capacity constraints and product availability. Important strategies to reduce uncertainty include SS techniques in terms of technology, quality, cost, and delivery performance. The SS process includes finding, assessing, and working with suppliers. Supplier management uses enormous economic resources and plays an essential role in ensuring the success of contractor management. Storage capacity and the ability to manage inventory within a specified period must be considered as economic determinants. SC capability describes the level of inter-organizational activities between a customer and its supplier while reacting to social concerns.

Supplementary selection steps should include analysis of the needs, collection of data, defining the criticality of products, building strategy, defining SS criteria, listing potential suppliers, reviewing RFPs, launching tenders, analyzing suppliers’ capabilities, evaluating the offers, performing the financial analysis, contract awarding, contract implementation, setup of the invoices, and monitoring performance. SS criteria include price, quality, delivery, lead time, responsiveness, capability, and capacity. One of the main stages of SS is the identification of potential suppliers by utilizing existing networks, online searches, tradeshows/exhibitions, referrals, and networking.

The main guidelines for selecting suppliers include planning, looking beyond price, considering the benefits, utilizing technology, and being practical and reasonable. Many factors must be considered when selecting a reliable supplier. Organizations must consider the track records of their performances, client satisfaction, costs, delivery, exchange rate, and other factors. Organizations should plan proactively and review their requirements and goals before beginning the SS process. One should also consider costs, the variety of services provided, the quality of goods, the delivery schedule, and accessibility while assessing vendors. Moreover, technology has simplified tracking and interaction with potential vendors. There are many Internet resources that help accomplish search and analysis. One should not count on obtaining all requirements from a single vendor and should preferably combine multiple goods and services from diverse suppliers.

The five stages of the supplier selection approach are described as follows:

3.1.1. Establishing the Requirements

Specifying these requirements is the first stage of the SS procedure. This entails being fully aware of what the providers are required to provide. The SS plan is a proactive process that involves organizing resources to balance supply and demand while optimizing the flow of products, money, and information. This plan determines the inventory levels and daily distribution of wheat. It estimates the annual demand and allocates the necessary inventory. Additionally, any plan should be flexible enough to change as the area experiences crises, such as an increase in the number of refugees, which increases the demand for wheat. The plan outlines the procedures, systems, and communication strategies employed. Any source strategy should include risks associated with suppliers, such as low quality, delivery delays, supply interruptions, supplier failures, and technology risks, which can be regarded as the most pertinent sub-criteria in SS. A sourcing strategy should be in place to assess and select suppliers who fulfill these requirements.

Reducing purchase risk, increasing buyers’ total value, and fostering intimate and long-lasting relationships between buyers and suppliers are the primary goals of SS. The process of identifying and assessing needs is included in the definition of needs analysis. This is the first step that should be considered to successfully create an effective strategy. Needs analysis enables companies to proactively address possible problems before they materialize. Risk management is becoming a crucial issue in supplier choices. Assessing supplier risk can help avoid detrimental effects on availability. The majority of the risks that could affect SS can be divided into four major groups: economic, environmental, political, and ethical. Product criticality is something whose supply is uncertain and for which there is no simple replacement. Because of the rising dangers associated with wheat supply and demand, wheat criticality has received attention on a global scale. Wheat inventory management is one of the most important factors in determining the required quantities, storage places, storage conditions, dates, and timing of demand. Because wheat is a strategic product that is imported and managed by the government, the quantities of stocks and storage conditions are important factors, so there is no stock-out at any moment. Supplier constraints allow suppliers to let the purchasing organization know how much they can supply. A cost limitation indicates a limited budget, whereas a time constraint indicates a deadline for completing the procurement process. Constrained management is essential for successful selection because most supplier limitations impact each other.

3.1.2. Defining the Selection Criteria

The selected criteria determine whether a supplier satisfies the specified requirements. To make an effective selection process, each criterion must be precisely specified, and the weighting for each selection criterion must be determined. Provider selection is based on a wide range of factors. These include costs, value for money, quality, delivery, reliability, lead time, responsiveness, flexibility, capability, communication, origin, and reliability. While some bids may use all these factors, others may only use a few of them. Although each of these factors may be significant, some are noteworthy. For example, the quality may be crucial. If costs are important, providers may offer poor quality and long lead times.

The conditions and specifications for the purchase of wheat for Jordan are listed in the tenders offered by MITS for the purchase of wheat [12]. The full wheat import procedure by seaports is stated in Jordanian customs [48], and there are some studies regarding the strategy for food security [49]. The specifications and conditions of the tenders for the purchase of wheat in Jordan depend on the specifications set by the MITS. Previous bids and suppliers are also considered in terms of compliance with the supply and quality specifications of imported or locally produced wheat. The amount of wheat stock or the quantity to be purchased is determined by the government based on strategic plans and national food security.

At this stage, the terms and conditions, product characteristics, and lessons learned from previous contracts should be determined by government authorities. In this study, among the many wheat characteristics mentioned earlier, the following seven criteria (quality, costs, delivery, flexibility, communication, origin, and reliability) have been identified by specialists as being crucial to evaluating the main wheat supplier to Jordan.

3.1.3. Identifying Possible Suppliers

The group from which a supplier has to be chosen must be established after the selection criteria are in place. The following should be considered throughout this stage as sources for locating, researching, and contacting potential vendors: current suppliers, previous suppliers, competitors, industry associations, recommendations, previous business connections, and the Internet. There are several methods to identify possible suppliers, such as utilizing current networks, online searches, recommendations from respectful people, networking with other businesses, and checking trade directories.

Supplier qualification can be viewed as a risk analysis technique. It should provide the right amount of assurance that suppliers, vendors, and contractors can deliver products, components, and services of consistently high quality while adhering to legal standards. Supplier qualifications include the ability to provide high-quality products or services that comply with all requirements, at fair prices, and conditions. Priorities and strategies determine how to prioritize these factors. As part of a supplier’s qualifications, you should regularly audit your suppliers. Having competent suppliers can help guarantee output quality.

Supplier capabilities represent the way suppliers interact with a buyer’s operations by providing significant inputs regarding the purchase of goods. Supplier capability evaluation includes determining the critical supplier evaluation criteria for price, quality, technology, regulatory compliance, economic viability, and stability. To anticipate capacity disruptions, it is necessary to gather supplier intelligence at the company, product, part, and process levels. Choose suppliers who share a commitment to sustainability and a shared commitment to support each other’s long-term goals. A supplier’s strategic commitment to a buyer has a significant impact on performance. Good suppliers are usually characterized by reliability, operational and technical capabilities, stability, and ease of communication at every level. It is necessary to have a long-term relationship, be financially stable, adhere to a total-quality performance philosophy, and carefully prepare and implement strategies. Success in this novel and distinctive medium necessitates a multifaceted, multidirectional strategy.

The supplier’s physical ability to fulfill needs as promised is a key factor in award determination. Reliable suppliers should be characterized by the capacity to deliver frequently and on time, small exact quantities, sharing data, showing continuous improvements, and partnerships with simple and open communications at all levels. Suppliers’ operational, technological, and technical proficiencies should also be assessed as significant skills. Verify whether a potential provider has the skills, resources, and tools required to meet their needs. This can be discovered by examining past performance data and active participation in industrial events. Financial analysis is frequently necessary to satisfy audit compliance requirements and aids in the assessment of overall supply-based risk concerns. Based on their financial stability, leverage, and competitive advantage, financial ratios assist suppliers in choosing and qualifying. Communication is important, particularly when issues develop. During this period, ensure that the supplier provides adequate feedback. The finest suppliers also go out of their way to communicate frequently and learn how to provide better services.

3.1.4. Evaluation and Selection

Every buyer should use an evaluation procedure before choosing an offer to ensure that all organizational needs are considered and optimized. Examining a supplier’s offer entails not only assessing its components but also determining whether the seller has the capacity to speedily deliver the desired quality. One should consider both the benefits and potential risks while evaluating the proposals. Before choosing and contracting a supplier, it is necessary to evaluate three important factors: responsiveness, capacity, and competitive value.

The process of evaluating a new or existing supplier is based on delivery, price, output, management level, technical expertise, and service. For both current and potential suppliers, a common methodology for suppliers’ evaluation must be applied in every situation. A framework for evaluating suppliers can assist in creating a benchmark and formulating strategies for remedial actions for current providers. The quality of the purchased wheat should be the primary consideration when choosing a provider. Because product quality can directly impact society, it should continuously satisfy set specifications. Additionally, suppliers’ attributes, such as delivery lead times, should be carefully considered. The product’s unit price, payment terms, cash discounts, ordering and carrying costs, logistics and maintenance expenses, and other qualitative factors that could be challenging to calculate should all be included in the total cost. The discussions should commence only when the list has been reduced to a manageable number of best possibilities, potentially just one supplier. One might simply negotiate with the top supplier based on bidding, even when others are still on the list of potential suppliers, depending on the essential good or service. Of course, until the conversations and agreements are finalized, the other parties are not informed that they are not number one. Lawyers may also be involved, depending on the complexity of the matter.

The evaluation and selection process should include ranking systems, mathematical modeling, contract creation, potential risks and benefits, responsiveness, capability, and competitive value. Shortlist the potential providers identified using the data collected during the preliminary research. Choosing the best provider is the final step in the SS process. The selection of the best supplier depends on various factors, including negotiation results, prequalification questions, and request for quote (RFQ) requirements. Additionally, as proof of its capacity to deliver that product, the supplier is required to present information on its capabilities, capacity, third-party certifications, and so on. The selected vendors are subsequently added to the organization’s list of authorized suppliers. Supplier selection is a multicriteria challenge that considers both qualitative and quantitative variables. These concrete and intangible characteristics, some of which may contradict each other, must be traded to choose the best suppliers. Buyers usually evaluate and confirm their ranking system before the bidding process. Mathematical modeling with decision support systems is an essential part of the evaluation and selection process for final suppliers.

When selecting the main supplier, risks such as non-delivery time, quality, and uncontrolled changes, the speed of the provider’s reaction to these changes, and the mechanism for dealing with disruptions are all considered. The supplier’s ability to supply the full quantities required within written timings and standards is most critical. One of the most important evaluation methods relates to the competitiveness of the provider in terms of quality, price, and delivery. After selecting the best provider, the process of drafting and signing the agreement, which includes legal, contractual, technical, financial, operational, and other conditions, is usually carried out by lawyers.

3.1.5. Implementation and Monitoring

The follow-up and control of supply from the contractor must include implementation plans, compliance measures, monitoring performance, risk assessment and mitigation, award proposals, transition plans, contract considerations, communication, tracking value, and key performance indicators and objectives. Throughout the SS process, performance must be monitored. Monitoring ensures that you receive the finest-quality service and value for money. To deploy new products, it is crucial to collaborate with new suppliers using communication strategies. The potential hazards of this relationship should be determined and reduced with adequate compliance controls. According to the aforementioned process, the most important risk variables are low quality, delivery delays, supply interruptions, supplier failures, and technological risks. Subsequently, a contract should be signed to formally bind the agreements. Modern technology can automatically construct contracts using data that has already been entered into the system and from earlier RFPs to guarantee supplier information accuracy. Key performance indicators and goals should be established and followed to hold suppliers accountable. Once a provider is selected, an invoicing procedure must be established. Any new supplier contract offers an opportunity to argue for invoice automation, especially if the new arrangement generates many invoices. Ongoing supplier performance tracking should include obtaining timely product deliveries, superior items, outstanding client service, the right goods, and the legitimate amount listed on the invoice. To demonstrate to the larger business how these cost-saving initiatives made possible by procurement have improved the bottom line, one should keep track of the savings achieved with the new supplier. Even under the best circumstances, purchasing is a challenging task. Organizations should be able to skillfully negotiate market shifts, supply constraints, and changing needs on an ongoing basis if a solid procedure is in place.

The proposed SS approach can be used to build a network that is stable, strong, and aligned with consumption and goals, and it can help prevent the usual mistakes committed while choosing suppliers. The SSA considers the dynamic nature of the market and potential external and internal changes. Therefore, it can prove beneficial by increasing the stability, adaptability, reliability, and robustness of supplier selection. In addition, the framework accounts for the planning and management of suppliers, implementation, and risks and serves as a quick tool for the evaluation and selection process. Establishing appropriate initial selection criteria and ensuring that the right supplier is selected are the primary steps in effective sourcing management. The successful establishment of a supplier selection system requires time and effort. However, if you are successful in putting it into practice, it will undoubtedly be very beneficial and will increase stability. Inadequate planning and effort frequently lead to predictable risks in this process, such as the selection of an incorrect supplier or subpar supplier performance. Therefore, organizations should thoroughly comprehend the procedures for SS and use them effectively.

3.2. Fuzzy VIKOR Approach

The VIKOR approach, which stands for “VlseKriterijumska Optimizacija I Kompromisno Resenje” (multi-criteria optimization and compromise-solution), was created by Opricovic in 1998 [50]. The VIKOR approach is either an MCDM or MCDA method. Assuming that a compromise is acceptable for conflict resolution, the decision-maker wants a solution that is as close to the ideal as possible, and the alternatives are evaluated in accordance with all established criteria. It was initially developed to resolve decision problems with conflicting criteria. The compromise option closest to the ideal is identified by VIKOR after ranking the alternatives [50]. VIKOR is used in different disciplines, such as engineering, business, environment, and supply chains, and has dozens of applications in different, complex, and interrelated topics, including SS, management decisions, partner selection, renewable energy, airport strategies, risk assessment, commerce, and various aspects of life. In general, the VIKOR is used in problems involving multiple-criteria evaluation and the selection of alternatives.

The fuzzy set concept presented by Zadeh [51] addresses issues in which a source of ambiguity exists. A regular number is expanded using a fuzzy number. This refers to a connected group of potential values rather than a single specific value [51]. To solve problems in a fuzzy environment, where both criteria and weights can be fuzzy sets, a fuzzy-VIKOR (F-VIKOR) approach was created. Triangular fuzzy numbers (TFN) were employed to manage the erroneous numerical values. The foundation of the F-VIKOR is the fuzzy merit aggregate, which shows how far an imperfect solution is from an ideal solution. The F-VIKOR algorithm was developed using fuzzy operations and methods to rank fuzzy numbers. Linguistic variables are phrases or clauses in a language, whether natural or artificial. The values include language variables rather than numerical variables. Fuzzy logic specifically handles linguistic variables. The following F-VIKOR steps were utilized in this study and adopted from [50,52,53,54,55].

• Steps for Fuzzy-VIKOR

Step 1: Define the attributes. Organize the decision-making team while outlining a list of pertinent factors. Before establishing evaluation scales to evaluate concepts, the selection criteria for concept designs must be determined. Identify the objectives of the decision-making process, create a list of feasible alternatives, find the evaluation criteria, and constitute a group of decision-makers. Suppose there are $K$ decision-makers $(D_t,t=1,2,\dots ,K)$, who are responsible for assessing $m$ alternatives $(A_i,i=1,2,\dots ,m)$, with respect to the importance of each $n$ criterion ($C_j,j=1,2,\dots ,n$).

Step 2: Identify the appropriate linguistic variables. Select the required linguistic variables and their positive triangular fuzzy values. The important weights of the criteria and evaluations of alternatives regarding various criteria should be computed using linguistic variables. A triangular fuzzy number (6, 7, 8), for instance, can be used to define the linguistic variable “very strongly important (Vs)”. To describe these linguistic variables as positive triangular fuzzy numbers, one must first specify the linguistic variables for the significant weights of the criteria and the fuzzy rates for the alternatives in relation to each criterion.

Step 3: The fuzzy importance weights of the evaluation criteria are determined. Many interpretations of the evaluation criteria cannot be assigned equal weight. Based on the assumption that there are $k$ specialists in the evaluation group, this study uses an average approach to integrate the opinions of numerous evaluators. The fuzzy importance weight ${\tilde{\mathrm{w}}}_{\mathrm{i}}$ of criterion $C_j$ will be as expressed in Equation (1) [52].

$${\tilde{w}}_i=\frac{1}{k}\left({\tilde{w}}_i^1+{\tilde{w}}_i^2+i{\tilde{w}}_i^k\right),$$

(1)

where $w_i^k$ is the criteria $C_j$’ is the fuzzy importance weight of criterion $C_j$, as determined by the k^th evaluator. Additionally, ${\tilde{w}}_i^k=(l_i^k+m_i^k+u_i^k)$.

Step 4: Creating a performance rating matrix. To obtain the aggregated fuzzy weighting of the alternatives, the decision-maker must be consulted to generate the fuzzy decision matrix $\tilde{D}$. Let $k$ be the total number of decision-makers in the group, and let ${\tilde{\mathrm{w}}}_{\mathrm{i}}$ be the total fuzzy weights for each criterion. The formula for $C_j$ is shown in step 3, and the combined fuzzy ratings can be calculated using the following equation. To calculate the number of possibilities for each criterion, a fuzzy decision matrix is created by analyzing the judgments of the decision-makers to obtain the aggregated fuzzy weights of the criteria and alternatives. The following is a formal matrix description of a typical fuzzy MCDM problem: Assume there are $m$ options for $k$ evaluators to assess in light of $n$ criteria. In the supplier selection problem, the value of the aggregated weights is expressed in a matrix format, as shown in Equation (2).

$$\tilde{D}=\begin{array}{c}A\\ A_2\\ ⋮\\ A_m\end{array}\left[\begin{array}{cccc}{\tilde{x}}_{11}& {\tilde{x}}_{12}& \dots & {\tilde{x}}_{1n}\\ {\tilde{x}}_{21}& {\tilde{x}}_{22}& \dots & {\tilde{x}}_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ {\tilde{x}}_{m1}& {\tilde{x}}_{m2}& \dots & {\tilde{x}}_{mn}\end{array}\right],i=1,2,\dots ,m;j=1,2,\dots ,n$$

(2)

where $x_{ij}$ is a linguistic variable represented by the triangular fuzzy number (TFN) rating of alternative $A_i$, $i=1,2,\dots ,\mathrm{m}$ with respect to criterion $C_j$ ($\mathrm{j}=1,2,\dots ,n$).

The performance of the feasible alternatives should be assessed or ranked by evaluators to express their preferences because the evaluators’ preferences for alternatives vary based on their personal experiences, cultural backgrounds, value systems, and educational backgrounds. This study uses the averaging approach to generate the fuzzy performance value $x_{ij}$ for $k$ evaluators, considering the same criterion, $C_j$ [52], as represented by Equation (3).

$${\tilde{x}}_{ij}=\frac{1}{k}\left({\tilde{x}}_{ij}^1+{\tilde{x}}_{ij}^2+j4{\tilde{x}}_{ij}^k\right),$$

(3)

where $x_{ij}^k$ is the performance rating for alternative $A_j$ with respect to the criterion $C_i$ evaluated by k^th expert, and ${\tilde{x}}_{ij}^k=(l_{ij}^k+m_{ij}^k+u_{ij}^k)$.

Step 5: Determine the best and worst fuzzy values for each criterion. The fuzzy best value $f_i^{*}$ = $(l_i^{*},m_i^{*},u_i^{*})$, and worst value $f_i^{-}$ = $(l_i^{-},m_i^{-},u_i^{-})$, can be determined using the relations [42] depicted in Equations (4) and (5).

$${\tilde{f}}_i^{*}=\underset{j}{\mathit{max}}\left({\tilde{x}}_{ij}\right),{\tilde{f}}_i^{-}=\underset{j}{\mathit{min}}\left({\tilde{x}}_{ij}\right),\mathrm{for}ifo$$

(4)

$${\tilde{f}}_i^{*}=\underset{j}{\mathit{min}}\left({\tilde{x}}_{ij}\right),{\tilde{f}}_i^{-}=\underset{j}{\mathit{max}}\left({\tilde{x}}_{ij}\right),\mathrm{for}ifo$$

(5)

where B and C are sets of beneficial and non-beneficial criteria, respectively.

The fuzzy difference ${\tilde{d}}_{ij}$ between ${\tilde{x}}_{ij}$ and the fuzzy worst value ${\tilde{f}}_i^{-}$ (or the fuzzy best value ${\tilde{f}}_i^{*}$) can be calculated using Equations (6) and (7) [53].

$${\tilde{d}}_{ij}=\frac{{\tilde{f}}_i^{*}-{\tilde{x}}_{ij}}{u_i^{*}-l_i^{-}},\mathrm{for}ifo$$

(6)

$${\tilde{d}}_{ij}=\frac{{\tilde{x}}_{ij}-{\tilde{f}}_i^{*}}{u_i^{-}-l_i^{*}},\mathrm{for}i\in C.$$

(7)

Step 6: Compute the values ${\tilde{S}}_j$ and ${\tilde{R}}_j$. This stage involves measuring the distances between alternative $A\left(j\right)$ and the fuzzy best value ${\tilde{f}}_i^{*}$ and the fuzzy worst value ${\tilde{f}}_i^{-}$, respectively. These values were assessed using the relationships [53] depicted through Equations (8) and (9).

$${\tilde{S}}_j={\sum }_{i=1}^n\left({\tilde{w}}_i(=){\tilde{d}}_{ij}\right),$$

(8)

$${\tilde{R}}_j=\underset{i}{\mathit{max}}\left({\tilde{w}}_i\left(\times \right){\tilde{d}}_{ij}\right),$$

(9)

where ${\tilde{S}}_j=(S_j^l,S_j^m,S_j^u)$, ${\tilde{R}}_j=\left(R_j^l,R_j^m,R_j^u\right),$ and ${\tilde{\mathrm{w}}}_{\mathrm{i}}$ is the importance weight for the criterion $C_j$.

Step 7: Compute the value of ${\tilde{Q}}_j$. ${\tilde{Q}}_j=(Q_j^l,Q_j^m,Q_j^u)$, can be calculated using Equation (10) [53].

$${\tilde{Q}}_j=v({\tilde{S}}_j(-){\tilde{S}}^{*})/(S^{-/}-/S^{*/})(+)(1\text{-}v)({\tilde{R}}_j(-){\tilde{R}}^{*})/(R^{-/}-/R^{*/}),$$

(10)

where ${\tilde{S}}^{\mathrm{*}}={Min}_j{\tilde{S}}_j,S^{-u}={Max}_j{S}_j^u,{\tilde{R}}^{\mathrm{*}}={Min}_j{\tilde{R}}_j,R^{-u}={Max}_j{R}_j^u$, $v$ is the weight for “the majority of the criteria” when making a decision, and (1-$v$) is the weight for individual regret, $vϵ\{0,1\}$ with $v$ as typically equal to 0.5 [44]. As $v$ is a weight of the strategy of maximum group utility, the compromise can be selected with “voting by majority” ($v>0.5$), or “consensus” $(v=0.5)$, or “veto” ($v<0.5$).

Step 8: Defuzzify the values ${\tilde{Q}}_j,{\tilde{S}}_j,and{\tilde{R}}_j,(j=1,2,\dots ,j)$ using the following Equation (11) [53]:

$$crisp\left(\tilde{N}\right)=(u+2m+l)/4$$

(11)

Step 9: Rank the alternatives by sorting the S, R, and Q values in ascending order.

The alternatives are ranked by sorting the crisp values in ascending order. The results were as follows: ${\left\{A\right\}}_S,{\left\{A\right\}}_R,and{\left\{A\right\}}_Q$ according to $crisp\left(S\right)$, $crisp\left(R\right)$, and $crisp\left(Q\right)$, respectively. We ranked the options by arranging the crisp values of S, R, and Q in ascending order. In other words, the Q value of an option decreases as it improves.

Step 10: Select the best alternative as the compromise solution. This step suggests a compromise solution, where alternative ($A^{\left(1\right)}$) is considered to be the best with the minimum $Q$ value if the next two conditions are satisfied [53].

If the following two conditions are satisfied simultaneously, the scheme with the minimum value of Q in the ranking is considered the optimal compromise solution.

C1: Acceptable benefit, wherein alternative $Q\left(A^{\left(1\right)}\right)$ has an acceptable advantage as presented by Equation (12)

$$\mathrm{Q}\left(A^{\left(2\right)}\right)-\mathrm{Q}\left(A^{\left(1\right)}\right)\ge DQ,$$

(12)

where $DQ$ = $1/(m-1)$, $m$ is the number of alternatives and $A^{\left(2\right)}$ is an alternative with second position in the ranking list.

C2: Reasonable decision-making stability, wherein alternative $Q\left(A^{\left(1\right)}\right)$ is stable within the decision-making process; in other words, it holds the top rank in $S_i$ (and/or) $R_i$.

If condition C1 is not satisfied, that means Q ($A^{\left(m\right)}$) − Q ($A^{\left(1\right)}$) $\le DQ,$ then all the alternatives $A^{\left(1\right)}$, $A^{\left(2\right)}$, …, $A^{\left(m\right)}$ are the compromise solution. There is no comparative advantage of $A^{\left(1\right)}$ over others, but in the case of the maximum value, the corresponding alternative is the compromise (closeness) solution. If Condition C2 is not satisfied, the stability in decision making is deficient, whereas $A^{\left(1\right)}$ has a comparative advantage. Therefore, $A^{\left(1\right)}$ and $A^{\left(2\right)}$ are the compromised solutions.

4. Case Study Analysis and Discussion

Like the rest of the Middle East, Jordan relies heavily on wheat in its daily diet. Wheat, flour, and bread are essential items on daily Jordanian tables. The Hauran Plains in northern Jordan and southern Syria are considered the breadbaskets of the ancient Roman Empire because of their prolific wheat production. Jordan had a surplus of wheat production until the end of the 1960s of the last century. Jordan remained between imports and exports until 1989. According to the Jordanian Ministry of Industry and Trade, Jordan currently imports approximately 95% of its wheat needs, amounting to approximately 1,100,000 tons per year. Jordan imports wheat from different countries, and sometimes wheat is supplied to Jordan in the form of aid, especially from the United States. The MITS issues an annual tender for the purchase of wheat at the port of Aqaba, carriage and insurance paid (CIP). The company that wins the tender supplies wheat according to specifications approved by the MITS, MOA, and JSMO. Thus, the source of wheat depends on the variety, quality, purchase prices from the source, transportation fees, loading and unloading costs, and other factors such as delivery periods. Wheat production is seasonal, and storage capacities are usually limited or just suitable for varying periods. The price of wheat varies globally according to the type and approved quality standards. Wheat is typically classified as hard or soft.

To increase their ability to compete in the global market, all SC parties concerned must endeavor to bring down the cost of logistics. Therefore, it is essential to create superior agricultural and food logistics [14]. For wheat SS for Jordan, we should take into consideration that several countries in North America and Europe dominate the supply chain for wheat [19]. COVID-19 has had a deleterious effect on wheat since it delayed crop harvesting and the lockdown impacted the supply chain and pricing [20]. The capacity of the wheat supply chain to remain viable is significantly impacted by the growing of wheat [21]. Therefore, the wheat supply chain must be dedicated to and participate in sustainable collaborative innovations [22]. All these factors should be taken into account when analyzing and selecting the main supplier or ranking suppliers for wheat. Moreover, expected and unexpected changes and risks may affect the future identification and selection of the main wheat suppliers to Jordan.

The VIKOR-MCDM-making approach was utilized to evaluate and select the top five wheat suppliers in Jordan. By utilizing linguistic assessment, which converts VIKOR to fuzzy numbers and refers to them as fuzzy-VIKOR factors, the supplier ratings were derived. The following steps summarize the numerical assessment of the case based on the F-VIKOR steps described in the previous section.

Step 1: The main objective of this study was to identify the main supplier of wheat in line with the available alternatives and approved wheat standards, specifications, and classifications. A team of twelve experts (K = 12) consisting of specialists, academicians, and logisticians was formed to define the attributes. Based on the available information, the experts identified five main countries to import from: Russia, Romania, Ukraine, Australia, and Syria. Although dozens of countries produce and export wheat, experts chose these countries for various reasons. Russia, from which imports have been made in recent years, is one of the largest global exporters of wheat. In recent years, Romania has become the main wheat supplier to Jordan and Ukraine, and Jordan has imported varying quantities of wheat from it in previous years. Australia, as Jordan had previously imported from it. In addition to them, Syria was also considered because it is a neighboring country and, therefore, the transportation costs are low compared to other sources.

The criteria approved by the team of specialists included the quality, expenses (price and costs), delivery (time, place, and amount), origin (source country), flexibility, communication, and reliability/solvency of the importer. The quality, specifications, and classifications set by the Ministry of Trade, Industry, and Supply, the Jordan Standards and Metrology Organization, and the Ministry of Agriculture include type, producer, percentage of moisture, percentage of harmful substances, and many other specifications determined by the concerned parties. Figure 3 below shows the main features of wheat imports to Jordan, including the goals, criteria, and alternatives. The F-VIKOR technique was used to identify the main wheat suppliers to Jordan and prioritize them based on the identified attributes.

Step 2:

Table 1. Linguistic scales and their corresponding TFNs.

Importance of Criteria			Ratings of Alternatives
Linguistic Variables	Abbr.	Corresponding TFNs	Linguistic Variables	Abbr.	Corresponding TFNs
Very low (VL)	VL	(0.0, 0.0, 0.1)	Very poor	VP	(0.0, 0.1, 0.2)
Low (L)	L	(0.0, 0.1, 0.2)	Poor	P	(0.1, 0.2, 0.3)
Medium low (ML)	ML	(0.2, 0.3, 0.4)	Medium poor	MP	(0.2, 0.35, 0.5)
Medium (M)	M	(0.4, 0.5, 0.6)	Fair	F	(0.4, 0.5, 0.6)
Medium high (MH)	MH	(0.6, 0.7, 0.8)	Good	G	(0.5, 0.65, 0.8)
High (H)	H	(0.7, 0.8, 0.9)	Very good	VG	(0.7, 0.8, 0.9)
Very high (VH)	VH	(0.8, 0.9, 1.0)	Excellent	E	(0.8, 0.9, 1.0)

shows the necessary linguistic elements and their triangular, fuzzy positive values. Using linguistic variables, the significant weights of the criteria and evaluations of alternatives based on numerous criteria were calculated. For example, an expert can describe the linguistic variable for the importance of criteria “medium high (MH)” as a triangular fuzzy number (0.6, 0.7, 0.8), and the rating for alternative “good (G)” as TFN (0.5, 0.65, 0.8).

Step 3:

Table 2. Fuzzy importance weights of the criteria.

Dt/Ci	Q	E	D	S	F	C	R
E1	VH	VH	H	H	VL	L	H
	(0.8, 0.9, 1)	(0.8, 0.9, 1)	(0.7, 0.8, 0.9)	(0.7, 0.8, 0.9)	(0, 0, 0.1)	(0, 0.1, 0.2)	(0.7, 0.8, 0.9)
E2	H	VH	H	MH	L	H	MH
	(0.7, 0.8, 0.9)	(0.8, 0.9, 1)	(0.7, 0.8, 0.9)	(0.6, 0.7, 0.8)	(0, 0.1, 0.2)	(0.7, 0.8, 0.9)	(0.6, 0.7, 0.8)
E3	VH	H	VH	MH	ML	MH	H
	(0.8, 0.9, 1)	(0.7, 0.8, 0.9)	(0.8, 0.9, 1)	(0.6, 0.7, 0.8)	(0.2, 0.3, 0.4)	(0.6, 0.7, 0.8)	(0.7, 0.8, 0.9)
E4	VH	MH	MH	VH	L	VL	VH
	(0.8, 0.9, 1)	(0.6, 0.7, 0.8)	(0.6, 0.7, 0.8)	(0.8, 0.9, 1)	(0, 0.1, 0.2)	(0, 0, 0.1)	(0.8, 0.9, 1)
E5	VH	VH	MH	VH	L	L	VH
	(0.8, 0.9, 1)	(0.8, 0.9, 1)	(0.6, 0.7, 0.8)	(0.8, 0.9, 1)	(0, 0.1, 0.2)	(0, 0.1, 0.2)	(0.8, 0.9, 1)
E6	H	VH	H	H	VL	L	H
	(0.7, 0.8, 0.9)	(0.8, 0.9, 1)	(0.7, 0.8, 0.9)	(0.7, 0.8, 0.9)	(0, 0, 0.1)	(0, 0.1, 0.2)	(0.7, 0.8, 0.9)
E7	VH	VH	H	H	VL	ML	H
	(0.8, 0.9, 1)	(0.8, 0.9, 1)	(0.7, 0.8, 0.9)	(0.7, 0.8, 0.9)	(0, 0, 0.1)	(0.2, 0.3, 0.4)	(0.7, 0.8, 0.9)
E8	VH	H	MH	MH	L	ML	VH
	(0.8, 0.9, 1)	(0.7, 0.8, 0.9)	(0.6, 0.7, 0.8)	(0.6, 0.7, 0.8)	(0, 0.1, 0.2)	(0.2, 0.3, 0.4)	(0.8, 0.9, 1)
E9	H	H	MH	H	ML	ML	MH
	(0.7, 0.8, 0.9)	(0.7, 0.8, 0.9)	(0.6, 0.7, 0.8)	(0.7, 0.8, 0.9)	(0.2, 0.3, 0.4)	(0.2, 0.3, 0.4)	(0.6, 0.7, 0.8)
E10	MH	VH	H	VH	ML	H	H
	(0.6, 0.7, 0.8)	(0.8, 0.9, 1)	(0.7, 0.8, 0.9)	(0.8, 0.9, 1)	(0.2, 0.3, 0.4)	(0.7, 0.8, 0.9)	(0.7, 0.8, 0.9)
E11	VH	VH	H	MH	L	VL	MH
	(0.8, 0.9, 1)	(0.8, 0.9, 1)	(0.7, 0.8, 0.9)	(0.6, 0.7, 0.8)	(0, 0.1, 0.2)	(0, 0, 0.1)	(0.6, 0.7, 0.8)
E12	VH	H	VH	H	VL	ML	MH
	(0.8, 0.9, 1)	(0.7, 0.8, 0.9)	(0.8, 0.9, 1)	(0.7, 0.8, 0.9)	(0, 0, 0.1)	(0.2, 0.3, 0.4)	(0.6, 0.7, 0.8)
${\tilde{\mathrm{w}}}_{\mathrm{i}}$	(0.76, 0.86, 0.96)	(0.75, 0.85, 0.95)	(0.68, 0.78, 0.88)	(0.69, 0.79, 0.89)	(0.05, 0.12, 0.22)	(0.23, 0.32, 0.42)	(0.69, 0.79, 0.89)

shows the fuzzy importance weights of the evaluation criteria. Based on the expert group evaluation, this study used the average approach to combine the views of many evaluators. The average criteria weights were computed using Equation (1).

Step 4: Analysis of makers’ assessments yields the combined fuzzy weights of the criteria and options, which are then used to form the fuzzy decision matrix. Each expert evaluated the alternatives according to each criterion.

Table 3. Expert evaluation of alternative $A_1$ with respect to criterion $C_1$.

RU-Q (A1-C1)	Linguistic Scale	Equivalent TFNs
		l	m	u
Expert 1	L	0.1	0.2	0.3
Expert 2	ML	0.2	0.35	0.5
Expert 3	L	0.1	0.2	0.3
Expert 4	M	0.4	0.5	0.6
Expert 5	L	0.1	0.2	0.3
Expert 6	M	0.4	0.5	0.6
Expert 7	M	0.4	0.5	0.6
Expert 8	L	0.1	0.2	0.3
Expert 9	MH	0.5	0.65	0.8
Expert 10	L	0.1	0.2	0.3
Expert 11	M	0.4	0.5	0.6
Expert 12	ML	0.2	0.35	0.5
Average $\left({\tilde{x}}_{ij}\right)$		0.25	0.3625	0.475

shows a sample of the twelve-expert evaluation of alternative $(A_1:RU)$ with respect to criteria $(C_1:Q)$, their linguistic variables, and the corresponding triangular fuzzy numbers (TFN). The aggregated fuzzy weights of the criteria and alternatives were then calculated using Equations (2) and (3). The results are shown in the performance rating matrix, depicted in

Table 4. The aggregated fuzzy performance rating matrix.

	Q	E	D	S	F	C	R
RU	(0.25, 0.36, 0.48)	(0.23, 0.36, 0.49)	(0.25, 0.38, 0.5)	(0.27, 0.38, 0.48)	(0.27, 0.39, 0.51)	(0.25, 0.38, 0.5)	(0.22, 0.34, 0.46)
RO	(0.33, 0.44, 0.54)	(0.38, 0.5, 0.62)	(0.31, 0.43, 0.54)	(0.25, 0.38, 0.5)	(0.41, 0.53, 0.64)	(0.36, 0.48, 0.59)	(0.3, 0.41, 0.53)
AU	(0.26, 0.39, 0.52)	(0.27, 0.38, 0.48)	(0.42, 0.54, 0.66)	(0.45, 0.56, 0.68)	(0.27, 0.38, 0.48)	(0.31, 0.43, 0.54)	(0.38, 0.5, 0.63)
UA	(0.38, 0.5, 0.63)	(0.38, 0.5, 0.62)	(0.29, 0.41, 0.53)	(0.22, 0.34, 0.46)	(0.35, 0.48, 0.6)	(0.46, 0.58, 0.69)	(0.42, 0.54, 0.66)
SY	(0.28, 0.4, 0.53)	(0.34, 0.46, 0.58)	(0.33, 0.44, 0.55)	(0.35, 0.48, 0.6)	(0.29, 0.41, 0.53)	(0.3, 0.41, 0.53)	(0.23, 0.35, 0.47)

.

Step 5: The fuzzy best value ${\tilde{f}}_i^{*}$ and fuzzy worst value ${\tilde{f}}_i^{-}$ for each criterion were calculated using Equations (4) and (5). “NB” represents the non-beneficial values and “B” represents beneficial values. The fuzzy difference ${\tilde{d}}_{ij}$ between ${\tilde{x}}_{ij}$ and the fuzzy worst value ${\tilde{f}}_i^{-}$ and the fuzzy best value ${\tilde{f}}_i^{*}$ were computed using Equations (6) and (7).

Table 5. The fuzzy best and worst values.

	Q	E	D	S	F	C	R
	NB	B	NB	B	B	B	B
${\tilde{f}}_i^{*}$	(0.25, 0.36, 0.48)	(0.38, 0.5, 0.62)	(0.25, 0.38, 0.5)	(0.45, 0.56, 0.68)	(0.41, 0.53, 0.64)	(0.46, 0.58, 0.69)	(0.42, 0.54, 0.66)
${\tilde{f}}_i^{-}$	(0.38, 0.5, 0.63)	(0.23, 0.36, 0.48)	(0.42, 0.54, 0.66)	(0.22, 0.34, 0.46)	(0.27, 0.38, 0.48)	(0.25, 0.38, 0.5)	(0.22, 0.34, 0.46)

shows the best and worst fuzzy values.

The separation between the alternatives and the fuzzy best and worst values (${\tilde{d}}_{ij}$) was computed using Equation (6) for beneficial criteria and Equation (7) for non-beneficial criteria.

Table 6. Fuzzy differences.

	Q	E	D	S	F	C	R
RU	(0, 0, 0)	(0.23, 0.36, 0.46)	(0, 0, 0)	(0.21, 0.31, 0.43)	(0.27, 0.36, 0.43)	(0.25, 0.38, 0.5)	(0.22, 0.34, 0.46)
RO	(0.22, 0.24, 0.24)	(0, 0, 0)	(0.11, 0.13, 0.14)	(0.21, 0.31, 0.4)	(0, 0, 0)	(0.17, 0.24, 0.31)	(0.18, 0.26, 0.35)
AU	(0.02, 0.07, 0.14)	(0.21, 0.34, 0.48)	(0.42, 0.54, 0.66)	(0, 0, 0)	(0.27, 0.38, 0.48)	(0.22, 0.32, 0.42)	(0.08, 0.09, 0.1)
UA	(0.38, 0.5, 0.63)	(0, 0, 0)	(0.07, 0.1, 0.11)	(0.22, 0.34, 0.46)	(0.14, 0.16, 0.16)	(0, 0, 0)	(0, 0, 0)
SY	(0.06, 0.11, 0.18)	(0.09, 0.13, 0.15)	(0.15, 0.17, 0.17)	(0.15, 0.18, 0.21)	(0.24, 0.31, 0.36)	(0.23, 0.34, 0.46)	(0.21, 0.33, 0.45)

shows the separation between the alternatives and the best and worst fuzzy values.

Step 6:

Table 7. The calculated values of ${\tilde{S}}_j$ and ${\tilde{R}}_j$.

	${\mathit{S}}_{\mathit{j}}^{\mathit{l}}$	${\mathit{S}}_{\mathit{j}}^{\mathit{m}}$	${\mathit{S}}_{\mathit{j}}^{\mathit{u}}$	${\mathit{R}}_{\mathit{j}}^{\mathit{l}}$	${\mathit{R}}_{\mathit{j}}^{\mathit{m}}$	${\mathit{R}}_{\mathit{j}}^{\mathit{u}}$
RU	1.17619	1.742708	2.274905	0.266667	0.375	0.5
RO	0.891425	1.177218	1.445826	0.222222	0.3125	0.403846
AU	1.208088	1.736364	2.296598	0.416667	0.5375	0.658333
UA	0.808701	1.091026	1.353509	0.375	0.5	0.625
SY	1.128242	1.560875	1.970866	0.240196	0.335156	0.456522
${\tilde{S}}^{*},{\tilde{R}}^{*}$	0.808701	1.091026	1.353509	0.222222	0.3125	0.403846
${\tilde{S}}^{-},{\tilde{R}}^{-}$	1.208088	1.742708	2.296598	0.416667	0.5375	0.658333

lists the calculated values of ${\tilde{S}}_j$ and ${\tilde{R}}_j$ using Equations (8) and (9).

Step 7: Using Equation (10), the value of ${\tilde{Q}}_j,$ where $v=0.5$ was calculated. The results are shown in

Table 8. $Q_j$ values.

	${\mathit{Q}}_{\mathit{j}}^{\mathit{l}}$	${\mathit{Q}}_{\mathit{j}}^{\mathit{m}}$	${\mathit{Q}}_{\mathit{j}}^{\mathit{u}}$
RU	0.574353	0.638889	0.677416
RO	0.103563	0.066131	0.048944
AU	1	0.995132	1
UA	0.392857	0.416667	0.434509
SY	0.446258	0.410837	0.430799

.

Step 8:

Table 9. Crisp values.

Alternative		${\mathit{S}}_{\mathit{j}}$	${\mathit{R}}_{\mathit{j}}$	${\mathit{Q}}_{\mathit{j}}$
RU	A1	1.734128	0.379167	0.632387
RO	A2	1.172922	0.312767	0.071192
AU	A3	1.744353	0.5375	0.997566
UA	A4	1.086065	0.5	0.415175
SY	A5	1.555215	0.341758	0.424683

shows the defuzzification of the values ${\tilde{Q}}_j,{\tilde{S}}_j\mathrm{a}\mathrm{n}\mathrm{d}{\tilde{R}}_j$ using Equation (11).

Step 9: The S, R, and Q values of each choice were sorted in ascending order for ranking.

Table 10. Ranks based on crisp, S, R, and Q values.

Rank Based on Q	Rank Based on R	Rank Based on S
A2	A2	A4
A5	A4	A2
A1	A5	A5
A4	A1	A1
A3	A3	A3

shows the ranking list according to the crisp values.

Step 10: Select the best alternative as a compromise solution based on conditions C1 (Equation (12)) and C2.

Table 11. Compliance with conditions/checking the two condition and select the optimal solution.

2	Q(A4)	0.415175
1	Q(A2)	0.071192
Q(A4)-Q(A2)		0.343983
DQ		0.25
Condition 1		OK
A2 is best ranked based on R		A2
Condition 2		OK
The best Alternative		A2

shows that alternative A2 has the minimum Q value and satisfies the first condition, C1. In addition, as A2 is top-ranked based on the R values, A2 is the best solution, as specified by the second criterion, C2. Thus, A2 was considered the optimal compromise solution.

Figure 4 shows the rankings of the suppliers based on the Q-value, ordered as Romania, Ukraine, Syria, Russia, and Australia. New sources may be considered in the future to include other suppliers from Europe and Central Asia and suppliers from South America and China.

Based on the current criteria considered by MITS, MOA, and JSMO, Romania is the best supplier of wheat to Jordan based on the attributes under consideration. To this extent, this assessment aligns with the assessment of the experts. This assessment is commensurate with the current situation, where Romania is considered a reliable, close source with good communication, exchange rates through official channels, flexibility, and the least expensive. This was due to the Russia-Ukraine war, the increase in taxes and fees for Russian exports, the long distance and limited availability of wheat from Australia, and Syria’s inability to supply Jordan with wheat due to the lack of reliability resulting from the civil war and the current poor economic situation. Additionally, contractors who apply for tenders to import wheat from Romania offer the lowest prices according to the required specifications; therefore, this source is considered appropriate in the current situation. Suppliers are subject to changes according to the new global environment, wheat prices from the source, robustness, shipping and handling prices, and taxes imposed by the exporting countries.

5. Conclusions

In most nations, including the Middle East and Jordan, where meal tables hardly ever go without wheat derivatives, wheat is a basic and important product. Therefore, through the import, storage, and distribution of wheat, the government manages a steady supply. The wheat supply chain has a significant impact on sustainability and food security as well. Moreover, Jordan faces several challenges, such as a rapidly growing population, extensive urbanization, water scarcity, and poor soil quality. Agriculture is not a plausible solution, despite the growing demand for food. Further, supply selection is influenced by a wide range of factors, including the price of the product in question, the number of producers, the cost of inputs, technological improvements, the cost of substitute products, and unpredictable phenomena such as weather. This study fills the knowledge gap regarding ranking or choosing top wheat suppliers, whether for Jordan or the MENA region. As wheat is regarded as a key food commodity in Jordan, this study identifies alternatives to wheat providers based on recognized requirements. This is especially important given that the government is in charge of importing, managing, and storing wheat.

Identifying Jordan’s primary wheat suppliers and ranking them according to specified criteria was the major objective of this study. The alternatives for wheat suppliers were investigated based on established standards. The study develops a comprehensive framework for SS stages and activities to efficiently discover wheat suppliers and reduce the risks associated with the selection process. Further applying the fuzzy-VIKOR method, the primary wheat suppliers to Jordan were identified, evaluated, and ranked based on the identified features using a numerical case.

Although many nations produce and export wheat, depending on various factors, the specialists’ panel chose five key nations to import from: Russia, Romania, Ukraine, Australia, and Syria. Quality, cost, delivery, origin, flexibility, communication, and importer dependability were the various criteria accepted by the committee of experts for the selection of wheat suppliers. Based on the outcomes, it can be concluded that Romania is the best supplier of wheat to Jordan. This evaluation is in line with that of the specialists. In the current scenario, Romania is seen as a trustworthy, close source with good communication, exchange rates through official channels, flexibility, and being the least expensive. This was due to the conflict between Russia and Ukraine, rising taxes and fees on Russian exports, the difficulty of obtaining wheat from Australia due to its distance and scarcity, and Syria’s inability to supply Jordan with wheat due to its unreliability because of the civil war and the country’s current dire economic situation. Additionally, suppliers who submit bids to import wheat from Romania offer the lowest costs in accordance with the necessary requirements; thus, the source is deemed appropriate given the current circumstances. Suppliers may alter in response to changes in the global economy, wheat prices at the source, exchange rates, transportation and handling costs, and national export taxes.

This study will assist traders, decision-makers, and those concerned with wheat imports as a strategic commodity in identifying and selecting suppliers. This study also suffers from some innate limitations, as it evaluates only five suppliers, which could be increased to include all countries that export wheat. The members of the expert committee and the number of criteria used in the evaluation could also have been increased to obtain more objective and accurate results. It is also possible to include more than one country or region in the supplier evaluation process. The currency exchange rate and the proximity of the provider can also be considered additional criteria. Future studies can include the Middle East in general and compare all suppliers in the region based on cost, quality, and reliability to identify and rank suppliers according to the specified criteria. Other MCDM methods can be used in combination, and their results can be compared.