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In the outcome of natural disasters, different factors, i.e., uncertain lead time and material quality, incur an additional cost, downgrading the supply chains' efficiency. The optimal inventory decisions are challenging due to the complexity arising from the multi-product, multi-vendor consideration, uncertainty of supplies, and conflicting objectives in sustainable construction supply chains. To fill the existing research gaps, this research presents an operation research modeling framework to minimize the amount of carbon emitted by suppliers' vehicles as well as ordering and holding costs in a post-disaster construction supply chain under the epistemic uncertainty of quality and cost data. Furthermore, the quality of the received material is maximized in the model. Also, the weights of objectives are estimated using two MCDM techniques. A case study is delineated to validate the proposed optimization model and its performance. To make the comparison of the proposed model with the suppliers' efficiency, the DEA model is applied, and sensitivity analysis is presented in this case. The results indicate that supplier selection based on the efficiency of suppliers can culminate in more contractors' satisfaction, although the mathematical model can choose the suppliers with consideration of the project timeline. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to develop an in-depth understanding of how supplier selection helps social enterprises achieve their social missions while maintaining commercial viability. Design/methodology/approach: The paper applies a multiple-case design to study the supplier selection processes of 15 Dutch social enterprises. Findings: Social enterprises tend to build supply relationships through existing networks and evaluate suppliers based on value alignment, relationship commitment, resource complementarity, and cost. Depending on the possibility of social value creation in supplier selection, the importance of these criteria varies across different social enterprise models and between key and non-key suppliers. Moreover, suppliers' long-term relationship commitment can help reconcile tensions between the social and commercial logic of a social enterprise and facilitate impact creation. Research limitations/implications: Data collection is limited to the perspectives of buyers – the social enterprises. Future research could collect supplier-side data to explore how they engage with social enterprises during the selection process. Practical implications: Managers of social enterprises can use our research findings as guidance for selecting the most suitable suppliers, while organizations that want to collaborate with social enterprises should actively build network ties to be identified. Originality/value: We contribute to the cross-sector collaboration literature by showing the underlying reasons for the preference for network reinforcing and indirect networking in supplier identification. We contribute to the social impact supply chain literature by revealing the critical role of supplier selection in shaping collaboration outcomes. [ABSTRACT FROM AUTHOR]

The landscape and the factors affecting the development of advanced tools (e.g., artificial intelligence, predictive analytics of outbreaks, knowledge management, vigilance, and reporting systems) with specific concern on supply chain disruptions are described, so as the main topics on health emergencies, preparedness, and mitigation are introduced. This work deals with Multi-Criteria Decision Making (MCDM) tools to address the Suppliers Selection Problem (SSP) and prepare emergency responses by applying Robust Optimization (RO) models, dealing with uncertainty and risk, and coping with computational issues. Definitions and goals are updated, and approaches and models for emergency preparation and responses are adjusted to enlighten the costing and performance indexes. Relevant topics are also integrated: the provisions (and procurement) disruptions and the mitigation of related impacts. After that, an actual situation for deciding on the supplier bid for Information Systems oriented to emergency events is addressed. The decision matrix is built using the referred criteria. Four different MCDM methods/models with the associated software are applied: (i) the simple additive model, (ii) the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) model, (iii) the "e" Fuzzy model, and (iv) - the Elimination and Choice Expressing Reality (ELECTRE) model. The hierarchy of alternatives is built, and the decision process ends by assigning one of the bidder supplier companies. As the differences between the obtained hierarchies are minor, a comparative analysis that addresses the deviation amplitude, the best and the least alternatives, is performed. [ABSTRACT FROM AUTHOR]

Prefabrication is increasingly recognized as a sustainable construction practice, with the efficiency of prefabricated component (PC) production lines playing a critical role in its success. However, supplier selection for PC production lines has become more complex due to evolving industrial demands, uncertain supply chain conditions, and operational complexities. This study addresses this gap by developing a lean-based AHP–improved VIKOR decision-making framework to enhance the supplier selection for PC production lines. The framework integrates advanced lean principles with universal and specific evaluation criteria, identified through a comprehensive literature review and expert interviews. Its validity was tested via a real-world case study with Yizhong Construction Co., Ltd., Tianjin, China. The results show that the three suppliers are ranked as Zhongjian > Tianyi > Xindadi, where Zhongjian is the best supplier in this case study, with a VIKOR index of 0.156. The findings show that the developed framework can improve the supplier selection efficiency by aligning with lean principles and enhancing the performance of PC production lines. By addressing the challenges of PC supplier selection, this study provides a practical tool to advance the adoption of prefabrication in construction. Furthermore, it contributes to the development of the PC industry by offering a robust method for the selection of suitable suppliers, which can help to optimize the production efficiency and support sustainable practices in construction. [ABSTRACT FROM AUTHOR]

Emergency supply chain management has recently drawn growing attention of managers and researchers with frequent appearance of pandemics, disasters and safety accidents. Previous studies proposed methods for supplier selection and order allocation, while they cannot satisfy the demand for emergency supplies as emergency events bring many uncertainties and risks in supply chain disruption. To guarantee the efficiency in emergency supplies procurement, this work aims at putting forward a two-stage approach for emergency supplier selection and order allocation by use of three-way decision and fuzzy multi-objective optimization. Firstly, by considering the perceived utilities and perceived losses of purchasing process simultaneously, a prospect profit-based three-way decision model is established. Next, the prospect multi-attribute three-way decision model for emergency supplier selection is proposed, constructing the calculation approaches of thresholds, conditional probabilities as well as decision rules. Thirdly, inspired by perceived utilities and perceived losses of supplies purchasing, the utility-based objective function and loss-based objective function are introduced to multi-objective optimization model for order allocation. Finally, a real case of government emergency supplies procurement is discussed to show the applicability and effectiveness of the proposed approach. The final results of the proposed methodology show that it can effectively manage data with uncertainty, determine the qualified suppliers as well as alternative suppliers simultaneously to prevent emergency supply chain disruption, and provide satisfactory solutions for order allocation by introducing different combinations of objective functions according to decision makers' preference. [ABSTRACT FROM AUTHOR]

Due to the outbreak of COVID-19 around the globe in the last few years, the need for pharmaceutical supply chains is felt more than before. However, increasing uncertainties along with unpredictable demand for products led to disruptions in supply chains when receiving requests from retailers. These disruptions not only affected the economic aspect of supply chains but also caused shortages in hospitals and medical centers. Therefore, it has become significant for companies to select their suppliers to avoid disruptions in the case of the severity of infections. To address this issue in practice, this paper has been conducted based on a case study to address the role of lean, agile, resilience, and green (LARG) criteria in selecting the supplier in a pharmaceutical supply chain and compare the results obtained before and after the prevalence of COVID-19. The main purpose of this study is to determine and evaluate different indicators within the LARG concept to avoid disruptions when selecting suppliers. Besides, the significance of these criteria before and after the pandemic condition is addressed. Due to addressing multiple aspects of the problem, a hybrid fuzzy multi-attribute decision-making (MADM) approach is adopted for this elaboration when the four LARG criteria are integrated with eighteen supplier selection sub-criteria. To calculate the impact of each criterion (or sub-criteria), a fuzzy best–worst method (BWM) along with an additive ratio assessment (ARAS) is employed to propose a supplier ranking for a distributor of a pharmaceutical supply chain. The developed model is novel as LARG criteria in the context of supplier selection have not been studied to address the disruptions in the pharmaceutical supply chain. This is significant because it gives insight to both retailers and suppliers to emphasize the correct criteria, especially in the pandemic or related disrupting conditions. The results demonstrated that quality, collaboration, safety stock, and environmental criteria weigh the highest before the pandemic, while just-in-time delivery, lead time, safety stock, and environmental criteria weigh the highest after the pandemic. This study demonstrates that developing a supplier selection approach that meets the demand in a short time and recommends suppliers to hold surplus inventory helps the healthcare systems better respond to the market needs. [ABSTRACT FROM AUTHOR]

Background: The supplier selection process (SSP) has grown as a crucial mechanism in organizations' supply chain management (SCM) strategies and as a foundation for continuously gaining a competitive advantage. The concept of the circular economy has garnered significant interest due to its ability to address both environmental and social criteria. It is highly important to carefully choose suppliers across all industries that take into account circular and sustainability issues, as well as traditional criteria. There is very limited research involving the supplier selection process in the Indian HVAC manufacturing sector. Design/Methodology/Approach: Thus, this study aimed to determine the critical factors for sustainable supplier selection for HVAC manufacturing firms using a mixed research method with three stages: a secondary study, the Delphi method, and the fuzzy analytical hierarchy process (FAHP). Thirty-two critical sub-factors were identified and grouped into eight major factors: delivery, economic, environmental, social, management and organization, quality, services, and supplier relationship. Results/Conclusions: For HVAC manufacturing firms, the major factors of delivery, quality, and economics were found to be top-ranked among the factors, followed by environmental factors. Studies in developing countries using sustainable factors are still nascent, especially in India. Originality/Value: This study's novelty lies with the proposed eight major factors, comprising all facets of organizations, including sustainability factors. Supplier selection in HVAC manufacturing firms is exhaustively dealt with in this study, filling a gap in the existing literature. This is important because HVAC products are high-energy-consuming, high-energy-releasing, and costly. [ABSTRACT FROM AUTHOR]

The world is characterized by volatility, uncertainty, complexity, and ambiguity (VUCA). In such an environment, the viability in terms of digitalization, resilience, and sustainability capabilities has gained worldwide attention in supply chain management. Therefore, it is crucial to give special consideration to these paradigms when selecting suppliers. Moreover, the availability of data in digital supply chain systems can aid in supplier selection by using Artificial Intelligence techniques to identify viable suppliers. This approach can streamline the supplier selection process and lead to more efficient and effective manufacturing operations. Thus, it is necessary to have a big data analytics infrastructure in today's data-driven world. In this context, this paper aims to design a multi-agent system that belongs to the theory of Distributed Artificial Intelligence based on big data analytics to give a strong tool for finding the best viable suppliers based on a thorough and data-driven evaluation. To do so, designing a multi-agent-based big data analytics system model necessitates identifying the multiple criteria needed for selecting viable suppliers in real-time decision-making. To this end, through a literature review, this paper analyzes more than 140 publications and identifies the main criteria needed for viable suppliers' selection in the VUCA world. Therefore, the proposed system can be used as an intelligent viable supplier selection that improves the quality of the process and controls it while considering different capabilities. It presents a comprehensive model for viable supplier selection, consisting of four main layers: decision-making system, data resources, supplier selection, and big data analytics. The model incorporates six types of agents: Suppliers agent, Resource Agent, Knowledge Management Agent, Pilot Agent, Analyst Agent, and Decision-Making Agent. The integration of these layers and agents enables real-time data-driven decision-making, contributing to the selection of viable suppliers in a volatile and uncertain environment. The proposed model enhances supply chain performance in the digital era, offering a robust tool for both academics and practitioners to improve the quality of supplier selection. [ABSTRACT FROM AUTHOR]

Supplier selection is crucial in supply chain management, directly impacting operational efficiency and production continuity. This study compares two Multi-Criteria Decision Making (MCDM) techniques—Fuzzy VIekriterijumsko KOmpromisno Rangiranje (VIKOR) and Fuzzy Multi-Objective Optimization based on Ratio Analysis (MOORA)—to optimize supplier selection for a manufacturing company in Indonesia facing single supplier dependency. Fuzzy logic is employed to reduce subjectivity and ambiguity in the selection process. The Fuzzy MOORA method achieved a sensitivity score of 16.7% compared to 0.00% for Fuzzy VIKOR, indicating higher responsiveness to changes in criteria weights. Additionally, Fuzzy MOORA reduced the supplier selection process time by 28.5% compared to the current system, demonstrating its efficiency in practical applications. In general, the Fuzzy MOORA is superior in terms of the performance level with a rate of 3 out of 4 factors including the computational times, simplicity, and mathematical calculation. This study contributes to enhancing supplier selection methodologies by providing a comparative analysis of these techniques in a real-world context.

Application of models for supplier assessment and selection in the clothing industry remains relatively underexplored. To fill this gap, this research study introduces the following fuzzy hybrid models for evaluating and selecting suppliers for clothing manufacturing firms: fuzzy set theory, Analytic Hierarchy Process method–fuzzy-Technique for Order of Preference by Similarity to Ideal Solution (AHP–fuzzy-TOPSIS), AHP–fuzzy-Weighted Sum Model (WSM), and AHP–fuzzy-Weighted Product Mean (WPM). Criteria weights were established utilizing these models, which were applied to identify the optimal supplier. A practical study was conducted within a clothing firm to evaluate the effectiveness of these fuzzy hybrid models. The main results reveal that the AHP–fuzzy-TOPSIS model outperforms the AHP–fuzzy-WSM and AHP–fuzzy-WPM models in selecting the optimal alternative. Indeed, this approach has the potential to be adapted to different industrial sectors, considering the specific criteria and conditions that govern their supplying processes.

This paper proposes a decision-support model for supplier selection based on integrating the step weight assessment ratio analysis (SWARA), the method based on the removal effects of a criterion (MEREC), and Additive Ratio Assessment (ARAS) using a case study of the leather industry in Indonesia. The model starts by identifying the main criteria using the opinions of leather industry experts using Delphi. The second stage is to weigh them based on the main criteria, using compromising of objective and subjective weighting methods, namely MEREC and SWARA. The suppliers are selected and ranked based on the main criteria. Lastly, a sensitivity analysis will be performed to check the robustness. Delphi methodology adopted in this study gives managers in Indonesia's leather industries insights into the factors that must be considered when selecting suppliers for their organizations. The selected approach also aids them in prioritizing the criterion. Managers can utilize the supplier selection methodology suggested in this study to rank the suppliers based on various factors/criteria. This study makes three novel contributions to the supplier selection area. First, Delphi is applied to the Indonesian leather industry and integrates MEREC, SWARA, and ARAS into supplier selection. Second, sensitivity analysis allows the determination of the impact of modifications in the primary criteria on the ranking of suppliers and assists decision-makers in assessing the resilience of the process. Last, we find it essential to develop a simple methodology for managers of the Indonesian leather industry to select the best suppliers. Moreover, this method will help managers divide complex decision-making problems into more straightforward methodologies.

Purpose: With the emergence of Industry 4.0, supply chains have undergone fundamental changes, profoundly impacting on production and supply processes in various industries. The main goal of this study is to identify and evaluate the important criteria for selecting suppliers in Iran's steel industry, taking into account industry 4.0 technologies and their role in supplier selection.Methodology: By studying and reviewing previous researches, the study first identifies the criteria. Then, the Fuzzy Delphi method is employed to confirm these criteria. Weighting and prioritization of criteria is done by using Fuzzy FUCOM method.Findings: The results show that criteria such as quality, price, how to use the internet of things, lead time, settlement method and digital cooperation play an important role in choosing a supplier in Iran's steel industry, considering the role of Industry 4.0 technologies.Originality/Value: For the first time, this research examines the supplier selection criteria in Iran's steel industry, focusing on the technologies and concepts of Industry 4.0. Because decision-making in the real world is often faced with uncertainty, Employing the Fuzzy Delphi and Fuzzy FUCOM methods in this article will make decisions in the selection of suppliers in Industry 4.0 enables more accurate and reliable decision-making.

Abstract In this study a real case multi-objective material and supplier selection problem in cardboard box production industries is studied. This problem for the first time optimizes the objective functions such as total wastage amounts remained from all raw sheets, total costs of the system including purchasing cost and transportation cost (including fixed and variable costs) of the raw sheets, and total overplus of produced cardboard boxes. To be closer to the real situations, as a novelty, the problem is formulated in belief-degree-based uncertain environment with normal distribution where this type of uncertainty applies the ideas of experts. A solution approach including two steps is proposed to solve the problem. In the first step, the proposed uncertain formulation is converted to a crisp form using a typical chance constrained programming scheme. In the second step, a new goal programming approach containing a piecewise penalty function is developed in order to solve the obtained multi-objective crisp formulation. In this approach, based on the ideas of experts, multiple goals are considered with different penalty values. A case study from cardboard box industries is considered to evaluate the proposed formulations and solution approach. According to the obtained results, the proposed solution approach is compared to similar approaches of the literature and its efficiency is studied.

Supplier selection has emerged as an important activity regarding strategic purchasing with implications for the operational efficiency of both organisations and supply chains. Given the need to evaluate both qualitative and quantitative criteria for different supply alternatives, the decision-making process became more complex.

In light of the burgeoning electric vehicle market, the demand for lithium-ion batteries (LiBs) is on the rise. However, the supply of materials essential for LiBs is struggling to keep pace, posing a significant challenge in meeting the surging market demand. This study offers a viable solution to bolster the dependability of the material supply chain by prioritizing material suppliers who are deeply committed to sustainable practices and performance. We have developed a comprehensive system for evaluating sustainable performance, encompassing three vital dimensions: economic, social and environmental contexts. Then, we introduced a pioneering approach known as the multi-criteria material supplier selection (MCMSS) methodology which amalgamates multi-criteria decision-making techniques with artificial intelligence to effectively generate sustainability performance of suppliers and identify the most suitable supplier, out of all alternatives. Eventually, the supply of four key materials of LiBs is used as illustrative examples to verify the feasibility and rationality of the proposed MCMSS. This work carries significant implications for overseeing the LiB material industry. The MCMSS model offers a solution for the government to establish a comprehensive material supplier database to intelligently supervise the activities of material suppliers and foster collaboration between upstream and downstream enterprises within the LiB industry. [ABSTRACT FROM AUTHOR]

In the food manufacturing industry, which is critical to national economies, there is a growing imperative to meet heightened safety, quality, and environmental standards, particularly in the face of supply chain disruptions. This study addresses the gap in literature by integrating sustainable and resilient supply chain theories with risk management and low-carbon principles into a supplier selection framework. Utilizing the Delphi method, fuzzy analytic hierarchy process (FAHP), and fuzzy multi-objective programming (FMOP), we develop a decision-making model specifically calibrated for the food sector. Initially, the study establishes a comprehensive criteria system encompassing quality, cost, delivery, low-carbon, and risk management through a literature review and expert consultation. Subsequently, FAHP is employed to determine the relative importance of each criterion in supplier selection. Furthermore, FMOP is utilized to develop a decision-making model for optimizing supplier selection and order allocation. Validated through a numerical study based on a Chinese food manufacturer, the framework presents a practical tool for food manufacturers, ensuring supply chain stability while aligning with sustainability objectives. This research refines decision making and strengthens the competitive stance of food manufacturers, significantly propelling the industry's green transformation. [ABSTRACT FROM AUTHOR]

The process of selecting suppliers is a critical and multifaceted aspect of supply chain management, involving numerous criteria and decision-making variables. This complexity escalates when integrating sustainable and resilient factors into supplier evaluation. This literature review paper explores various evaluation criteria that encompass economic, environmental, social, and resilience dimensions for supplier selection. Different methodologies to model and address these complexities are investigated in this research. This review synthesizes the findings of 143 publications spanning the last decade (2013–2023), highlighting the prevalent evaluation criteria and methodologies and identifying existing research gaps. In addition, the feasibility of combining multiple approaches to more accurately reflect real-world scenarios and manage uncertainties in supplier selection is examined. This paper also proposes a decision-making framework to assist practitioners in navigating the intricacies of this process. The paper concludes by suggesting seven potential directions for future research in this evolving field. [ABSTRACT FROM AUTHOR]

• Considers supplier selection problem with uncertain demand. • Assumes that suppliers produce defective units and offer buyback contract. • Uses mathematical programming and develops a solution algorithm. • Reports interesting findings and insights. Demand and supply uncertainties are the two challenges of any supply chain. Supply uncertainty can happen if suppliers' production systems generate defective units. Demand uncertainty also indicates that the demand fluctuates over time. Suppliers may share the risk of uncertain demand with buyers by agreeing to buy back unsold inventory at the end of the season. Also, a multi-sourcing scenario can be an effective strategy to handle both uncertainties. We consider in this paper an inventory problem where a buyer purchases an item from multiple suppliers. The suppliers may have limited production capacity, produce defective units, and allow the buyer to return unsold units at the end of the season with a buyback price. To determine the order quantities from the suppliers, the buyer must evaluate them based on wholesale price, buyback price, and defective rate. We first develop a solution algorithm to solve the problem and then provide some managerial insights for both the buyers and the suppliers. [ABSTRACT FROM AUTHOR]

In today's competitive and environmentally conscious industries, the ability of organizations to adapt and respond is more important than ever. This study focuses on overcoming the obstacles faced by the Iranian automobile sector by highlighting the significance of incorporating green supply chain techniques. The research intends to integrate organizational operations with environmental sustainability goals by utilizing a MULTIMOORA strategy for supplier selection. The Iranian automobile sector, facing substantial environmental challenges, requires a strategy framework for selecting environmentally friendly suppliers in order to sustain competitiveness and fulfill ecological obligations. The study develops a supplier selection model based on extensive research and expert knowledge. The Delphi and MULTIMOORA techniques are employed to assess and prioritize suppliers according to green criteria, assuring conformity with environmental goals. Data are collected by conducting a comprehensive analysis of the existing literature and engaging in conversations with industry experts in order to acquire information for the construction of the model. The results emphasize the crucial significance of trust-based relationships with suppliers, rigorous compliance with quality standards in new product development, and substantial investment in employee training and development. Sector analysts view these characteristics as crucial for promoting sustainability and gaining a competitive advantage in the Iranian vehicle sector. This study provides firms with strategic instruments to effectively negotiate the intricacies of green supply chain management, with a particular focus on the need for adopting sustainable practices while selecting suppliers in the dynamic and competitive context of the Iranian automobile industry. [ABSTRACT FROM AUTHOR]

Due to escalating climate variation and excessive resource utilization, organizations are under enormous pressure from governing agencies to diminish the adverse environmental influence of their products. In addition, Industry 4.0 has lately instigated substantial changes in the supply chain (SC) operations of enterprises. Therefore, to stay competitive, organizations are eager to purchase from those suppliers who can assist them in making their SC efficient and environment-friendly. In this concern, for the first time, this research amalgamates the notions of sustainability, circular economy, and Industry 4.0 for selecting the beverage sector's suppliers using a novel hybrid approach. With the help of a thorough literature examination and experts' feedback, fourteen sub-criteria were initially identified, which were later categorized into the three main aspects (economic, social, and circular) for suppliers' evaluation. Subsequently, the fuzzy full consistency method (FUCOM) was utilized to compute the selected criteria and sub-criteria relative weights. Later, the fuzzy multi-objective optimization based on ratio analysis with the full multiplicative form (MULTIMOORA) method was employed to assess and rate the efficacy of suppliers based on three ranking subordinates. Finally, the obtained rankings were aggregated using the ordinal dominance theory (ODT) to identify the ultimate rank of the suppliers. To effectively manage the inherent uncertainty associated with the decision-makers (DMs) judgment, fuzzy set theory (FST) has been combined with the proposed methodology. A Pakistani beverage sector case study was offered to show the viability and usefulness of the suggested technique. Based on the obtained results, cost (C11), GIS/GPS assisted logistics (C15), training and awareness of employees on Industry 4.0 (C21), cyber-physical production and smart manufacturing (C13), and eco-friendly packaging (C33) sub-criteria were found to be the most significant criteria. In contrast, supplier S2 achieved the highest rank among selected suppliers based on its performance. Finally, the sensitivity and comparative analysis results reveal that the proposed hybrid technique delivers reliable and robust outcomes. The proposed research will offer a crucial guide for the managers of manufacturing organizations if they want to optimize their resource consumption and SC efficiency. [ABSTRACT FROM AUTHOR]

Background: Supply chain management (SCM) plays a fundamental role in the progress and success of organizations and has continuously evolved to better adapt to today's complex business environments. Consequently, the issue of supplier evaluation and selection (SES), which is one of the most critical decisions in SCM, has gained special significance and has been examined from various perspectives. The concept of green and resilient (gresilient) SCM has emerged in response to recent concerns about environmentally friendly production and operations, as well as organizations' ability to cope with crises and disasters. In the rapidly growing construction industry, applying gresilient principles can ensure green operations and help overcome future challenges. Methods: This study focuses on gresilient SES in a real-world construction case study, proposing a streamlined FUCOM (S-FUCOM) approach. The proposed method streamlines traditional FUCOM processes to solve decision-making problems in deterministic and uncertain environments. Several numerical examples are provided to illustrate its applicability. Results: the case study results identify air emissions, environmental management systems, and restorative capacity as the most critical gresilient SES criteria. Conclusions: The third supplier emerged as the top performer based on decision-making indicators. Finally, a sensitivity analysis was conducted across 20 scenarios, demonstrating that S-FUCOM is robust and provides stable results. [ABSTRACT FROM AUTHOR]

Background: This research endeavors to enhance supplier selection processes by combining the Analytic Network Process (ANP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodologies, with a specific focus on sustainability criteria. Method: Initially comprising 21 sub-criteria derived from prior research, the selection criteria are refined to 17, eliminating redundant elements. The core principle guiding this refinement is the comprehensive coverage of economic, social, and environmental dimensions, essential for sustainable supplier evaluation. Results: The study's outcomes underscore the paramount importance of economic criteria (0.0652) in supplier selection, followed by environmental (0.0343) and social dimensions (0.0503). Key sub-criteria contributing significantly to this evaluation encompassed consistent product quality, competitive raw material pricing, proficient labor capabilities, recycling potential, punctual delivery performance, and effective waste management practices. Conclusions: These sub-criteria are thoughtfully integrated into the sustainable assessment framework, aligning seamlessly with the economic, environmental, and social criteria. [ABSTRACT FROM AUTHOR]

Procurement of new raw materials is needed when product demand increases, and raw material suppliers must be determined to meet the company's needs. This research examines what criteria a company needs when selecting criteria using Delphi. The weighting of criteria cannot be separated from the element of the decision maker's subjectivity; therefore, it is necessary to compromise between subjective and objective criteria. Therefore, the study used The Method of Removal Effects of Criteria (objective weighting of criteria) and Stepwise Weight Assessment Ratio Analysis (subjective weighting of criteria) in weighting criteria. Then, considering the weight of the criteria, the Proximity Index Value (PIV) is used to evaluate and rate the suppliers. The offered methodology is applied to a real case study from a leather manufacturing company in Indonesia to verify its applicability with a sensitivity analysis performed on different scenarios. The findings indicated that the proposed model is dependable and that the rankings are resilient to fluctuations in the criterion weights.

Selecting suppliers strategically is a critical challenge in supply chain design, particularly in a dynamic global marketplace where maintaining competitiveness and sustainability is essential. Supplier location is a key factor that significantly impacts supply chain sustainability. Hence, this study proposes a new Sustainable Joint Economic Lot Size (S-JELS) model that integrates average annual cost and total amount of emissions associated with annual activities related to both buyer and supplier. The developed model accounts for costs and emissions from the buyer’s purchasing, inventory management, and transportation activities, as well as from the supplier’s production, packaging, and delivery processes. Additionally, the model incorporates various transportation modes, including shipping, trucking, and rail, to more accurately assess the environmental impact of different supply chain strategies. The proposed JELS model can support managers performing sustainable purchasing decisions and assess a sustainable supplier selection. The developed model is applied in a leather case study characterized by high resource intensity, significant environmental impacts, and complex global logistics to accomplish supplier selection between one domestic and one international supplier, aiming to determine the most sustainable strategy in terms of cost and emission.

AbstractIn a multi-channel distribution environment, the selection and assessment of suppliers are pivotal to effective retailer performance management. This necessitates heightened qualifications and competence from suppliers. This study examines the relationship between the perception of supplier firms’ status, evaluation performance, and overall satisfaction of food buyers with suppliers in online/offline channels. In addition, this study investigates the moderating impacts of the distribution channel to explore the influence of the online/offline distribution channel on this relationship. For exploratory research, food buyers from 20 large retailers headquartered in Seoul, South Korea, were surveyed, and 110 valid questionnaires were used for analysis. Results of the study indicate that among suppliers’ firm factors, suppliers’ firm size and experience with the retailer were significantly and positively related to buyer satisfaction. Likewise, among evaluation factors, product competency and delivery reliability were significantly and positively related to buyer satisfaction. In addition, the results reveal that the distribution channel has moderating effects on the relationship between firm size and buyer satisfaction, as well as between experience with the retailer and buyer satisfaction. Therefore, the study verified that the distribution channel is an important factor that must be considered not only in the consumer market but also in the buying behavior research of the business-to-business market.

AbstractThis research develops an optimization model of supplier selection and order allocation with transportation mode alternatives under carbon cap and trade policy. The objective function of the model is to maximize the manufacturer’s total profit. Total revenue comes from the selling of both good items and imperfect items. Total cost in this research consists of purchase cost, transportation costs from supplier to manufacturer and from manufacturer to retailer, transaction cost, holding cost, inspection cost and carbon emission cost. Transaction cost is applied for each selected supplier in each period without considering the type and quantity of the item. This research considers a supply chain with multiproducts, multiperiods, multisuppliers and multiretailers. We assumed that the manufacturer inspects all the products come from the suppliers and some of the products are defective. Those defect items are sold in a lower price at a secondary market. This research has a contribution in the inclusions of carbon emission and defect product in the model. Hence, the model is in line with the sustainability issue and closer to the real condition in terms of imperfect manufacturing. A mixed integer linier programming (MILP) model is developed in this research, and the model was solved using Lingo 15.0 software with branch and bound method. The result of sensitivity analysis shows that the objective function is sensitive to the changes of demand, selling price of good item, transportation cost, purchasing cost and holding cost. While the decision variables of order allocation and the number of transportation mode are sensitive to the changes of demand, transportation cost and purchasing cost.

Supply chain management (SCM) is crucial in establishing long-term partnerships that are pivotal for achieving sustained business success. Effective SCM demands rigorous criteria and decision-making processes, which significantly impact the overall outcomes. Recent studies highlight cloud-based market analysis as a valuable tool for assessing supply chain dynamics, offering insights into the benefits and challenges of SCM. The integration of deep learning (DL) and machine learning (ML) approaches in SCM presents transformative potential, enabling more efficient management of the supply chain. This paper identifies the contributions of DL and ML techniques in various aspects of SCM, including supplier selection, production, inventory control, transportation, demand and sales estimation, and others. The extensive review presented in this work delivers an in-depth examination of the integration of DL and ML with SCM, highlighting strategies for enhancing operational efficiency, addressing current limitations, and identifying future research opportunities. A comprehensive literature table consolidates existing research on enhancing SCM with ML and DL techniques, offering a precise overview of objectives, findings, and areas for improvement, and providing rapid insights into the evolving landscape of SCM.

Purpose: The COVID-19 pandemic has led to a significant crisis in society's health, industries, and businesses. In this regard, the medical devices industry has played an important role in crisis management and providing healthcare and has faced several major challenges in supplying raw materials, production activities, and distribution activities due to the disruptions caused by the pandemic. One of the critically important issues in the medical devices supply chain is supplier selection. Hence, this research investigates the supplier selection problem considering the emerging concepts that have dramatically attracted the attention of researchers after the COVID-19 pandemic, namely viability and Industry 5.0.Methodology: In this study, a hybrid fuzzy decision-making approach is developed to investigate the viable supplier selection problem considering the Industry 5.0 dimensions. In this regard, in the first stage, according to the literature and experts, the main indicators of the research problem are extracted, and their weights are calculated using the fuzzy best-worst method. In the next stage, the feasible suppliers are evaluated by employing the fuzzy VIKOR method. Also, to show the robustness and validation of the proposed approach, its results are compared with those of traditional approaches.Findings: In this study, a list of indicators, including six aspects and 34 criteria, is provided for the research problem based on its nature, and their importance has been computed. Based on the outputs, the general metric is the most important aspect, and the human-centricity metric is the least significant. Also, the results show that in addition to the general criteria, such as cost and quality, other criteria, such as reliability, technical capability, pollution control, risk reduction, and service, also play a significant role in the process of selecting suppliers. The results indicate that managers in today's competitive and industrial markets should redirect their attention from traditional criteria to those contributing to the sustainability and improvement of their systems' performance.Originality/Value: Reading the results of this research can help Industrial and organizational managers evaluate the potential suppliers of their companies based on the viability and Industry 5.0 dimensions and select the best ones, which can significantly improve the performance and efficiency of their businesses.

Implementing the right strategies in the agricultural supply chain in the supply of seeds, pesticides, fertilizer, energy, fuel and agricultural mechanization tools and equipment has a great role in increasing agricultural productivity. The main purpose of the study is to rank and evaluate alternatives in choosing a sustainable fertilizer supplier in the agricultural supply chain by using AHP-F and ARAS-F techniques. In an environment of uncertainty and complex supply chain structure, multi-criteria decision making (MCDM) methods are widely used to solve supplier selection problems. In this study, the importance levels and weights of the criteria in the selection of sustainable fertilizer suppliers were measured by the AHP-F method. The criteria that are important for fertilizer supplier selection were evaluated by taking expert opinions, the uncertain and uncertain opinions of the decision makers were modeled with the AHP-F approach and the weights of the criteria were determined. Among the criteria, resource consumption (FSC05) has the highest weight. Then, alternative rankings were obtained with the ARAS-F method. Fertilizer supplier alternatives in the agricultural supply chain were ranked with the ARAS-F method, using the criterion weights found with AHP-F. In the ranking of alternatives, alternative fertilizer supplier FS03 ranked first with the highest value. This study provides a resource for businesses and other stakeholders to make decisions regarding sustainable fertilizer supplier selection.

In recent years, the two-sided matching theory has been applied in various fields. Its influence in the engineering field is becoming more and more significant. In the construction engineering context, from the contractor’s perspective as the decision-maker, the mutual matching between project bidding and subcontractors is a complex and uncertain process. A suitable matching method needs to be selected according to the particular situation. Since this study requires considering both the highest satisfaction of parties and the weight of individual fulfillment, we use the two-sided matching theory to address the mutual matching between the engineering project bidding and subcontractor. At the same time, the Ordinal Priority Approach (OPA) is employed to determine the weights and evaluate the indicators of both parties and then determine the preference between the two parties, effectively avoiding the deviation caused by subjective influence in the process. As a result, a bilateral matching model is proposed with the highest satisfaction and considering individual satisfaction. Finally, an example is presented to verify the feasibility and effectiveness of the proposed model.

Purpose: The COVID-19 pandemic has led to a significant crisis in society's health, industries, and businesses. In this regard, the medical devices industry has played an important role in crisis management and providing healthcare and has faced several major challenges in supplying raw materials, production activities, and distribution activities due to the disruptions caused by the pandemic. One of the critically important issues in the medical devices supply chain is supplier selection. Hence, this research investigates the supplier selection problem considering the emerging concepts that have dramatically attracted the attention of researchers after the COVID-19 pandemic, namely viability and Industry 5.0. Methodology: In this study, a hybrid fuzzy decision-making approach is developed to investigate the viable supplier selection problem considering the Industry 5.0 dimensions. In this regard, in the first stage, according to the literature and experts, the main indicators of the research problem are extracted, and their weights are calculated using the fuzzy best-worst method. In the next stage, the feasible suppliers are evaluated by employing the fuzzy VIKOR method. Also, to show the robustness and validation of the proposed approach, its results are compared with those of traditional approaches. Findings: In this study, a list of indicators, including six aspects and 34 criteria, is provided for the research problem based on its nature, and their importance has been computed. Based on the outputs, the general metric is the most important aspect, and the human-centricity metric is the least significant. Also, the results show that in addition to the general criteria, such as cost and quality, other criteria, such as reliability, technical capability, pollution control, risk reduction, and service, also play a significant role in the process of selecting suppliers. The results indicate that managers in today's competitive and industrial markets should redirect their attention from traditional criteria to those contributing to the sustainability and improvement of their systems' performance. Originality/Value: Reading the results of this research can help Industrial and organizational managers evaluate the potential suppliers of their companies based on the viability and Industry 5.0 dimensions and select the best ones, which can significantly improve the performance and efficiency of their businesses. [ABSTRACT FROM AUTHOR]

This study addresses the multi-item, multi-sourcing supplier-selection and order-allocation problem. We propose an iterative procurement combinatorial auction mechanism that aims to reveal the suppliers' minimum acceptable selling prices and assign orders optimally. Suppliers use a flexible bidding language to submit procurement bids. The buyer solves a Mixed Integer Non-linear Programming (MINLP) model to determine the winning bids for the current auction iteration. We introduce a buyer's profit-improvement factor that constrains the suppliers to reduce their selling prices in subsequent bids. Moreover, this factor enables the buyer to strike a balance between computational effort and optimality gap. We develop a separate MINLP model for updating the suppliers' bids while satisfying the buyer's profit-improvement constraint. If none of the suppliers can find a feasible solution, the buyer reduces the profit-improvement factor until a pre-determined threshold is reached. A randomly generated numerical example is used to illustrate the proposed mechanism. In this example, the buyer's profit improved by as much as 118% compared to a single-round auction. The experimental results show that the proposed mechanism is most effective in competitive environments with several suppliers and comparable costs. These results reinforce the importance of fostering competition and diversification in a supply chain. [ABSTRACT FROM AUTHOR]

Copyright of Pamukkale University Journal of Business Research / Pamukkale Üniversitesi İşletme Araştırmaları Dergisi is the property of Pamukkale University Journal of Business Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: With the emergence of Industry 4.0, supply chains have undergone fundamental changes, profoundly impacting on production and supply processes in various industries. The main goal of this study is to identify and evaluate the important criteria for selecting suppliers in Iran's steel industry, taking into account industry 4.0 technologies and their role in supplier selection. Methodology: By studying and reviewing previous researches, the study first identifies the criteria. Then, the Fuzzy Delphi method is employed to confirm these criteria. Weighting and prioritization of criteria is done by using Fuzzy FUCOM method. Findings: The results show that criteria such as quality, price, how to use the internet of things, lead time, settlement method and digital cooperation play an important role in choosing a supplier in Iran's steel industry, considering the role of Industry 4.0 technologies. Originality/Value: For the first time, this research examines the supplier selection criteria in Iran's steel industry, focusing on the technologies and concepts of Industry 4.0. Because decision-making in the real world is often faced with uncertainty, Employing the Fuzzy Delphi and Fuzzy FUCOM methods in this article will make decisions in the selection of suppliers in Industry 4.0 enables more accurate and reliable decision-making. [ABSTRACT FROM AUTHOR]

Copyright of Informatics Control Measurement in Economy & Environment Protection / Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska is the property of Lublin University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: Given the competitive and globalized nature of markets, availability has become a crucial aspect of product design in recent decades. Modern availability includes functional requirements, adherence to standards, design considerations, predictability of availability, modeling, and evaluation. One objective of availability is to design systems with maximum accessibility. System availability is often improved by enhancing the availability of individual components or by allocating redundant components. These improvements are achieved through better materials, improved manufacturing processes, and the application of design principles. Method: This paper introduces an innovative approach to optimizing multiple parallel-series multistate systems. Unlike traditional methods that focus on optimizing a single system, this approach simultaneously optimizes multiple systems to enhance their overall efficiency and performance. These systems contain parallel subsystems with multi-state components that can operate in various states, providing different performance outcomes. A significant aspect of this model is the impact of multi-stage failure rates on the systems, analyzed through state diagrams. The model also considers various assumptions, including the capability to select suppliers with different conditions and constraints. Additionally, the effects of technical and organizational activities on continuous optimization intervals are analyzed. The model is refined using a genetic algorithm, showing considerable improvements in system performance. Results and discussion: An optimization mathematical model is presented to address the problem under specified assumptions. A numerical example is provided where the state transition distribution function is exponential, and technical and organizational activities have varying performance intensities. In this example, the performance rate of each subsystem equals the sum of the performance rates of its components, and the system's performance is at least as good as the minimum performance rate of its subsystems. Based on these assumptions, the system's availability probability and cost can be calculated using the model's objective function. The example problems are then solved using a genetic algorithm, and the results are reported. Conclusions: Recent research indicates that scholars in the field of redundancy allocation models for both binary and multi-state systems have continuously aimed to make these problems more realistic by incorporating new assumptions or eliminating simplifying ones. These efforts underscore the importance of developing mathematical optimization models that consider all system conditions and constraints, addressing the broader issues faced by decision-makers. Our research demonstrates that expanding the dimensions of optimization problems related to redundancy allocation can produce models that better reflect real-world conditions. [ABSTRACT FROM AUTHOR]