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The circular economy (CE) seeks to maintain products and materials at their highest utility and value. The organisational and governmental policy have seised onto the CE philosophy to advance socio-economic and environmental development. CE remains an essentially contested concept – making its utilisation as a foundation for managerial and policy decisions challenging. Circularity assessment has not been systematically adopted, especially within supply chain management. Using critical scholarly and practical evidential foundation, we proposed a comprehensive set of metrics that can be utilised in supplier selection, monitoring, and development for circularity. These metrics include the macro, meso, and micro levels. A group decision-making method integrating best-worst method (BWM), regret theory (RT), and dual hesitant fuzzy sets (DHFS) for circular economy and circularity (CEC) supplier evaluation and selection is introduced – providing instrumental value for the identified metrics typology. The proposed BWM-DHFE-RT integrative analytical method can accommodate decisionmaker psychological behaviour under uncertainty while simultaneously capturing divergent or conflicting opinions of different decision-makers. An illustrative business scenario is utilised to demonstrate the application of the proposed method. Though the proposed CE performance metrics and methodology are used for CEC supplier management reasons they have broader applicability. Future research and application directions are discussed. [ABSTRACT FROM AUTHOR]

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]

Given that medical equipment is directly linked to patient well-being, the meticulous choice of a reliable supplier is of paramount importance. Within the context of intensive care units, health technology assessment and the process of making healthcare decisions are grounded in various criteria, substantiated by evidence, and influenced by the diverse viewpoints of involved stakeholders. Multi-criteria decision analysis (MCDA) provides a promising framework for organizing this procedure and considering a range of perspectives. With the increasing number of intensive care units in cities, the main aim of this study is to investigate the perspectives and preferences of stakeholders in intensive care units when evaluating health interventions. This examination utilized a multi-criteria approach to assess a mechanic ventilator as a case study. In the research on the ventilation device procurement problem for the 3rd Level Training and Research Hospital, fuzzy VIKOR and fuzzy EDAS methods were used to select the most suitable one among 4 alternative companies evaluated with 45 criteria. According to the results, both methods identified alternative 3 as the best option and contributed to the literature on the application of multi-criteria decision-making techniques. [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]

The choice of green-resilient supplier selection as a complex multi-criterion decision-making problem that often involves some uncertain situations, has become a key issue in the development of electronics manufacturing. Decision-making models supporting the assessment of supply chain performance must consider comprehensive elements in the supply network, covering not only cost and technical capabilities, but also greenness and resilience aspects. Therefore, in order to fill the research gap, the present study proposed an integrated decision-making method based on the fuzzy set theory and data envelopment analysis (DEA) approach for supplier selection in a supply chain network to handle uncertainties caused by decision makers 'subjective judgments which could reduce the supply chain costs and environmental impact and, moreover, extend the value of resilience. A case study was used to validate the applicability of the proposed model. The sensitivity analysis was employed to examine the impact of the proposed model. Furthermore, the proposed DEA-based model has been compared with a supplier selection Fuzzy inference system based method in the literature to show its validation. The results indicated the proposed fuzzy DEA method can provide a reliable and consensus decision-making framework for enterprises to select green-resilient suppliers in the electronic industry of household appliances. [ABSTRACT FROM AUTHOR]

Medical tourism refers to people traveling abroad to benefit from both medical and tourism services. The delivery of medical tourism services requires the simultaneous participation of multiple service providers. This study focuses on the tactical decision level of supply chain planning and aims to evaluate and select the providers of the services that are offered in a medical tourism service package by a medical assistance company and allocate the available capacity of the preselected providers to the medical tourist demand. Thus, it examines the selection of individual service providers and the allocation of capacity based on medical tourist preferences from a business-to-business perspective, namely a supplier selection and order allocation problem. To address this problem, we suggest a two-step process where a fuzzy rule-based inference system is used to evaluate service providers and determine their collaboration scores, and subsequently a fuzzy linear model is employed to select providers and determine the capacity allocation of service requests while maximizing the overall collaboration scores. The applicability of the proposed approach is presented by a quantitative application for dental tourism in Turkey. The suggested approach serves as a guide for scholars as well as for medical tourism practitioners engaged in the process of configuring a medical tourism service network. [ABSTRACT FROM AUTHOR]

Selecting a supplier is a critical strategic decision for supply chain management in today's global context. The process involves evaluating suppliers based on core competencies, pricing, delivery timeframes, location, data gathering, and related risks. Suppliers play a crucial role in an organization's profitability and stability. Finding the most optimal supplier can help industries reduce material expenses and maintain their competitive advantage. The supplier not only impacts the organization's profit margin but also its economic strength. Choosing a supplier requires considering qualitative and quantitative elements, making it a decision issue with several criteria. This study aims to create and evaluate a supplier selection model using the analytical hierarchy approach, focusing on a specific case study. When selecting the best supplier, it is crucial to consider tangible and intangible elements that may conflict. The supplier selection process considers several criteria, including qualitative and quantitative variables. The proposed methodology involved a literature review and informal interviews with industry experts and academics to establish the selection criteria. "Quality Supplier Corporation" was chosen due to the paramount importance of their quality. This research will comprehensively analyze several criteria to identify suppliers accurately. [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]

Conflicts and wars profoundly impact infrastructure, exacerbating the adversity already caused by natural disasters. Therefore, it is imperative that the reconstruction process be both effective and efficient to expedite a return to normalcy. This study aims to enhance the efficacy of reconstruction efforts through improved construction supplier evaluation and selection. It introduces an innovative hybrid multi-objective decision-making model that integrates a broad spectrum of economic, technical, and humanitarian criteria. The model is designed to optimally select and assign construction suppliers in regions affected by human and natural conflicts and crises. Fifteen criteria have been incorporated into the evaluation process to validate its effectiveness and maximize its contribution to local communities. This methodology streamlines decision-making and enhances transparency in conflict zones, aligning with the interests of all stakeholders. The study incorporates advanced methodologies, including Fuzzy Goal Programming (F-GP), Geographic Information System (GIS)-based Risk Assessment, and Fuzzy Analytic Hierarchy Process (F-AHP), leveraging real-world data and a case study. Additionally, a sensitivity analysis examines the impact of varying inputs on the model's output. The findings attest to the model's utility in conflict-affected regions and its potential applicability in stable settings. [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]

It is widely recognized that supplier selection is a critical issue in supply chain management. In the context of supplier selection in various phases of supply chain, a company has to take decisions regarding the effective selection of suppliers to procure raw materials, process it and finally deliver the finished products efficiently. Deciding on the number of suppliers at each stage of the supply chain network is crucial. Recently, optimizing system reliability has also emerged as a popular research area. Notably, the structure of the system reliability optimization problem closely resembles that of the supplier selection problem in supply chains. This paper explores the application of system reliability optimization concepts in supplier selection for make-to-order supply chains. Initially, supplier reliability is defined, and the supplier selection problem is formulated as a reliability maximization problem for a parallel–series system, constrained by cost and the maximum number of suppliers at each stage. A heuristic technique is developed to solve this problem, demonstrated with numerical examples. The solution identifies the selected suppliers at each stage and the corresponding minimum total supply chain cost. Analysis of these solutions reveals practical insights. Finally, sensitivity analyses concerning various heuristic and model parameters are conducted and presented graphically. [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: This study aims to focus on sustainable supplier selection in a construction company considering a new multi-criteria decision-making (MCDM) method based on dominance-based rough set analysis. The inclusion of sustainability concept in industrial supply chains has started gaining momentum due to increased environmental protection awareness and social obligations. The selection of sustainable suppliers marks the first step toward accomplishing this objective. The problem of selecting the right suppliers fulfilling the sustainable requirements is a major MCDM problem since various conflicting factors are underplay in the selection process. The decision-makers are often confronted with inconsistent situations forcing them to make imprecise and vague decisions. Design/methodology/approach: This paper presents a new method based on dominance-based rough sets for the selection of right suppliers based on sustainable performance criteria relying on the triple bottom line approach. The method applied has its distinct advantages by providing more transparency in dealing with the preference information provided by the decision-makers and is thus found to be more intuitive and appealing as a performance measurement tool. Findings: The technique is easy to apply using "jrank" software package and devises results in the form of decision rules and ranking that further assist the decision-makers in making an informed decision that increases credibility in the decision-making process. Originality/value: The novelty of this study of its kind is that uses the dominance-based rough set approach for a sustainable supplier selection process. [ABSTRACT FROM AUTHOR]

Due to the short-term management periods and frequent changes in leadership, especially in the oil industry in some developing countries, suppliers often prioritize profitability, neglecting the crucial impacts on the environment and society. As organization profitability has a direct relationship to its market share, this study proposes a quantitative analysis for examining the relationship between corporate social responsibility changes and market share of suppliers. To do this, this research investigates the impact of corporate social responsibility on supplier selection, order allocation, and supplier market share in the oil industry. Accordingly, this study proposes a multi-objective optimization model to address the challenges of integrating corporate social responsibility in supplier selection and order allocation problem and uses both traditional and corporate social responsibility criteria. The Best-Worth Method is applied to determine the weights of criteria. The study is validated with a numerical example and a real case to compare the amounts allocated to suppliers and their market share in two models: one that includes corporate social responsibility and one that does not. The findings indicate that there is no significant positive relationship between corporate social responsibility consideration and the improvement of suppliers' market share, at least in the short-term. [ABSTRACT FROM AUTHOR]

Under the constraints of uncertainty and carbon emission policies, reasonable and flexible supplier selection and order allocation can minimize the total cost, which is of great significance to the supply chain. We study sustainable supplier selection and order allocation problems with multiple periods, multiple suppliers, and multiple products under a carbon cap‐and‐trade policy. We exploit distributionally robust versions of supplier selection and order allocation problems with an explicit distributional uncertainty set and derive computationally tractable reformulations of the distributionally robust optimization model with chance constraints by cone duality theory and conditional value‐at‐risk measure. Numerical experiments show that the proposed distributionally robust optimization models with nice computational tractability and probability guarantees are superior to the existing distributionally robust chance‐constrained optimization models in terms of cost and risk aversion. Relevant management insights on different carbon emission regulations and the application of distributionally robust optimization models are provided for policymakers and enterprises' decision‐makers based on the derived results. [ABSTRACT FROM AUTHOR]

Over the past few years, supply chains have become globalised and multi-tiered. These factors complicate their structure as the focal company is responsible for the problems experienced at each stage in the multi-tier supply chain. The critical issue for focal companies in managing their multi-tier supply chain is to adopt sustainability standards. One of the study's contributions is the role of weight determination in the tiers and evaluation of alternative suppliers in facilitating the effective management of multi-tier supply chains, especially in complex industries such as the food industry. The other contribution of this study is its proposition for a multi-stage framework based on sustainability concerns. The study identifies 14 criteria for companies in diffusing sustainability standards throughout multi-tier supply chains. The weights of these criteria are determined for each tier of the food supply chain using the Best Worst Method. Results show that the 'environmental' criteria are most important for supply chain tiers in the food industry. Supply chain visibility/traceability is the most important criteria for the first tier, followed by environmental responsibilities, for the second and environmental competencies for the third. Finally, for the food company, PROMETHEE is used to evaluate three alternative suppliers for each tier. [ABSTRACT FROM AUTHOR]

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.

The carbon neutral policy restrains the carbon emissions of product family manufacturers, which incurs additional costs for manufacturers. In previous studies of product family supplier selection, the dilemma of carbon neutrality faced by manufacturers was rarely researched. Therefore, this study puts forward an intelligent optimisation method for product family supplier selection under carbon neutral constraints, which assists manufacturers in satisfying carbon neutrality constraints while minimising total production costs. Firstly, the data representation models of product family are constructed by using information ontology technology, and then the candidate suppliers that meet the manufacturer's performance requirements are mined by semantic similarity. Secondly, by calculating the configuration cost and carbon footprint of the product family, the configuration cost-carbon neutral cost model (CC-CNC model) of the manufacturer is established with the goal of minimising the manufacturer's total cost, and then the genetic algorithm is employed to solve the model, thus the optimal supplier selection scheme is mined. The feasibility of the proposed method is demonstrated by supplier selection for water-cooled fan product family, which guides the manufacturer to minimise the cost by selecting the best supplier scheme and taking reasonable carbon neutral measures, contributing to the manufacturer's low-carbon production and economic benefits. [ABSTRACT FROM AUTHOR]

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]