Search Results (237)

Supplier selection is a crucial factor for ensuring compliance with the circular economy’s principles. Existing approaches often overlook the integration of circularity and risk assessment in supplier evaluation, limiting their effectiveness in achieving sustainability goals. This paper addresses this gap by applying suitable criteria and proposing a structured decision-making model for circular supplier selection. The model innovatively integrates Multi-Criteria Decision Analysis (MCDA) techniques with risk evaluation, providing a comprehensive framework for assessing suppliers in circular supply chains. By advancing the theoretical understanding of circular supplier selection, this research contributes to both academia and practice, reinforcing the alignment between supply chain decision-making and the Sustainable Development Goal (SDG), particularly Target 12.5. Full article

Supplier selection is one of the critical processes that entail multiple complex deliberations. The selection of an appropriate alternative supplier is a highly intricate process, primarily due to there being multiple criteria which are exceptionally subjective. This paper aims to develop a practical framework for choosing a suitable precast supplier by integrating the Value Engineering (VE) concept, Stepwise Weight Assessment Ratio Analysis (SWARA), and the Weighted Aggregated Sum Product Assessment (WASPAS) technique. This paper introduces a novel method to estimate the quality weights of alternative suppliers’ criteria (CQW) by linking factory capacity with the coefficients of the nine significant criteria, computed using principal component analysis (PCA). None of the formal studies make this link directly. The framework’s findings were validated by comparing its results with an expert assessment of five Saudi supplier alternatives. The results revealed that the framework’s results agree with the expert’s judgment. The method of payment criterion received the highest weight, indicating that it was considered the most important of the nine criteria identified. Combining PCA and VE with the WASPAS technique resulted in an unprecedentedly effective selection tool for precast suppliers. Full article

The development of technology has influenced agricultural production and the establishment of the Agriculture 4.0 system in practice. This research is focused on the selection of equipment and machinery suppliers for the needs of the MAMEX Company. When selecting suppliers, an approach based on the application of an intuitionistic fuzzy set for decision-making was used. This approach allows the uncertainty present in decision-making to be incorporated, considered, and, hopefully, reduced in order to make a final decision on which of the observed suppliers is the most suitable for this company. Ten criteria were used that enable the application of sustainability in the supply chain. Eight local suppliers of equipment and machinery were observed with these criteria. The results obtained by applying the SWARA (Step-wise Weight Assessment Ratio Analysis) method showed that the most important criterion for selecting suppliers is the reliability and quality of equipment and machinery, while the results of the CORASO (COmpromise Ranking from Alternative Solutions) method showed that the SUP2 supplier is the best choice for establishing partnership relations with the MAMEX company. This supplier should help the MAMEX company improve its business and achieve better results in the market. The contribution of this research is to improve the application of intuitionistic fuzzy sets in decision-making, and to emphasize the importance of equipment and machinery in agricultural production in the Agriculture 4.0 system. Full article

Production involves processes such as raw material extraction, energy consumption, and waste management, which can lead to significant environmental consequences. Therefore, supplier selection based not only on technical performance but also on environmental sustainability criteria has become a fundamental component of eco-friendly manufacturing strategies. Moreover, in the selection of electric vehicle batteries, it is essential to consider customer demands alongside environmental factors. Accordingly, selected suppliers should fulfill company expectations while also reflecting the “voice” of the customer. The objective of this study is to propose an integrated approach for green supplier selection by taking into account various environmental performance requirements and criteria. The proposed approach evaluates battery suppliers with respect to both customer requirements and green criteria. To construct the relational structure, the DEMATEL method was employed to analyze the interrelationships among customer requirements (CRs). Subsequently, the Quality Function Deployment (QFD) model was used to establish a central relational matrix that captures the degree of correlation between each pair of supplier selection criteria and CRs. Finally, to evaluate and rank alternative suppliers, the Interval Type-2 Fuzzy VIKOR (IT2 F-VIKOR) method was applied. The hybrid approach proposed by us, integrating DEMATEL, QFD, and IT2 F-VIKOR, offers significant improvements over traditional methods. Unlike previous approaches that focus independently on customer preferences or supplier criteria, our model provides a unified evaluation by considering both dimensions simultaneously. Furthermore, the use of Interval Type-2 Fuzzy Logic enables the model to better manage uncertainty and ambiguity in expert judgments, yielding more reliable results compared to conventional fuzzy approaches. Additionally, the applicability of the model has been demonstrated through a real-world case study, confirming its practical relevance and robustness in the selection of green suppliers for electric vehicle battery procurement. Full article

Starting Active Materials for Synthesis (SAMS) represents a critical stage in drug manufacturing, directly influencing the microbiological quality and safety of the final product. The introduction of SAMS marks the point where Good Manufacturing Practices (GMP) begin to apply, which are essential for ensuring sterility and preventing microbial contamination during the synthesis process. However, defining the exact point in the process that qualifies as the SAMS is subject to uncertainties, as earlier stages are not always governed by stringent GMP standards. The regulatory differences between various countries further contribute to this issue. This study explores the implications of SAMS selection and use in relation to sterility and infection control, analyzing the guidelines of major Regulatory Authorities and comparing their approaches to GMP. Regulations from several international regulatory agencies were examined, with a particular focus on microbiological control measures and infection protection in the SAMS manufacturing process. The analysis focused on the microbiological control requirements and safety measures applicable to the stages preceding the introduction of SAMS into the production of the final Active Pharmaceutical Ingredients (APIs). Documents published between 2015 and 2025 were included based on predefined criteria regarding relevance, accessibility, and regulatory authority. The analysis revealed significant discrepancies between regulations regarding the definition and management of SAMS. In particular, the regulations in Mexico and India have notable gaps, failing to provide clear guidelines on SAMS sterility and protection against infectious contamination. Conversely, China has introduced risk-based approaches and early-stage microbiological controls, especially for sterile products, aligning with international standards. The European Medicines Agency (EMA), the U.S. Food and Drug Administration (FDA), the Pharmaceutical Inspection Co-operation Scheme (PIC/S), and the World Health Organization (WHO) have well-established systems for microbiological quality control of SAMS, including rigorous measures for the validation of suppliers and risk management to ensure that SAMS does not compromise the microbiological safety of the final product. The regulations in Brazil and Canada introduce additional measures to protect the microbiological quality of SAMS, with specifications for contamination control and certification of critical stages. The lack of a harmonized language for the definition of SAMS, coupled with a fragmented regulatory framework, presents a challenge for infection protection in pharmaceutical manufacturing. Key issues include the absence of specific regulations for stages prior to the introduction of SAMS and the lack of standards for inspections related to these stages. A desirable solution would be the mandatory extension of GMPs to the stages before SAMS introduction, with centralized control to ensure sterility and protection against infection throughout the entire manufacturing process. Full article

Effective management of the hydrogen supply chain (HSC), starting with supplier selection, is crucial for advancing the hydrogen industry and economy. Supplier selection, a complex Multi-Criteria Decision Analysis (MCDA) problem in an inherently uncertain environment, requires careful consideration. This study proposes a novel hybrid MCDA framework that integrates the Bayesian Best–Worst Method (BWM), Fuzzy Analytic Hierarchy Process (AHP), and Entropy Weight Method (EWM) for robust criteria weighting, which is aggregated using a game theory-based model to resolve inconsistencies and capture the complementary strengths of each technique. Next, the globally weighted criteria, emphasizing sustainability performance and techno-risk considerations, are used in the TODIM method grounded in prospect theory to rank hydrogen suppliers. Numerical experiments demonstrate the approach’s ability to enhance decision robustness compared to standalone MCDA methods. The comparative evaluation and sensitivity analysis confirm the stability and reliability of the proposed method, offering valuable insights for strategic supplier selection in the evolving hydrogen landscape in the HSC. Full article

Suppliers nowadays can be directly approached in business-to-business (B2B) E-commerce websites. This makes the product and service information of certain suppliers accessible in online reviews. Therefore, online reviews have become important for B2B supplier evaluation and selection. Recently, the sustainability of food packaging has attracted increasing attention from companies and consumers. This study developed a novel multi-criteria decision making (MCDM) method called Percentage Assessment with Synergistic Comparisons And Aggregated Ranks (PASCAAR) to support the selection of sustainable food package suppliers based on online review information in B2B E-commerce websites. Such a method used three different percentage comparisons between alternatives and the minimal options, and then aggregates the comparisons with their ranks. This study confirmed the effectiveness of PASCAAR by applying it to a case study to select the supplier of sustainable food packages (i.e., biodegradable food containers) from six candidates in the B2B E-commerce website by considering multi-dimensional online review information and their own product properties. Using PASCAAR, this study obtained the outcome that the third candidate is the most suitable one, as quantitative results indicate this supplier has the highest PASCAAR score. Based on the results, this study further conducted thorough sensitivity tests to validate the results. It can be found that, compared with the classical MCDM methods in measuring the performance of alternatives and aggregating evaluation scores, the PASCAAR method can have more robust and informative results. This study also developed a PASCAAR Solver to enable easy implementation of this method. This study contributes to the existing literature by providing new ranking and aggregation ideas in MCDM and can offer practitioners a more informative and highly actionable method for supplier selection and decision support system development by utilizing online review information. Full article

Sustainable development has become the universal consensus among nations worldwide, with society increasingly recognizing the importance of fulfilling ESG responsibilities. This recognition has begun to influence companies’ supplier evaluation and selection behaviors. Meanwhile, integrating AI and IoT technologies has brought about new possibilities for enhancing the intelligence of supply chain management. Firstly, the concept of AIoT suppliers was defined, and evaluation and selection criteria were discussed and proposed. Secondly, the impact of ESG factors on the evaluation criteria of AIoT suppliers was explored, while the value of AIoT technology empowerment from an ESG perspective was analyzed. According to a case study of new energy vehicle supply chain management, the application of AIoT technology can assist companies in improving their ESG management capabilities and performance across multiple dimensions, thereby contributing to the long-term sustainable development of companies. Full article

The evaluation and selection of green suppliers is an important way for enterprises to maintain sustainable development and help them reduce costs and increase efficiency. This paper proposes a multi-criteria decision-making (MCDM) model in an interval intuitive fuzzy environment. This model uses interval-valued intuitive uncertainty language number (IVIULN) to describe expert evaluation of qualitative indices. Expert weights are determined through expert social networks, and an improved aggregation operator is proposed to aggregate the evaluation information. The proposed operator can ensure the stability of the results even in the case of extreme values. Subsequently, considering a large number of mutually related indices, a novel teaching-learning-based optimization (NTLBO) algorithm is used to identify the value of $\lambda$-fuzzy measures. This algorithm improves the teaching stage and proposes the idea of teaching students in accordance with their aptitude, introduces precision parameters, and adds a self-study stage. It has been verified by numerical examples that it is far superior to commonly used heuristic algorithms in terms of algorithm accuracy and run time. Finally, the alternatives are ranked by bi-direction Shapley–Choquet integral. The model’s effectiveness is demonstrated through a case study. This paper also examines the impact of key parameters on the results through sensitivity analysis. Full article

The increasing demand for sustainable business practices has highlighted the need for a robust and risk-aware green supplier selection framework. Traditional supplier evaluation methods primarily focus on cost, quality, and delivery performance but often fail to incorporate sustainability and risk factors effectively. This study proposes an improved AHP-FMEA method that integrates the Analytic Hierarchy Process (AHP), entropy weight method, and Failure Mode and Effects Analysis (FMEA) to enhance decision-making in green supplier selection. The AHP method determines subjective criteria weights, while the entropy method adjusts these weights objectively to reduce bias. The FMEA approach incorporates risk assessment by identifying and quantifying potential supplier failures, ensuring a more comprehensive evaluation. A case study is conducted to validate the proposed model, comparing it with classical AHP and AHP-Entropy methods. The results show that incorporating risk factors significantly influences supplier ranking, demonstrating the model’s ability to provide a more scientific, objective, and risk-conscious evaluation. The proposed approach enhances the accuracy and reliability of green supplier selection, making it a valuable tool for sustainable supply chain management. Full article

Background: Nowadays, traditional supply chain management (SCM) processes are undergoing a profound transformation enabled by advanced technologies derived from Industry 4.0. The rapid adoption of these technologies has led to the emergence of smart SCM, which integrates modern technologies in sourcing, production, distribution, and sales. Supplier evaluation and selection (SES) in smart SCM is a strategic decision impacting the entire supply chain. Organizations must also incorporate sustainability principles into their strategic decisions alongside smart production and efficiency. Methods: The main objective of this study is to develop a multi-criteria decision-making (MCDM) approach under uncertainty to address sustainable–smart supplier evaluation and selection problems. The approach integrates polygonal fuzzy numbers (POFNs), Z-numbers, expected interval (EI), and expected value (EV) to develop methods such as the logarithmic methodology of additive weights (LMAW) and the weighted aggregated sum product assessment (WASPAS), which are used to prioritize criteria and rank suppliers. Furthermore, novel approaches are introduced for calculating membership functions, $\mathcal{a}$-cut formulations, and the crispification process in POFNs. Results: A real case study in the home appliance industry revealed that cost reduction through smart technologies, green and smart logistics and manufacturing, and smart working environments are the most critical evaluation criteria. Suppliers three and four, excelling in these areas, were identified as top suppliers. Conclusions: The proposed approaches effectively addressed hybrid uncertainty in SES problems within smart SCM. Finally, sensitivity and comparative analysis confirmed their robustness and reliability. Full article

Digital supply chains (DSCs) are value-driven and collaborative digital systems designed to generate business value for firms through various innovative technologies. Today, we are witnessing companies transitioning from traditional supply chain models to DSCs through digital technologies. The effective selection of digital suppliers during these digital transformation processes is a strategic research topic. Additionally, factors such as the proliferation of information and communication technologies, globalization, and the pandemic have contributed to the expansion of e-commerce platforms. In this rapid growth phase, identifying the right supplier is crucial for the success of e-commerce sites. This study aims to develop an innovative, integrated, and comprehensive decision-making methodology to assist e-commerce platforms in selecting appropriate suppliers for their DSCs. To achieve this, an extended fuzzy VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method is tested, where criteria and alternative evaluations made by decision-makers (DMs) are characterized by interval-valued intuitionistic fuzzy numbers (IVIFNs). The proposed decision mechanism is tested on the DSS problem of an e-commerce platform specializing in household products. Findings of the application, which uses three experts’ opinion to evaluate four digital suppliers based on the seven criteria, are discussed to help e-commerce sites conduct the DSS process more effectively. Full article

Supplier selection plays a pivotal role in the mining industry, forming a key component of the supply chain management. It has been established that the integration of sustainability and resilience into this process can significantly enhance the industry’s ability to withstand economic, environmental, and social shocks. Despite a large body of literature investigating supplier selection, there is a notable gap in research specifically addressing the incorporation of sustainability and resilience criteria in the mining industry. The objective of this research is to bridge this knowledge gap and contribute to the understanding of sustainable and resilient supplier selection in the mining industry. A constructive research approach was employed, identifying both practical and theoretical problems and proposing a construction—a mathematical model. This model was developed in collaboration with industry key actors, ensuring its practical applicability and validity. The main result of this research is an optimization mathematical programming model that allows practitioners to evaluate and select suppliers considering both sustainability and resilience criteria. The model facilitates a comprehensive assessment of suppliers, incorporating a wide range of factors beyond cost, including environmental impact, social responsibility, and the ability to maintain supply under various potential disruptions. Full article

Uncertainty exists in reality decision-making problems. Therefore, fuzzy theories, fuzzy intervals, intuitionistic fuzzy, and Z-numbers have been proposed and successfully applied to multi-criteria decision-making (MCDM). However, since the information presented in the Z-numbers method is the subjective opinion of the decision-maker, the problem of overestimating or underestimating the reliability of the information may occur in both individual and group decision-making. The Extended Z-numbers (Z^E-numbers) method was proposed in 2021 to solve this problem; hence, the decision-making process no longer relies on subjective opinions only but seeks external experts related to the problem to further vote on the evaluation value given by the internal decision-maker, in order to modify the information’s reliability, and thus to obtain a more realistic result. This paper combines the Z^E-numbers method with the improved Elimination et Choix Traduisant la Realite II (ELECTRE II) proposed in 2022 and proposes a new MCDM method based on Z^E-numbers, named Z^E-ELECTRE II. The green supplier selection problem was used as an illustrative example. Meanwhile, the close analysis in this paper examines two primary dimensions of variability: (1) simulation of external expert voting situations to analyze the variations in information reliability and decision-making results and to cross-compare them with other MCDM methods; (2) investigation of the impact of internal preferences, as reflected through systematic adjustments to the weights of the evaluation criteria. The results show that uncertainty of information, reliability, and the perspectives of different decision-makers and expert groups can be considered through Z^E-numbers. The proposed Z^E-ELECTRE II is applicable to group decision-making, validates the robustness of the process, and is suitable for dynamic decision-making under varying decision-maker preferences. Furthermore, using the Z^E-numbers along with the MCDM method can obtain more flexibility and more reliable results. Full article

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. Full article