Search Results (18)

Produce supply chains play a critical role in ensuring fruits and vegetables reach consumers efficiently, affordably, and at optimal freshness. In recent decades, hub-and-spoke network models have emerged as valuable tools for optimizing sustainable cold food supply chains. Traditional optimization efforts typically focus on removing inefficiencies, minimizing lead times, refining inventory management, strengthening supplier relationships, and leveraging technological advancements for better visibility and control. However, the majority of models rely on deterministic approaches that overlook the inherent uncertainties of crop yields, which are further intensified by climate variability. Rising atmospheric CO₂ concentrations, along with shifting temperature patterns and extreme weather events, have a substantial effect on crop productivity and availability. Such uncertainties can prompt distributors to seek alternative sources, increasing costs due to supply chain reconfiguration. This research introduces a stochastic hub-and-spoke network optimization model specifically designed to minimize transportation expenses by determining optimal distribution routes that explicitly account for climate variability effects on crop yields. A use case involving a cold food supply chain (CFSC) was carried out using several weather scenarios based on climate models and real soil data for California. Strawberries were selected as a representative crop, given California’s leading role in strawberry production. Simulation results show that scenarios characterized by increased rainfall during growing seasons result in increased yields, allowing distributors to reduce transportation costs by sourcing from nearby farms. Conversely, scenarios with reduced rainfall and lower yields require sourcing from more distant locations, thereby increasing transportation costs. Nonetheless, supply chain configurations may vary depending on the choice of climate models or weather prediction sources, highlighting the importance of regularly updating scenario inputs to ensure robust planning. This tool aids decision-making by planning climate-resilient supply chains, enhancing preparedness and responsiveness to future climate-related disruptions. Full article

Supplier selection in the medical device manufacturing (MDM) industry significantly affects quality, operational efficiency, and overall organizational performance. Due to the industry’s dependence on advanced technologies and rigorous regulatory standards, identifying critical success factors (CSF) for selecting suppliers is essential. This study aims to analyze relationships among critical success factors (CSF) influencing supplier selection and their influence on supplier quality and the performance outcomes of MDM companies. A structured survey was conducted among MDM companies in Mexico, and the collected data were analyzed through exploratory and confirmatory factor analysis. Structural equation modeling (SEM) was used to quantify the relationships identified. Results indicate that information technology, reliable delivery, Industry 4.0 adoption, resilience, and environmental and social responsibility positively influence supplier quality, which subsequently enhances MDM firm performance. Supplier quality emerges as a critical mediator between supplier selection factors and company performance. Findings emphasize that prioritizing supplier quality, reinforced through Industry 4.0 technologies and resilient practices, ensures operational continuity, enhances competitive advantage, and supports sustainability. Companies incorporating these critical success factors into their supplier selection processes are better equipped to manage supply disruptions, achieve consistent quality, and sustain performance in highly regulated environments. 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

Background: Cross-border suppliers have always been points of disruption, further impacting international trade, businesses, and societies along the chain. Understanding the current resilience capabilities of cross-border suppliers is a stepping-stone to implementing resilience initiatives and policies to mitigate disruptions. However, no guidelines or practical tools exist to help cross-border suppliers conduct a deep-dive analysis of their resilience. Therefore, this paper proposes an assessment tool to guide cross-border suppliers in assessing their resilience capabilities. Methods: The supplier-focused resilience assessment approach was adapted from the Logistics Performance Index concept. The questionnaire and its resilience assessment dimensions were established through a literature review with the support of experienced cross-border professionals. Case study validation was further conducted to demonstrate the tool’s applicability. Results: The assessment evaluation through the Cross-Border Resilience Performance Index facilitates detailed analysis and benchmarking, enabling recommendations for necessary resilience initiatives and policies. Conclusions: This study contributes to the supply chain literature by adding a more practical resilience assessment approach focusing on cross-border suppliers. The Cross-Border Resilience Performance Index is the study’s primary contribution and is novel to the literature. The tool’s advantages include ease of use, replication potential, and its ability to glean comprehensive insights, ultimately improving supplier resilience and supply chain robustness. By implementing more precise initiatives, the tool increases the chances of cross-border suppliers being selected and maintained by buyers, helping them sustain their businesses and better respond to changing conditions to mitigate disruptions. Full article

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. Full article

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. Full article

Background: The corn supply chain is vital for food security and economic stability regionally and globally. This study integrates sustainable supply chain management with location optimization to address trade-offs from climate change, economic viability, and environmental impact while assuming the constant social obligation inherent in the supply chain structure. Methods: This study employs a mixed-integer programming (MIP) framework to optimize facility locations in North Dakota, including corn production zones as suppliers and ethanol plants as consumers. Primary objectives include cost minimization and greenhouse gas reduction, enabling the prioritization of economic or environmental goals as per organizational strategies and regulations. This approach ultimately maximizes resource utilization by ensuring efficient production and distribution practices. Results: The case study results highlight the optimal selection of 20 out of 30 corn production zones to meet statewide ethanol plant demand efficiently. Using compressed natural gas (CNG) instead of diesel could potentially save USD 2 million annually and cut carbon emissions by up to 1148 thousand tons per year, demonstrating meaningful progress toward economic and environmental sustainability within the supply network. Conclusions: The presented work offers a systematic methodology for designing sustainable supply chains for various agricultural products, aligning with the broader goal of promoting sustainability and resilience for efficient agricultural production and distribution systems. Full article

As a new sustainable building production mode, prefabricated building supply chains can realize energy saving, environmental protection and full cycle value maximization of building products. Prefabricated building supply chains often experience disruptions due to supply instability, transportation delay and force majeure, resulting in project delays and cost escalations and posing challenges to the sustainable development objectives of enterprises. Therefore, it is important and essential to study the strategy of enhancing the resiliency of prefabricated building supply chains, which has not been comprehensively explored in previous papers. This paper constructs decision-making models for supply chain cost resilience strategies under varying scenarios of supply disruptions, incorporating both redundant inventory and back-up supplier strategy. It considers the total cost and resilience of the supply chain as dual objective functions. Parameter-tuned non-dominated sorting genetic algorithm-Π (NSGA-Π) algorithms were used innovatively to solve the project case, and the impacts of the redundant inventory coefficient and back-up supplier supply price coefficient on the model result were analyzed. The results indicate that the supply chain with resilience construction has a superior capability to cope with disruption. The results show that when there is a mild supply disruption, the general contractor uses the capacity within the supply chain and chooses a redundant inventory strategy to restore resilience. In the event of moderate disruption, both the easy inventory strategy and back-up supplier strategy are selected to maintain supply chain stability. In the event of a severe disruption, only the back-up supplier strategy is selected to cover the losses and maintain the project schedule. In addition, the choice of resilience strategy is impacted by the inventory levels and component prices of back-up suppliers. It further verifies the effectiveness of the model and the impacts of uncertain parameters in the model on the results. This study contributes to enhancing the resilience management of the prefabricated building supply chain by the general contractor, thereby elevating the overall efficiency and competitiveness of the supply chain and furthering the sustainable development of prefabricated buildings. Full article

Globalization and multinational commerce have increased the dynamism and complexity of supply networks, thereby increasing their susceptibility to disruptions along interconnected supply chains. This study aims to tackle the significant concern of supplier selection disruptions in the Thai agri-food industry as a response to the aforementioned challenges. A novel supplier evaluation system, PROMETHEE II, is suggested; it combines the Fuzzy Analytical Hierarchy Process (FAHP) with inferential statistical techniques. This investigation commences with the identification of critical indicators of risk in the sustainable supply chain via three phases of analysis and 315 surveys of management teams. Exploratory factor analysis (EFA) is utilized to ascertain six supply risk criteria and twenty-three sub-criteria. Following this, the parameters are prioritized by FAHP, whereas four prospective suppliers for an agricultural firm are assessed by PROMETHEE II. By integrating optimization techniques into sensitivity analysis, this hybrid approach improves supplier selection criteria by identifying dependable solutions that are customized to risk scenarios and business objectives. The iterative strategy enhances the resilience of the agri-food supply chain by enabling well-informed decision-making amidst evolving market dynamics and chain risks. In addition, this research helps agricultural and other sectors by providing a systematic approach to selecting low-risk suppliers and delineating critical supply chain risk factors. By bridging complexity and facilitating informed decision-making in supplier selection processes, the results of this study fill a significant void in the academic literature concerning sustainable supply chain risk management. Full article

This paper presents an innovative approach for supplier selection (SS) within the Tunisian dairy industry, with a specific focus on sustainability and resilience. The proposed method is a hybrid model that integrates multi-criteria decision-making (MCDM) with fuzzy logic. Initially, twelve distinct criteria are identified, categorized into social, environmental, economic, and resilience indexes through a combination of literature review and expert input. To establish the importance of these criteria, a fuzzy analytic hierarchy process (FAHP) is employed to calculate their weights. These weights are then integrated into a new method called fuzzy multi-attribute border approximation area comparison (FMABAC) to rank the suppliers. The methodology aims to provide decision-makers with a more intuitive means of expressing their preferences and evaluating supplier criteria, ultimately identifying the most effective dairy farmers. The research concludes by evaluating the results through two sensitivity analyses: one involving varying criteria weights and the other involving a comparative assessment with alternative fuzzy MCDM techniques. Full article

This study aims to propose an integrated approach for supplier selection based on the lean, agile, resilience, green, and sustainable (LARGS) paradigm. This approach was validated using structural equation modelling (SEM) and the intuitionistic fuzzy TOPSIS method. A comprehensive literature review was conducted, identifying twenty-six criteria, which were then consolidated into five main criteria. A questionnaire was distributed to 237 individuals from manufacturing companies listed on the Tehran Stock Exchange, and the collected data were analyzed using third-order factor analysis and the partial least squares method. Subsequently, the proposed integrated approach was applied to evaluate four suppliers in an intuitionistic fuzzy environment, utilizing expert opinions and a case study on the automotive industry. The results demonstrate the effectiveness and practicality of the proposed approach in terms of prioritizing and selecting suitable suppliers according to LARGS criteria. In conclusion, this study contributes to the existing literature by proposing an integrated approach that addresses the decision-making challenges in supplier selection. This approach offers a practical tool for managers seeking to enhance sustainable supply chain performance from the LARGS perspective. Full article

Nowadays, managers are facing the challenge of operating in situations of high uncertainty: delayed deliveries, lack of energy or rising energy and gas costs, the need to replace energy sources, and changing supply and sales markets. In the literature, two dominant trends in the activities of enterprises in the face of crises can be distinguished: (I) changes in supply chain management (increased flexibility by searching for local suppliers); and (II) transition to digital production and investment in technologies in the concept of Industry 4.0 or even Industry 5.0, such as artificial intelligence, 3D printing, robots, cyber-physical systems, digital manufacturing, and blockchain. A gap in the research has been observed in examining the impacts of these actions on the implementation of sustainable solutions and designating organizational changes in manufacturing. The main goal of this study is to review the literature using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) for data collection and, secondly, the methodology of Systematic Literature Review (SLR) and Mapping in Literature Reviews (MLR). Our literature review of the selected databases is based on 566 published articles in 2020–2022. The achieved results indicate the main organizational changes in the context of sustainable development in manufacturing, namely in the business management area (adopting Sustainable Project Management (SPM), Sustainable Supply Chain Management practices, Sustainable Supplier Selection (SSS), and Resilient Manufacturing Strategy (RMS)) and in the production area (adopting Internet of Things (IoT)-enabled Additive Manufacturing assists, simulation software, and Life Cycle Assessment. The findings of our study revealed key relationships between the adoption of fifth-generation industrial technologies and the sustainable development of manufacturing. Full article

Disruption risks exacerbate the complexity of low-carbon supply chain network design in an uncertain supply chain environment. Considering the low frequency and non-repeatability of these disruption events makes it impossible to collect data to obtain their probabilities. In this study, supply disruptions were regarded as uncertain events; supply chain uncertain disruption risk is defined and quantified based on the uncertainty theory, in which uncertain disruptions are characterized by the belief degree on account of expert estimation with duality, i.e., symmetry. Optimization models were constructed with the objective of minimizing expected carbon emissions and costs, which optimizes the selection of suppliers with uncertain disruptions, and the assignment of manufacturers and customers. The properties of the model were analyzed, and the models were solved separately using different methods according to different decision criteria. Finally, the validity of the proposed models and algorithm were verified using a real case study of a glass manufacturing company. The findings exhibit promising insights for designing a sustainable and resilient supply chain network in an uncertain environment. Full article

The resilience and sustainability of supply chains are facing new challenges due to the increasing complexity of supply chains. Compared to traditional supply chain networks, the Physical Internet (PI) has the potential to address sustainable development challenges and build resilient supply chain networks by providing interconnected and open logistics services. However, the interplay between resilience and sustainability has yet to be thoroughly explored in the PI-enabled supply chain literature. This paper aims to investigate the relationship between resilience and sustainability in PI-enabled supply chains. An innovative hybrid approach that combines the resilient-sustainable performance scoring method with mixed-possibilistic programming is proposed to trade-off and analyze the relationship between the two. Applying the concepts of resilience and sustainability to PI-enabled supply chains involves optimizing costs, sustainable performance, and resilience levels simultaneously. This enables coping with unforeseen disruptions from suppliers, plants, and PI hubs. The results of computational experiments have demonstrated that the PI-enabled supply chain exhibits stronger sustainable and resilience performance in dealing with random disruptions compared to traditional supply chains. The fruitful research yielded important management insights and practical implications, thereby contributing to the reinforcement of the literature on PI. Full article

Supplier selection and order allocation (SS-OA) are two of the most important strategic decisions for supply chain network design and operation. If sustainability and resilience criteria are taken into consideration together and a holistic sustainable and resilient SS-OA is carried out, it may enable the supply chain network to perform better when subjected to disruption scenarios. In this research paper, a novel comprehensive multi-phase, multi-period sustainable, and resilient SS-OA decision support framework has been proposed. This framework integrates fuzzy multi-criteria decision-making (MCDM) techniques with a fuzzy multi-objective mixed integer nonlinear programming (MOMINLP) mathematical model to optimize triple bottom line (TBL) sustainability and resilience criteria simultaneously for a multi-modal, multi-echelon supply chain network. The proposed framework has been implemented using real-time data collected from the pharmaceutical industry. The results show that among the TBL sustainability criteria, product price, past business, innovative capability, and information disclosure rank as the most significant sub-criteria for the DMs in the pharmaceutical industry, while robustness and flexibility are considered the most valued attributes for potential suppliers as far as the resilience criterion is concerned. An assessment of the results has revealed that transfer cost and custom clearance cost together comprise more than two-thirds of the overall cost of the supply chain network. It has also been noted that inland transportation of goods is dominated by rail as the most preferred mode of transport. Transportation by rail is particularly preferred by suppliers located in geographically contiguous countries. Full article