Search Results (227)

Global supply chains face unprecedented challenges from geopolitical conflicts, climate change, economic volatility, and technological disruptions, highlighting the critical role of supply chain resilience as a core strategy for firms to maintain stability and competitive advantage. Grounded in the resource-based view and dynamic capability theory, this study examines how supply chain capability—that is, entrepreneurial leadership, collaborative capability, and digital transformation—enhances resilience, which mediates its impact on performance. Using structural equation modeling on survey data from Chinese firms, we find that resilience, comprising absorptive, reactive, and recovery capability, significantly mediates the relationship between supply chain capability and performance. Environmental uncertainty moderates this relationship, particularly in highly uncertain contexts, where resilience becomes a key driver of competitive advantage. Theoretically, this study extends dynamic capability theory by disaggregating resilience and exploring its mediating role. Practically, it emphasizes strengthening entrepreneurial leadership, collaborative capability, and digital transformation to improve resilience and performance in uncertain environments. Full article

Manufacturing serves as a vital indicator of a nation’s economic strength, technological advancement, and comprehensive competitiveness. In the context of the VUCA (Volatility, Uncertainty, Complexity, Ambiguity) business environment and globalization, uncertain market demand has intensified supply chain disruption risks, necessitating resilience strategies to enhance supply chain stability. This study proposes five resilience strategies—establishing an information sharing system, multi-sourcing, alternative suppliers, safety stock, and alternative transportation plans—while integrating sustainability requirements. A multi-objective mixed-integer optimization model was developed to balance cost efficiency, resilience, and environmental sustainability. Comparative analysis reveals that the resilience-embedded model outperforms traditional approaches in both cost control and risk mitigation capabilities. The impact of parameter variations on the model results was examined through sensitivity analysis. The findings demonstrate that the proposed optimization model effectively enhances supply chain resilience—mitigating cost fluctuations while maintaining robust demand fulfillment under uncertainties. Full article

This study presents a reinforcement learning–based approach to optimize replenishment policies in the presence of uncertainty, with the objective of minimizing total costs, including inventory holding, shortage, and ordering costs. The focus is on single-level assembly systems, where both component delivery lead times and finished product demand are subject to randomness. The problem is formulated as a Markov decision process (MDP), in which an agent determines optimal order quantities for each component by accounting for stochastic lead times and demand variability. The Deep Q-Network (DQN) algorithm is adapted and employed to learn optimal replenishment policies over a fixed planning horizon. To enhance learning performance, we develop a tailored simulation environment that captures multi-component interactions, random lead times, and variable demand, along with a modular and realistic cost structure. The environment enables dynamic state transitions, lead time sampling, and flexible order reception modeling, providing a high-fidelity training ground for the agent. To further improve convergence and policy quality, we incorporate local search mechanisms and multiple action space discretizations per component. Simulation results show that the proposed method converges to stable ordering policies after approximately 100 episodes. The agent achieves an average service level of 96.93%, and stockout events are reduced by over 100% relative to early training phases. The system maintains component inventories within operationally feasible ranges, and cost components—holding, shortage, and ordering—are consistently minimized across 500 training episodes. These findings highlight the potential of deep reinforcement learning as a data-driven and adaptive approach to inventory management in complex and uncertain supply chains. Full article

Uncertainty in consumer purchasing behavior within online markets propels manufacturers to adopt blockchain for risk mitigation, reshaping supply chain operational dynamics. This study investigates the sales mode selection and blockchain adoption strategies of a risk-averse manufacturer in platform supply chain under uncertain market demand. By integrating Stackelberg game theory with mean-variance analysis, we analyze supply chain equilibrium across four scenarios: RN, RB, AN, and AB. Our findings highlight the significance of a critical commission rate threshold in the manufacturer’s sales mode choice, emphasizing that blockchain adoption enhances the preference for the agency mode. Importantly, highly risk-averse manufacturers are inclined to absorb higher costs associated with blockchain adoption, while those with lower risk aversion only consider it when costs are minimal. Notably, the “agency mode with blockchain adoption” (AB) creates mutual benefits under low adoption costs and risk aversion. When both parties exhibit risk aversion, the platform’s risk aversion significantly influences resale-mode decisions, leading to a transition from the scenario AN to the RB, thereby optimizing synchronized profits. 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

The Sustainable Supply Chain Network Design Problem (SSCNDP) is to determine the optimal network configuration and resource allocation that achieve the trade-off among economic, environmental, social, and resilience objectives. The Sustainable Supply Chain Network Design Problem (SSCNDP) involves determining the optimal network configuration and resource allocation that allows trade-off among economic, environmental, social, and resilience objectives. This paper addresses the SSCNDP under hybrid uncertainty, which combines objective randomness got from historical data, and subjective beliefs induced by expert judgment. Building on chance theory, we formulate a dependent-chance goal programming model that specifies target probability levels for achieving sustainability objectives and minimizes deviations from these targets using a lexicographic approach. To solve this complex optimization problem, we develop a hybrid intelligent algorithm that combines uncertain random simulation with Reinforcement Learning-enhanced Salp Swarm Optimization (RL-SSO). The proposed RL-SSO algorithm is benchmarked against standard metaheuristics—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and standard SSO, across diverse problem instances. Results show that our method consistently outperforms these techniques in both solution quality and computational efficiency. The paper concludes with managerial insights and discusses limitations and future research directions. Full article

The recycling of lithium-ion batteries is picking up rather slowly, although recent rapid growth in consumption and increasing prevalence of battery electric vehicles have increased the quantity of recoverable material from past years of production. Yet, the diversity of different product types i.e., chemistries and product life spans complicates the recovery of raw materials. At present, large-scale industrial recycling of lithium-ion batteries employs (1) pyrometallurgy, with downstream hydrometallurgy for recovery of refined metals/salts; and (2) hydrometallurgy, requiring upstream mechanical shredding of cells and/or modules. Regulatory requirements, especially in Europe, and the high industry concentration along the lithium-ion battery value chain drive recycling efforts forward. The present study aims to quantify the potential contribution of 2nd lithium from recycling to battery production on a global and European scale up to 2050. The overall recycling output of lithium in any given year depends on the interactions between several different factors, including past production, battery lifetime distributions, and recovery rates, all of which are uncertain. The simplest way to propagate input uncertainties to the final results is to use Monte Carlo-type simulations. Calculations were done separately for EVs and portable batteries. The overall supply of lithium from recycling is the sum of the contributions from EVs and portable electronics from both the EU and the RoW in each battery production scenario. Results show a total global supply of recycled lithium below 20% in each scenario until 2050. On the EU level, the contribution of recycled lithium may reach up to 50% due to the high collection and recovery rate targets. Full article

The increasing emphasis on integrating sustainability into corporate operations has prompted supply chain managers to incorporate not only economic objectives but also environmental and social considerations into their network designs. This study presents a structured six-stage methodology to develop a fuzzy multi-objective optimization model for the sustainable design of a multi-level, multi-product forward supply chain network. The model incorporates two conflicting objectives: minimizing total network costs and reducing environmental impact. To quantify environmental performance, a comprehensive life cycle assessment is conducted in accordance with the ISO 14040 standard and the ReCiPe 2016 method, focusing on three impact categories: human health, resources, and ecosystems. To address uncertainty in demand and production costs, fuzzy mixed-integer linear programming is employed. The model is validated and applied to a real-world case study of a dairy small-to-medium enterprise in Ecuador. Using the epsilon-constraint method, a Pareto frontier is generated to illustrate the trade-offs between the economic and environmental objectives. This research provides a robust decision-making tool for uncertain environments and advances knowledge on the integration of life cycle assessment with supply chain optimization and network design methodologies for sustainable development. Full article

This paper introduces and analyzes the concept of $\lambda$-statistical convergence in fuzzy paranormed spaces, demonstrating its relevance to supply chain inventory management under demand shocks. We establish key relationships between generalized convergence methods and fuzzy convex analysis, showing how these results extend classical summability theory to uncertain demand environments. By exploring $\lambda$-statistical Cauchy sequences and $(V,\lambda )$-summability in fuzzy paranormed spaces, we provide new insights applicable to adaptive inventory optimization and decision-making in supply chains. Our findings bridge theoretical aspects of fuzzy convexity with practical convergence tools, advancing the robust modeling of demand uncertainty. Full article

Port shipping collaboration is vital to greener, more resilient trade, yet decisions remain siloed and uncertain. This study develops a Bayesian network model grounded in empirical data from major Chinese ports, aiming to systematically analyze and enhance port shipping collaborative capacity. The methodology integrates expert knowledge and structural learning algorithms to construct a Directed Acyclic Graph (DAG), representing complex multi-stakeholder interactions among port enterprises, shipping companies, customers, and governmental bodies. Through forward and backward probabilistic inference, the study quantifies how coordinated improvements yield substantial synergistic benefits. Five leverage points stand out: customer engagement in green supply chains, perceived service quality, port digital information integration, multilateral trading maturity, and strict policy enforcement. A newly revealed feedback loop between digital integration and enforcement extends Emerson et al.’s collaborative governance framework, highlighting “digital-era connectivity” as a critical governance dimension and offering managers a focused, evidence-based action agenda. Full article

Background: Brazil’s bioethanol supply chain is vital for global energy security and climate action but remains vulnerable to climate disruptions, market volatility, and conflicting stakeholder interests. While resilience strategies exist, they often overlook the potential of circular economy (CE) principles. Methods: This study proposes an integrated decision-support framework that combines Strategic Options Development and Analysis (SODA), the Composition of Probabilistic Preferences (CPP), and the Rank-Order Centroid (ROC) method to prioritize CE strategies under uncertainty. The approach incorporates stakeholder input and probabilistic modeling to evaluate 20 alternatives across 10 criteria. Results: The analysis identified climate risk modeling, biogas utilization, and blockchain-enabled traceability as the most effective strategies for improving supply chain resilience. The model demonstrated strong robustness, maintaining 95% consistency in rankings under varied decision-making scenarios. Conclusions: This research presents a novel, structured method for supporting complex decisions in uncertain environments. By integrating CE principles and group decision-making tools, the study offers valuable guidance for policymakers and industry leaders seeking to build more resilient and sustainable bioethanol supply chains. Full article

The rapid expansion of online grocery shopping has intensified the need for strategically located distribution hubs that ensure efficient and sustainable operations. Traditional location models emphasize economic and logistical factors but often neglect energy efficiency and environmental sustainability. This paper proposes a hybrid decision-making model that integrates the analytic hierarchy process (AHP) and the spherical fuzzy technique for order of preference by similarity to ideal solution (SFTOPSIS) to address the complexities of delivery hub location selection. The AHP is used to determine the relative importance of key decision-making criteria, including cost, accessibility, infrastructure, competition, and sustainability, while SFTOPSIS ranks the candidate locations based on their proximity to the ideal solution. Spherical fuzzy sets allow for a more nuanced treatment of uncertainty, improving decision-making accuracy in dynamic environments. The results demonstrate that this hybrid approach effectively manages incomplete and uncertain data, delivering a robust ranking of candidate locations. By incorporating sustainability as a key factor, this study provides a structured and adaptive framework for businesses to optimize logistics operations in the post-pandemic landscape. The proposed methodology not only enhances decision-making in location selection but contributes to the development of more resilient and sustainable supply chain strategies. Full article

In light of the frequent occurrence of uncertain events, supply chain resilience has emerged as a critical issue for the survival and development of enterprises. This study empirically examines the impact of corporate environmental, social, and governance (ESG) performance on supply chain resilience, utilizing data from A-share listed companies in China from 2015 to 2023. The findings reveal that strong ESG performance positively influences supply chain resilience. The concept of “new quality productive forces” provides a novel perspective for understanding corporate sustainable development. Mechanism tests indicate that new quality productive forces play a significant mediating role between ESG performance and supply chain resilience. Specifically, by enhancing ESG performance, enterprises indirectly promote the growth of new quality productive forces, thereby further strengthening supply chain resilience. The robustness of these results is confirmed through tests involving the replacement of core explanatory variables, expansion of sample size, inclusion of additional control variables, and Hausman Tests. Furthermore, heterogeneity analysis demonstrates that state-owned enterprises exhibit a more pronounced effect of ESG performance on supply chain resilience compared to private enterprises. Full article

Brazil has abundant natural resources and a largely renewable electricity matrix, with about 90% of its capacity from clean sources. Despite strong offshore wind potential, its economic viability remains uncertain due to the lack of a domestic supply chain and reliance on international cost estimates. This study assesses offshore wind competitiveness in Brazil using the investment decision model (IDM), which minimizes expansion and operational costs through 2031. Capacity factors (CF) from ERA5 data support monthly energy production estimates across load levels. Three scenarios were analyzed: (i) a reference case based on Brazil’s 10-Year Energy Plan (PDE 2031); (ii) mandatory addition of 500 MW/year of offshore wind to assess cost impact; and (iii) a breakeven case with gradual CAPEX and OPEX reductions until offshore wind became cost-competitive. The results indicate that offshore wind energy can become economically viable with a CAPEX range of approximately USD 1500–1550/kW and an OPEX of USD 50–55/kW·year in locations with a CF above 60%. These cost levels have already been observed in global markets and may be achievable in Brazil. However, challenges, such as the lack of a domestic supply chain and volatility in the exchange rate, remain significant barriers. Full article

Increasing supply chain uncertainty due to market volatility has heightened the need for more flexible procurement strategies. While procurement through long-term forward contracts provides supply stability and cost predictability, it limits adaptability. Option contracts offer procurement flexibility, but require additional upfront premiums. Meanwhile, the spot market enables real-time purchasing without prior commitments, enhancing flexibility but exposing buyers to price volatility. Despite the growing adoption of portfolio procurement—combining forward contracts, option contracts, and spot market purchases—the existing research primarily examines these channels in isolation or in limited combinations, lacking an integrated perspective. This study addresses this gap by developing a comprehensive procurement model that simultaneously optimizes procurement decisions across all three channels under uncertain demand and fluctuating spot prices. Unlike prior studies, which often analyze one or two procurement channels separately, our model presents a novel, holistic framework that balances cost efficiency, risk mitigation, and adaptability. Our findings demonstrate that incorporating the spot market significantly enhances procurement flexibility and profitability, particularly in environments with high demand uncertainty and price volatility. Additionally, sensitivity analysis reveals how fluctuations in spot prices and demand uncertainty influence optimal procurement decisions. By introducing a new, practical approach to portfolio procurement, this study provides managerial insights that help businesses navigate complex and uncertain supply chain environments more effectively. However, this study assumes unlimited spot market capacity and reliable suppliers, highlighting a limitation that future research should address. Full article