Current Issue : July-September Volume : 2025 Issue Number : 3 Articles : 5 Articles
This study explores the impact of COVID-19-related supply chain disruptions on manufacturing firms, focusing on external risks: demand, environmental, and supply. Our literature review reveals the general lack of comprehensive disruption plans and exposes the vulnerabilities in manufacturing firms with limited research addressing this issue. By adopting an interpretive research philosophy and a qualitative, inductive approach, our research delves into the operational challenges and adaptations implemented in the manufacturing sector during the pandemic through case studies. The findings reveal that COVID-19 significantly increased risks, causing demand surges, logistical disruptions, extended lead times, and labour shortages due to lockdowns, necessitating strategic shifts towards localised and digital supply chains in the manufacturing sector. Our study not only enriches the supply chain literature by detailing the pandemic’s effects and emphasising the need for robust disruption plans for enhanced resilience but also offers new insights into managing supply chain disruptions in crises, highlighting the necessity of strategic adaptations for future crisis preparedness across various industries....
The issue of inventory balance in supply chain management represents a classic problem within the realms of management and logistics. It can be modeled using a mixture of equality and inequality constraints, encompassing specific considerations such as production, transportation, and inventory limitations. A Zeroing Neural Network (ZNN) model for time-varying linear equations and inequality systems is presented in this manuscript. In order to convert these systems into a mixed nonlinear framework, the method entails adding a non-negative slack variable. The ZNN model uses an exponential decay formula to obtain the desired solution and is built on the specification of an indefinite error function. The suggested ZNN model’s convergence is shown by the theoretical results. The results of the simulation confirm how well the ZNN handles inventory balance issues in limited circumstances....
The advent of Industry 4.0 and the integration of Artificial Intelligence (AI) is transforming supply chain management (SCM), improving efficiency, resilience and strategic decision-making capabilities. This research study provides a comprehensive overview of AI applications in key SCM processes, including customer relationship management, inventory management, transportation networks, procurement, demand forecasting and risk management. AI technologies such as Machine Learning, Natural Language Processing and Generative AI offer transformative solutions to streamline logistics, reduce operational risk and improve demand forecasting. In addition, this study identifies barriers to AI adoption, such as implementation challenges, organizational readiness and ethical concerns, and highlights the critical role of AI in promoting supply chain visibility and resilience in the midst of global crises. Future trends emphasize human-centric AI, increasing digital maturity, and addressing ethical and security concerns. This review concludes by confirming the critical role of AI in shaping sustainable, flexible and resilient supply chains while providing a roadmap for future research and application in SCM....
As product diversity continues to expand in today’s market, there is an increasing demand from customers for unique and varied items. Meeting these demands necessitates the transfer of different sub-product components to the production line, even within the same manufacturing process. Lean manufacturing has addressed these challenges through the development of kitting systems that streamline the handling of diverse components. However, to ensure that these systems contribute to sustainable practices, it is crucial to design and implement them with environmental considerations in mind. The optimization of warehouse layouts and kitting preparation areas is essential for achieving sustainable and efficient logistics. To this end, we propose a comprehensive study aimed at developing the optimal layout, that is, creating warehouse layouts and kitting preparation zones that minimize waste, reduce energy consumption, and improve the flow of materials. The problem of warehouse location assignment is classified as NP-hard, and the complexity increases significantly when both storage and kitting layouts are considered simultaneously. This study aims to address this challenge by employing the genetic algorithm (GA) and Ant Colony Optimization (ACO) methods to design a system that minimizes energy consumption. Through the implementation of genetic algorithms (GAs), a 24% improvement was observed. This enhancement was achieved by simultaneously optimizing both the warehouse layout and the kitting area, demonstrating the effectiveness of integrated operational strategies. This substantial reduction not only contributes to lower operational costs but also aligns with sustainability goals, highlighting the importance of efficient material handling practices in modern logistics operations. This article provides a significant contribution to the field of sustainable logistics by addressing the vital role of kitting systems within green supply chain management practices. By aligning logistics operations with sustainability goals, this study not only offers practical insights but also advances the broader conversation around environmentally conscious supply chain practices....
This SI provides a comprehensive exploration of the evolving landscape of logistics and supply chain management in the post-COVID-19 era, highlighting the interactions between sustainability, resilience, and digital transformation. The disruptions caused by the pandemic, coupled with geopolitical instabilities and the global energy crisis, have highlighted the need for innovative approaches to increase supply chain resilience and flexibility. The research contributions included in this issue demonstrate how digital technologies, particularly simulations and numerical modeling, play a pivotal role in optimizing supply chain operations, enabling multi-objective decision-making that considers and balances economic, environmental, and social aspects....
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