Modern Supply Chains Research Articles

Revolutionizing Supply Chain : The Synergy of Warehouse Management and Data Analytics

This article examines the transformative impact of integrating data analytics with warehouse management in modern supply chains. It explores how this synergy is revolutionizing inventory management, order fulfillment, and customer service across industries. The article discusses key elements of effective warehouse management, including optimal space utilization, efficient inventory control, and timely order processing. It then delves into how data analytics elevates these functions through inventory optimization, real-time monitoring, and predictive analytics. Case studies from e-commerce and manufacturing sectors illustrate real-world applications and benefits. Finally, the competitive advantages gained through enhanced operational efficiency, cost reduction, and improved service levels are analyzed, highlighting the critical role of this integration in shaping the future of supply chain management.

Root Cause Attribution of Delivery Risks via Causal Discovery with Reinforcement Learning

Managing delivery risks is a critical challenge in modern supply chain management due to the increasing complexity and interdependencies of global supply networks. Existing methods often rely on correlation-based approaches, which fail to uncover the true causes behind delivery delays. This limitation makes it difficult for supply chain managers to identify actionable factors that can mitigate risks effectively. To address these challenges, we propose a novel method that integrates causal discovery with reinforcement learning to identify the root causes of delivery risks. Unlike traditional correlation-based methods, our approach uncovers both the direction and strength of causal relationships between variables, allowing for more accurate identification of the key drivers behind delivery delays. By applying causal strength quantification, we further measure the impact of each factor on delivery performance. Using real-world supply chain data, our results demonstrate that the proposed method reveals hidden causal relationships between factors such as shipping mode, order size, and delivery status. These insights enable supply chain managers to implement more targeted interventions, significantly improving risk mitigation strategies.

Theoretical foundations and practical implementation of supply chain optimization a metal fabrication business model

In the dynamic landscape of modern business, effective management of supply chains stands as a cornerstone of organizational success and competitive edge. This study investigates the theoretical principles and practical implementations of optimization models aimed at refining supply chain operations in the metal fabrication business, giving the maximum profit. It begins with a comprehensive survey of optimization theory, encompassing linear programming, integer programming, and stochastic optimization. It proceeds to their application in addressing diverse challenges encountered in supply chain management. The discussion includes deterministic models, such as those optimizing facility location under capacity constraints and transportation logistics, alongside stochastic models designed to manage uncertainties inherent in demand forecasting and inventory control. Advanced methodologies like metaheuristic algorithms and multi-objective optimization techniques are examined for their capacity to navigate the intricate and often conflicting objectives within supply chain networks. Through comprehensive theoretical analysis and a study of the supply chain model developed for the metal fabrication business, this paper contributes to advancing knowledge in mechanical engineering and supply chain management, offering insights pertinent to practitioners and researchers alike. Ultimately, it emphasizes the critical role of optimization models in optimizing efficiency, reducing costs, improving profit margin, reducing CO2 emission, and enhancing competitiveness in modern supply chain operations.

Ensuring Food Safety through Effective Supply Chain Management:Challenges and Suggestions

Ensuring food safety is a complex challenge exacerbated by the global nature of modern supply chains. Effective supply chain management plays a crucial role in safeguarding food. This paper explores the concerns of food safety that causes by unethical food supply chain. Case studies from both developed and developing regions illustrate successful initiatives and highlight lessons learned. The role of collaboration among stakeholders—producers, processors, distributors, regulators, and consumers—is emphasized as crucial for effectively managing risks and ensuring compliance with safety standards throughout the supply chain. This essay examines how supply chain issues affect food safety through three case studies: China’s oil scandal mixing edible and chemical oils, the 2022 U.S. peanut butter recall due to salmonella contamination, and antibiotic-contaminated Vietnamese shrimp amid Japan’s nuclear water release. Each case underscores the impact on public health and trust. By examining three current cases, this paper aims to provide a comprehensive understanding of how ineffective supply chain management can create food safety risks and reduce consumer confidence in the safety and integrity of the food supply chain and suggest possible course of action.

Influence of Supply Chain Diversification on the Performance of Large Food and Beverage Processing Companies in Nairobi City County, Kenya

The performance of food and beverage manufacturing companies has become a pressing concern due to the rising incidence of supply chain disruptions, which pose significant threats to achieving operational goals. This study investigates the influence of supply chain diversification on the performance of large food and beverage processing companies in Nairobi City County. Supply chain disruptions have been linked to severe financial repercussions, including lower stock returns and increased failure rates, necessitating innovative management strategies. Utilizing a quantitative correlational research design, the study surveyed all 46 major food and beverage companies in the region, targeting procurement, financial, operations, and supply chain managers. Data collection was conducted via questionnaires, achieving an impressive response rate of 81%, underscoring the representativeness of the findings. Statistical analysis revealed a strong positive correlation between supply chain diversification and organizational performance, with an R-squared value of 0.650, indicating that approximately 65% of performance variability could be attributed to diversification practices. Regression analysis confirmed that each unit increase in diversification significantly enhanced performance metrics. These results align with existing literature, emphasizing the need for proactive diversification strategies to mitigate risks and enhance operational efficiency. Recommendations include continuous investment in diversifying supplier bases, adopting advanced technologies such as artificial intelligence, and implementing regular training programs for staff. Furthermore, strategic partnerships with suppliers and routine assessments of risk management policies are vital for maintaining competitive advantages. This research highlights the critical role of supply chain diversification in enhancing performance in the food and beverage sector, providing insights that are essential for navigating the complexities of modern supply chains. Future research should explore comparative studies across different sectors, longitudinal impacts of diversification, and qualitative insights from industry leaders to further enrich understanding in this area. By addressing these recommendations and research avenues, food and beverage manufacturing companies can enhance their resilience and ensure sustained performance in an ever-evolving market landscape..

Artificial Intelligence-Powered Risk Assessment in Supply Chain Safety

The increasing complexity and globalization of supply chains necessitate robust risk management strategies to ensure safety and resilience. Traditional risk assessment methods often fall short in dynamically adapting to the rapidly changing conditions and voluminous data inherent in modern supply chains. This study explores the potential of Artificial Intelligence (AI)-powered risk assessment to address these limitations in the context of Malaysia's supply chain industry. By employing AI technologies such as machine learning, IoT, and predictive analytics, organizations can significantly enhance their risk management capabilities, improving predictive accuracy, real-time monitoring, and overall operational efficiency. Through a qualitative analysis involving in-depth interviews with supply chain managers, AI experts, and technology vendors, the study identifies the strategies employed for AI integration, the perceived effectiveness of these technologies, and the challenges faced in implementation. The findings highlight the importance of robust data governance, the development of explainable AI models, and continuous skill development to overcome barriers related to data quality, model interpretability, and high implementation costs. The study concludes with recommendations for fostering a safer and more resilient logistics environment in Malaysia, emphasizing the need for comprehensive AI adoption frameworks and scalable solutions for small and medium-sized enterprises.

Research on Innovation of Food Logistics Supply Chain Management

In the context of globalization and constantly changing consumer demand, the management of food logistics supply chains has become increasingly complex and important. This paper mainly studies the problems and innovative methods in food logistics supply chain management, aiming to improve the efficiency and adaptability of the supply chain by optimizing management strategies. This paper adopts literature analysis and case analysis methods to analyze in detail the problems of information asymmetry, cost control, transportation efficiency, and inventory management in the food logistics supply chain. It proposes innovative supplier management models, digital management, modern supply chain management concepts, inter chain cooperation, and risk management solutions. Research has found that adopting information-sharing mechanisms and advanced information technology, as well as strengthening collaboration among various links in the supply chain, can effectively improve management efficiency and reduce operating costs. This study provides some useful references for food logistics enterprises in practical operation, helping them improve service quality and reduce operating costs in a fiercely competitive market environment, enhance market competitiveness, ensure food safety and freshness, and meet the growing needs of consumers.

Optimizing Supply Chain Transparency and Customer Compatibility with AI-Driven Models

As global supply chains become increasingly complex, the adoption of artificial intelligence (AI) technologies has emerged as a critical strategy for enhancing operational transparency and improving customer compatibility. This study investigates the application of AI-driven models in optimizing supply chain performance, focusing on predictive analytics, real-time data integration, and customer-centric personalization. A comprehensive experimental framework was employed, evaluating five distinct AI configurations against four key performance criteria: operational transparency, customer compatibility, cost efficiency, and delivery performance. Results demonstrated that the Real-Time Data Integration Model achieved a 20% improvement in operational transparency, allowing for enhanced visibility into inventory management and more agile responses to dynamic demand fluctuations. Additionally, the Customer-Centric Personalization Model increased customer satisfaction by 10%, emphasizing the critical role of tailored service delivery in modern supply chain management. The Cost Optimization Model yielded significant cost reductions, improving cost efficiency by 18%, though it showed a marginal decrease in customer compatibility. The findings highlight the trade-offs between cost efficiency and customer-centric strategies, suggesting that a balance is required to achieve a well-rounded and sustainable supply chain model. This research underscores the transformative potential of AI in driving efficiency, transparency, and customer satisfaction. Future work should explore the integration of advanced technologies such as 5G and further investigate scalable AI solutions capable of addressing the evolving challenges faced by global supply chains.

Towards trustworthy AI for link prediction in supply chain knowledge graph: a neurosymbolic reasoning approach

Modern supply chains are complex and interlinked, resulting in increased network risk exposure for companies. Digital Supply Chain Surveillance (DSCS) has emerged as a practice to proactively monitor these risks. Within DSCS, link prediction, a particular task whose objective is to identify hidden relationships in the supply chain, has been increasingly studied in the literature. While many approaches have been proposed, machine learning (ML) based techniques have recently gained wider attention due to their state-of-the-art performance across different benchmark datasets. However, adoption of these technologies in practice remains difficult. Their black-box nature have resulted in a lack of trustworthiness among practitioners. In this paper, we design a trustworthy ML approach based on recent theoretical development in neurosymbolic AI methods that enables a more transparent learning and reasoning process. We find that our approach is not only on par with state-of-the-art black box models when tested in two benchmark datasets on the automotive and energy industry but also addresses research gaps on explainability and complexity analysis.

Nature-inspired design principles promote supply network resilience

The dynamic, interconnected nature of modern supply chains makes it important to understand how firm-level decision-making will impact the robustness of supply chains to disruption. The behavior of naturally evolved distribution systems offers a useful starting point to identify potential design features that can promote robustness without compromising the viability of individual firms. Drawing inspiration from how ant food-sharing networks respond to supply shortages, we developed an agent-based model of a generalized supply network and evaluated how different local strategies influenced the ability of firms to acquire sufficient resources to meet their demand. Our simulations reveal that differences among firms in strategic behavior can reduce variation in outcomes across firms while maintaining mean performance, thereby buffering system-level robustness. In addition, the ability to expand one’s supplier network bolstered performance when firms experienced difficulty in meeting their demand. Conversely, under the assumptions of our model, overly relying on distributors to gain access to additional suppliers or to gain competitive advantages was ineffective in helping firms to meet their consumptive demand. Our nature-inspired modeling framework provides a potentially useful approach for evaluating how different participant decision-making strategies may impact the robustness and resilience of global supply chains that are increasingly likely to face frequent and unpredictable disruptions.

Performance assessment of a multi-level shuttle system: an optimisation-embedded simulation study on time and energy consumption

ABSTRACT In response to the dynamic demands of modern e-commerce and supply chains, this paper addresses the critical need for adaptability and efficiency through the implementation of automated storage and retrieval systems. By using an optimisation-embedded simulation methodology, the proposed study focuses on a multi-level shuttle system designed to overcome the inherent limitations of traditional storage systems. A comprehensive discrete event simulation model is developed to evaluate the performance of the multi-level shuttle system. This model includes calculations of travel time and energy consumption, taking also into account stochastic handling times associated with repetitive tasks. In addition, a dynamic programming algorithm is integrated in the simulation model to optimise the sequencing of storage and retrieval missions performed by the handling machine. The study systematically investigates various design and operational parameters, including warehouse configurations and capacity, simultaneous handling of multiple unit loads, and the dimensions of the optimisation sessions. For all configurations tested, the research demonstrates operational advantages in the simultaneous handling of two unit loads. Theoretical contributions extend the current state of simulation models for automated warehousing systems. For practitioners, the study provides empirical insights to support decision making in the configuration of automated warehouses.

Optimizing supply chain networks using mixed integer linear programming (MILP)

Mixed Integer Linear Programming (MILP) has emerged as a powerful tool for optimizing complex supply chain networks. This paper explores the theoretical foundations of MILP, including the integration of integer variables and advanced solution techniques such as branch-and-bound and branch-and-cut algorithms. Through detailed modeling of production planning, network design, and transportation logistics, MILP enables companies to achieve significant cost reductions and operational efficiencies. We present case studies from retail, manufacturing, and pharmaceutical sectors to illustrate the practical applications of MILP. These examples demonstrate how MILP optimization can lead to reductions in production and inventory costs, improved customer satisfaction, and enhanced service levels. The findings underscore the value of MILP in addressing the multifaceted challenges of modern supply chain management.

Building Resilient Supply Chains with Intelligent Automation

Building Resilient Supply Chains with Intelligent Automation represents a transformative approach to modern supply chain management, focusing on optimizing operations through the integration of advanced technologies, data analytics, and process automation. In an increasingly complex and competitive global market, these solutions offer a framework to enhance efficiency, transparency, and responsiveness across the entire supply chain from procurement to final delivery. This paper explores the core components of Intelligent Automation, including data integration, supply chain visibility, inventory and demand management, and process automation. However, implementing these systems also presents challenges such as overcoming data silos, managing supply chain complexity, and ensuring regulatory compliance. This study aims to provide a comprehensive understanding of Intelligent Automation in supply chains, emphasizing their benefits, challenges, and the critical role they play in the evolution of supply chain management in today's dynamic business environment. Keyword: Supply Chain, Advance Logistics, Courier Management, Supply Solution, Automation, Demant Management

Enhancing Data Security and Traceability in Supply Chain Management using Blockchain Technology

This paper introduces a blockchain-powered tool to enhance supply chain management security and traceability. Supply chains are often plagued by fraud, counterfeiting, and a lack of transparency, particularly for end customers. Blockchain technology, known for its immutability and transparency, presents a promising solution to these challenges. In this study, we thoroughly discuss our implementation of blockchain technology within the context of the supply chain. The paper outlines the entire process, beginning with data collection and pre-processing and culminating in the secure posting of transactions via smart contracts. By leveraging Blockchain's decentralized nature, our system ensures that each transaction is securely recorded and immutable, significantly reducing the risk of data tampering or fraud. A synthetic dataset tailored for supply chain operations demonstrates the proposed system's feasibility and effectiveness. The results indicate substantial improvements in data integrity, traceability, and overall supply chain efficiency. This research highlights the potential of Blockchain to revolutionize supply chain management and provides a detailed framework for implementing such a system in real-world scenarios. The study's findings suggest that blockchain technology can be a critical tool in addressing the prevalent issues of trust and transparency in modern supply chains, offering a robust solution to enhance the reliability and efficiency of these complex networks.

Enhancing Supply Chain Performance: A Comprehensive Review of Supplier Quality Management Strategies

This review article provides a comprehensive overview of supplier quality management (SQM) in the context of modern supply chain management (SCM). SQM is a methodical strategy aimed at examining and managing the dependability and performance of suppliers who provide components, raw materials, or services essential to the manufacturing process. The article discusses the objectives of SQM, including boosting procurement accuracy, simplifying supplier assessments, improving communication with suppliers, and promptly addressing non-compliance issues. It highlights the importance of SQM in ensuring product quality, reducing risks, and enhancing consumer satisfaction. The article also delves into supplier selection and evaluation methods, supplier evaluation criteria, risk assessment and mitigation strategies, quality assurance agreements, continuous improvement practices, and regulatory compliance challenges in SCM processes. By providing insights into these key aspects, this review article aims to offer a comprehensive understanding of SQM and its significance in contemporary supply chain operations.

Green-synthesized antibacterial and unidirectional water-permeable polylactic acid/ZnO composite film for enhanced preservation of perishable fruits

Preserving perishable fruits during transportation and storage is a critical challenge in modern logistics and supply chains. This study developed an antibacterial and unidirectional water-permeable tri-layered polylactic acid/ZnO composite film using green biosynthesis and electrospinning technologies to protect highly perishable non-climacteric fruits. The composite film, with encapsulated ZnO nanoparticles, exhibited exceptional antibacterial properties, achieving a 99.999 % inhibition rate against Escherichia coli and Staphylococcus aureus. Additionally, the film's unidirectional water permeability allowed water droplets to penetrate its surface within 1.10 s, ensuring effective moisture retention. When used to package strawberries, cherries, and grapes, the film significantly reduced decay and moisture loss, keeping strawberries fresh for 8 days and cherries and grapes for 12 days at room temperature. This polylactic acid/ZnO composite film shows great potential for enhancing the preservation of perishable fruits in packaging applications.

Credit risk contagion of supply chain finance: An empirical analysis of supply chain listed companies.

With the gradual rise of the supply chain financial model and the expansion of scale, credit risk and contagion effects are gradually strengthened as business and financial links between upstream and downstream enterprises in the supply chain. The traditional credit risk contagion assessment model based on the financial status of an enterprise and the pledging of fixed assets has been unable to meet the basic needs of modern supply chain companies and financial institutions for risk control. Therefore, this paper introduces the Cox-Copula model to comprehensively assess a company's financial situation and the business health of upstream and downstream companies in the supply chain from the perspective of actual transactions between companies and fixed asset pledges. The study found that credit risk has a contagion effect in supply chain enterprises, and this contagion effect of credit risk has certain dynamic characteristics. At the same time, it was found that the impacts of macroeconomic factors and microfinance factors on credit risk contagion of supply chain finance have differences in the two dimensions of degree and direction of action.

Supply Chain System Optimization Model for Logistics Enterprises in the Internet of Things Era Based on Improved ECC Algorithm

In this article, a new method to optimize the supply chain system (SCS) of logistics companies in the era of Internet of Things (IoT) using an improved elliptic curve cryptography (ECC) algorithm was put forward.The focus of this research was to address the challenges faced by logistics organizations in adapting to the complexity of modern supply chains and meeting the growing demands of customers. The research aimed to improve the efficiency, security and reliability of supply chain management systems by utilizing advances in ECC algorithms.By conducting comprehensive performance tests of the proposed system, including interface testing, joggle testing, and system response time analysis, the study demonstrated the superiority of the system over traditional systems. The research findings indicated that the system based on the improved ECC algorithm outperformed traditional systems in terms of interface effectiveness, usability, and system response time. The improved ECC algorithm enhanced the security of the encryption process, making it more resistant to unauthorized access, and improved the system’s overall data processing load capacity. Additionally, the system demonstrated superior performance in managing logistics data and information, facilitating better decision-making and efficiency in logistics operations. Overall, this research contributes to the advancement of supply chain management in the IoT era by providing a practical and efficient solution for logistics enterprises to optimize their supply chain systems. The innovative application of the improved ECC algorithm offers a secure, reliable, and efficient approach to managing logistics operations, addressing the challenges posed by the complexities of modern supply chains.

The effect of organisational factors on adopting big data analytics in supply chain operation among companies in Saudi Arabia: The moderating role of resistance to change

Background: Big data analytics (BDA) is vital for modern supply chain operations. However, implementing BDA requires significant administrative effort to ensure success, as managers and employees may resist change. A study was conducted on large and medium-sized companies in Saudi Arabia to investigate the impact of organisational factors on BDA adoption and identify potential resistance. (hereafter Saudi Arabia).Objectives: This study investigate how organisational factors influence the adoption of BDA in medium and large supply chain companies in Saudi Arabia. It also explores the moderating role of resistance to change (RTC) in this relationship.Method: A survey questionnaire was given to 402 supply chain practitioners working in medium and large enterprises (MLEs) in Saudi Arabia as part of a quantitative study. The collected data were analysed using the Statistical Package for Social Science (SPSS29.0) and SMART PLS (version 4.0).Results: The findings indicated that organisational factors (top management support, IT expertise, and organisational resources) insignificantly influenced BDA adoption (p 0.05). Furthermore, RTC did not moderate the relationship between organisational factors and technology adoption (p 0.05).Conclusion: The successful use of BDA is crucial for improving Saudi supply chain operations, with a positive impact on customers, companies, and the country.Contribution: This study is important for medium and large companies in the Saudi Arabian supply chain. It provides scope for developing innovations in adopting BDA by understanding organisational factors that influence adoption. The results will help push Saudi companies forward in trusting BDA in supply chain operations.

Optimizing risk mitigation in maritime supply chains through strategic supplier relationship management

Modern supply chains face escalating vulnerabilities, especially in the maritime industry. Traditional lean supply chain management lacks flexibility and risk mitigation measures, encouraging the proposal of agile-focused SCM. The success of companies is intricately tied to supplier performance, highlighting the pivotal role of procurement in risk mitigation through supplier relationship management (SRM) strategies. This emphasizes the role of procurement in risk mitigation since they manage suppliers using supplier relationship management (SRM) strategies. To properly apply SRM strategies, suppliers are segmented. The standard segmentation method is the Purchasing Portfolio Matrix (PPM). The downfall of this matrix is the focus on power relations, which misses a softer relationship side of SRM. The Supplier Potential Matrix (SPM) includes relationship dynamics but overlooks supply risk. A new matrix for segmentation is proposed, the Integrated Supplier Matrix (ISM), which combines and integrates the PPM and SPM. A case study in a maritime company assesses risks using the Best-Worst Method (BWM), revealing significant procurement risks such as product uniqueness, regulatory compliance, and external factors. The ISM then establishes relationships between supplier willingness, capabilities, risks, and profit impact. The findings emphasize the critical role of communication and trust in managing trade-offs within supplier relationship management (SRM).