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International Journal of Supply Chain Management
Published by ExcelingTech
ISSN : 20513771     EISSN : 20507399     DOI : -
Core Subject : Science, Social, Engineering,
Decision Sciences, Operations Research & Management Engineering Environmental Science Industrial & Manufacturing Engineering Transportation
International Journal of Supply Chain Management (IJSCM) is a peer-reviewed indexed journal, ISSN: 2050-7399 (Online), 2051-3771 (Print), that publishes original, high quality, supply chain management empirical research that will have a significant impact on SCM theory and practice. Manuscripts accepted for publication in IJSCM must have clear implications for Supply chain managers based on one or more of a variety of rigorous research methodologies. IJSCM also publishes insightful meta-analyses of the SCM literature, conceptual/theoretical studies with clear implications for practice, comments on past articles, studies concerning the SCM field itself, and other such matters relevant to SCM.
Arjuna Subject : -
Articles 2,561 Documents
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Real-Time Visibility for Building Adaptive Resilience Wycislak, Slawomir; Akhtar, Atif
International Journal of Supply Chain Management Vol 14, No 3 (2025): International Journal of Supply Chain Management (IJSCM)
Publisher : ExcelingTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59160/ijscm.v14i3.6311

Abstract

Supply chain resilience has become critical in today's volatile global landscape. Recent disruptions have exposed vulnerabilities in interconnected supply networks, highlighting the need for new approaches to managing complex, rapidly evolving challenges. This research investigates how real-time visibility facilitates adaptive resilience in global supply chains through an integrated theoretical framework. Using a mixed- method approach, we analyze five major recent supply chain disruptions including the Red Sea crisis, Panama Canal drought, COVID-19 outbreaks, Baltimore bridge collapse, Israel-Iran conflict. We develop the Real-Time Adaptive Resilience (RTAR) model, a four- layer framework that explains how visibility transforms data into adaptive capabilities. Our findings reveal three key mechanisms through which real-time visibility enhances resilience: information velocity, system-wide transparency, and predictive capability. The research contributes to supply chain management theory by integrating Resource-Based View, Dynamic Capabilities, and Systems Dynamics perspectives while providing practical guidance for building adaptive supply chain capabilities.
Optimizing Spare Parts Inventory and Logistics for Maximum Plant Uptime in the Energy Sector Narayanan, Shankar Bhaskaran
International Journal of Supply Chain Management Vol 14, No 2 (2025): International Journal of Supply Chain Management (IJSCM)
Publisher : ExcelingTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59160/ijscm.v14i2.6305

Abstract

Equipments play a critical role in Oil Gas (ONG) operations. Failure of their proper and efficient functioning has direct and significant bearing on plant uptime, energy output and supply chain (SC) stability. This article delves into the mechanical failure modes and predictive maintenance techniques, to enhance spare part forecasting. It establishes the usefulness of predictive analytics in deciding optimum spare parts inventory levels necessary for ensuring cost rationalization for balancing operational cost and efficiency. The article focuses on the application of sophisticated technology for achieving maximum plant uptime.
Key Metrics for Evaluating Maritime Supply Chain Performance: Insights from Literature Hye, Mohammad Nazmuzzaman; Habib, Md. Mamun
International Journal of Supply Chain Management Vol 14, No 2 (2025): International Journal of Supply Chain Management (IJSCM)
Publisher : ExcelingTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59160/ijscm.v14i2.6301

Abstract

Evaluating performance metrics in maritime supply chain in crucial for achieving operational efficiency, resilience, and service reliability in an increasingly complex global shipping environment. This Study systematically reviews 31 peer-reviewed articles published between 2010 and 2024 to identify, classify, and analyse key performance metrics used in the maritime logistics sector. The review was conducted using four major databases-ScienceDirect, Google Scholar, SpringerLink, and EBSCO Host- following the PRISMA framework. This study employs a systematic literature review (SLR), incorporating thematic analysis to identify and synthesize common patterns across the selected literature. The findings are categorized into three main dimensions: operational efficiency (e.g., berth productivity, vessel on-time performance, ship turnaround time), resilience (e.g., disruption recovery time, supply chain redundancy, routing flexibility), and servicer reliability (e.g., customer satisfaction, delivery accuracy, schedule adherence) The novelty of this paper lies in the development of a comprehensive and structured framework that integrates these key performance metrics, providing maritime stakeholders with actionable insight for performance evaluation and strategic alignment. This farmwork not only synthesizes current academic perspectives but also incorporates digitalization and technological readiness as enablers of enhanced Supply Chain performance. The outcome offers valuable guidance for decision-makers aiming to optimize resource allocation, mitigate risks, and improve overall competitiveness in maritime logistics.
Enhancing Operational Performance in the Manufacturing Sector: A Review on the Role of Supply Chain Digitization and Visibility Khan, Tahsina; Habib, Md. Mamun
International Journal of Supply Chain Management Vol 14, No 3 (2025): International Journal of Supply Chain Management (IJSCM)
Publisher : ExcelingTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59160/ijscm.v14i3.6313

Abstract

The objective of this research is to comprehensively assess the impact of digitization and enhanced visibility in supply chain performance improvement of the manufacturing industry. The methodological approach adopted was systematic in examining contemporary empirical research and frameworks focused on supply chain digitization and visibility. The research shows that various indicators of performance are significantly improved by combining processes such as supply chain digitization with better visibility, including reduced lead times, more effective inventory management, and increased responsiveness in supply chain. Although many of these breakthroughs have been accomplished, there are still numerous practical challenges, like harmonizing data systems, protecting data, and dealing with resistance from the users. The study will be of value to the practitioners because it emphasizes the pivotal role played by blending the use of digital solutions with visibility practices in simplifying supply chain processes. The study suggests that the digital strategies to supply chain management could lead to greater resilience and adaptability, and as such, enhance better efficiency in supply chain management. This analysis provides a new insight into the combination of digitization and visibility in supply chains, a unified picture of the combined contributions to operational success.
Blockchain Integration in Sustainable Letters of Credit Supply Chain Majumder, Mohammad Ismail; Habib, Professor Md Mamun
International Journal of Supply Chain Management Vol 14, No 2 (2025): International Journal of Supply Chain Management (IJSCM)
Publisher : ExcelingTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59160/ijscm.v14i2.6306

Abstract

Letters of Credit (LC) are pivotal in global trade finance, providing secure payment assurance to exporters and importers. However, the traditional LC process is laden with inefficiencies, risks of forgery, and delays, particularly in developing countries like Bangladesh. In response to these challenges, Blockchain Database Integration (BDI) is gaining traction as a secure, transparent, and efficient alternative. This study investigates how BDI influences Sustainable Letters of Credit Supply Chains (SLCSC), mediated by the adoption of Technology-based Letters of Credit Supply Chains (LCSC). Drawing on a sample of 400 respondents from LC-related sectors in Bangladesh, the study employed Exploratory Factor Analysis (EFA) and Structural Equation Modeling (SEM) to assess the validity and reliability of the proposed reflective measurement model. Two key hypotheses were tested, and the results supported both the direct impact of BDI on LCSC and the mediated relationship between BDI and SLCSC through LCSC. The findings confirm that blockchain, when effectively integrated into LC operations via technological platforms and smart contracts, enables a transformative path toward eco-efficient and transparent supply chain practices. The study contributes both theoretically and practically to the discourse on digital trade finance and sustainable development.
The Supply Chain Maze: The Struggles of Small and Medium Enterprises and Ways to Navigate the Challenges Natesh Kumar, Nitin
International Journal of Supply Chain Management Vol 14, No 2 (2025): International Journal of Supply Chain Management (IJSCM)
Publisher : ExcelingTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59160/ijscm.v14i2.6304

Abstract

Small and Medium Enterprises (SMEs) face significant challenges in managing supply chains due to fragmented communication, limited resources, and outdated technologies. Without a centralized platform, inefficiencies arise, leading to missed deadlines, inaccurate inventory levels, and supply- demand mismatches. This article explores these challenges and presents strategies to enhance SME resilience and profitability. By adopting modern SCM technologies such as cloud-based systems, real-time tracking, and predictive analytics, SMEs can improve operational efficiency and decision-making. Diversifying supplier relationships and leveraging trade finance solutions can help mitigate risks and ensure financial stability. Additionally, outsourcing logistics and customer service can reduce operational burdens while sustainability initiatives offer long-term cost savings and market competitiveness. Implementing these strategies will not only strengthen SME supply chains but also support policymakers in formulating practical measures to enhance business resilience in an increasingly complex global economy.
The Evolution of Purchasing Models in the Semiconductor Supply Chain: A Literature Review Johnson, Laquanda Leaven; Ebakivie, Oghenetejiri; Johnson, Sean M.
International Journal of Supply Chain Management Vol 14, No 3 (2025): International Journal of Supply Chain Management (IJSCM)
Publisher : ExcelingTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59160/ijscm.v14i3.6312

Abstract

Semiconductors are crucial components of modern technology, with uses ranging from communications to national security. As demand for semiconductors rises, it is critical to understand procurement methods, which have a considerable impact on pricing and supply chain dynamics. The study provides an in-depth analysis of the literature on the evolution of purchasing models in the semiconductor industry, including historical and current viewpoints. It analyzes four key purchasing models: direct purchasing, distributor purchasing, value-added resellers (VAR), and online purchasing, each with unique strategic advantages and problems that impact buyer-supplier relationships, cost, and delivery consistency. This study contributes to the literature through providing a comprehensive review of the semiconductor supply chain while exploring available purchasing models and identifying factors that help shape purchasing decisions. The research delves into the historical development of various models, their current implementation, and potential future trends. Additionally, the research identifies existing gaps in the literature and suggests areas for future investigation to further enhance supply chain efficiency and effectiveness. The findings from this study will provide valuable insights for the semiconductor industry, offering strategies to optimize their supply chain, improve resilience, and adapt to emerging challenges.
The Final Frontier of Logistics: Artificial Intelligence in the Last Mile Delivery Natesh Kumar, Nitin
International Journal of Supply Chain Management Vol 14, No 3 (2025): International Journal of Supply Chain Management (IJSCM)
Publisher : ExcelingTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59160/ijscm.v14i3.6307

Abstract

Last-mile delivery (LMD), the most critical segment of the modern supply chain (SC), plays a vital role in shaping customer satisfaction but also accounts for the highest cost among all supply chain activities. Particularly in the context of e-commerce, it serves as a key differentiator while grappling with challenges such as rising customer expectations, urban traffic congestion, adverse weather, and sustainability concerns. This article explores how Artificial Intelligence (AI) and its subset, Machine Learning (ML), can be leveraged to optimize last-mile delivery. With the growing wave of digital transformation across the globe, this area is gaining increased attention. The article adds to the relatively limited body of research on this topic and concludes that AI and ML have the potential to significantly enhance supply chain efficiency, especially in last-mile delivery, by reducing costs, accelerating delivery speeds, and ultimately improving customer satisfaction.
Use of Machine Learning in Predicting Electric School Bus Battery Range for Optimized Routing Sharma, Aditya Kumar
International Journal of Supply Chain Management Vol 14, No 2 (2025): International Journal of Supply Chain Management (IJSCM)
Publisher : ExcelingTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59160/ijscm.v14i2.6290

Abstract

The transition to electric school buses (ESBs) promises significant environmental and economic benefits. However, optimizing their operations remains a challenge due to the limited and variable range of their batteries. This paper contributes to addressing this challenge by introducing a machine learning (ML)-based framework for accurately predicting ESB battery range under diverse operational conditions. By leveraging historical and real-time data on energy consumption, traffic patterns, weather conditions, and charging infrastructure, this study develops predictive models that enhance routing efficiency, reduce operational costs, and improve fleet reliability. Our approach integrates advanced ML techniques such as regression models, ensemble learning, and neural networks to create robust range predictions. The study's key contributions include (1) the development of a comprehensive ML-driven predictive model tailored for ESB fleets, (2) the integration of real-time environmental and operational data for dynamic decision-making, and (3) the demonstration of the model's effectiveness through numerical experiments using both simulated and real-world datasets. The findings illustrate the potential of ML in optimizing ESB routing and reducing energy wastage, paving the way for more sustainable student transportation systems.
The Sharon Waves Theory as A QuantumInspired Theory for Supply Chain Management Sharma, Ashutosh; Saluti, Dean
International Journal of Supply Chain Management Vol 14, No 3 (2025): International Journal of Supply Chain Management (IJSCM)
Publisher : ExcelingTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59160/ijscm.v14i3.6316

Abstract

Modern supply chains are increasingly unpredictable and interconnected, making traditional forecasting and optimization models inadequate. This paper introduces a novel framework inspired by quantum mechanics to better capture the uncertainty and complexity of supply chains. By modeling inventory, demand, and risk as quantum-like entities called sharons, the framework treats these components as both particles (discrete units) and waves (continuous flows). The resulting Sharon wave function represents all possible supply chain states and evolves over time using principles like superposition, interference, and uncertainty. We define quantum-inspired operators to extract insights into demand, inventory, cost, and risk. Through mathematical modeling and real-world data, we demonstrate how this approach improves forecasting accuracy and enables dynamic strategy optimization. This framework offers a new lens to understand and manage supply chain behavior under uncertainty and aligns well with emerging quantum computing technologies.

Page 254 of 257 | Total Record : 2561

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