Beyond Adoption: Stakeholders' Roles in the Diffusion of Sustainable Supply Chain Management - A Case Study Analysis

This study examines the wide-ranging impact of companies' decisions to adopt Sustainable Supply Chain Management (SSCM) practices on their stakeholders and the subsequent diffusion of these practices across the stakeholder network. Drawing on game theory, the research investigates how stakeholder interactions and reciprocal influences contribute to the spread of sustainable practices within and beyond organizational boundaries. Through a qualitative, multiple case study approach involving three major Moroccan companies, the study employs triangulation and cross-case analysis to uncover the complex dynamics underlying SSCM implementation. The findings reveal that stakeholder relationships evolve into iterative, strategic interactions that not only reinforce sustainability adoption within individual firms but also catalyse broader diffusion across the supply chain ecosystem.The study contributes to SSCM literature by: (1) clarifying the mechanisms grounded in gametheoretical principles-that underpin sustainability diffusion, and (2) offering rare empirical insights from an emerging market context. By linking SSCM adoption to strategic stakeholder engagement, the research underscores its transformative potential for sustainable development. These insights are particularly valuable for policymakers, practitioners, and scholars seeking to leverage SSCM for broader societal impact

The Evolution of Purchasing Models in the Semiconductor Supply Chain: A Literature Review

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

Use of Machine Learning in Predicting Electric School Bus Battery Range for Optimized Routing

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

Real-Time Visibility for Building Adaptive Resilience

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 Liquified Natural Gas (LNG) Transportation & Logistics - Application of Compressors and Al-Driven Analytics

Liquefied Natural Gas (LNG) transportation is not just a fascinating process but also a crucial activity in the international energy market. Countries across the world are switching over from coal and crude oil to natural gas to lower carbon footprint. But natural gas has to be transported, generally over long distances, from source to place of consumption and that to in the form of liquid. Conversion of natural gas into LNG facilitates its comparatively easy and safe transport, particularly where distances are large. The entire process of transformation and transportation is very complex, but demands study due to the growing importance of LNG as an alternative fuel – a crucial element in energy transition and sustainability. This article explores the role played by compressors in the transportation of LNG. The article while adding to the pool of literature on LNG transport optimization, establishes that compressors are vital to optimizing LNG transportation and logistics. This article also establishes the utility of Artificial Intelligence (AI) in improving profitability of the players. It shows how predictive analytics be useful in enhancing the efficiency and economy of LNG transportation through the churning of huge volume of data generated at every step of the transportation process and then use it effectively to improve performance

Role of Blockchain Technology in Supply Chain Management

Blockchain technology has drew interest for its capability in supply chain management to has prompt efficiency in terms of improved transparency. Blockchain has received a lot of attention with its promises of improving supply chain through accountability. This paper aims to evaluate the benefits and risks of incorporating blockchain in supply chain management networks with especial focus on peer-reviewed articles, conference papers, and literatures documented between 2015 to 2021. From the paper, it is apparent that blockchain brings critical advantages, such as real-time tracking of product movement, less fake products, and fewer errors, which is stored in the shared platform for every stakeholder’s view. Also, the use of smart contracts promotes automated execution of a contract so that the responsibility and performance of agreements are enhanced as well as operationalism. Combine examples of Walmart and IBM’s operation solution Food Trust and the shipping logistics platform Trade Lens to demonstrate the use of blockchain for the optimistic outcome in different fields. However, the broad implementation of blockchain has some barriers; these are; reluctance by the supply chain executor to adopt new structures and systems among partners, high initial costs, and diversity with blockchain solutions that does not support operational norms across platforms. These challenges therefore call for more concentrated efforts in enhancing the understanding of education on blockchain technology and supply chain actors, setting of standard practices for the ecosystem and associating the pertinent players to allow for the considerably more effective operation of the technology. This research is useful for those academics as well as practitioners who are planning to pursue the strategy of blockchain implementation in supply chain management. The direction for further research should be to address the mentioned challenges and identify the state-of-the-art approaches, formats and standards, and the ways of implementing them in practice

Reverse Supply Chain and Its Effect on Customer Satisfaction: A Comparative and SERVQUAL Analysis of Retail Practices

Reverse supply chain (RSC) operations have become increasingly critical for retail firms seeking to enhance sustainability, reduce costs, and retain customer loyalty. While traditional supply chain research emphasizes forward logistics, customer satisfaction in reverse logistics remains underexplored. This study investigates the relationship between RSC processes and customer satisfaction using qualitative semi-structured interviews with six U.S.-based supply chain professionals. It applies Qualitative Comparative Analysis (QCA) and the SERVQUAL model to analyze the configurations of RSC conditions that influence customer experience. Four key drivers emerged: return process efficiency, communication and transparency, employee competence, and alignment with customer expectations. A truth table developed using fuzzy set QCA 3.0 reveals five RSC condition combinations that consistently lead to high satisfaction. Customer communication and a hasslefree return process were necessary in all successful configurations. This study highlights best practices, including flexible return options, use of return metrics, staff training, and clear return policies. It contributes to the existing literature by reconceptualizing the reverse supply chain as not merely a cost recovery or operational function, but as a strategic service platform for generating competitive advantage in retail logistics. Further, this research offers a novel analytical framework capable of identifying multiple high-performance pathways rather than prescribing a one-size-fits-all solution. The emphasis on equifinality, or the possibility of different configurations leading to the same positive outcome, reflects the complexity and diversity of modern supply chain environments and aligns with the contemporary demands of supply chain managers

Use of Generative Artificial Intelligence to Create Sustainable Supply Chain Management: An Online Retail Perspective

Generative artificial intelligence (AI) refers to the type of AI that is capable of creating such new contents as music, images, texts, videos, and codes, using expansive AI models, known as foundation models, that will help to learn from and simulate large volumes of data. Modern e-commerce businesses have realized that application of such sophisticated technology in supply chain management (SCM) to create efficient and sustainable supply chains. This article explains how Generative AI can be useful in this respect especially in a world where SCM is undergoing significant transformations due to changing consumer behavior and the changing nature of trade and commerce in an internet driven world

A Scope of Implementation intellectual Capital Accounting and Supply Chain Management in Telecommunication Companies: Evidence from Iraq

Research positioned in the intersection between management accounting and supply chain management is increasing. So, the core purpose of this research study is to identify the range of harmonization between theoretical concepts of Intellectual Capital (IC) around the three aspects and empirical practices required from international and local accounting standards (IAS). The current research follows the positivist paradigm and quantitative in nature. Secondary data was used to measure the compatibility between the theoretical concepts of intellectual capital and the items that were accepted in international accounting standards across ratios, and to test these variables with TIST between the average actual practices of companies and the objective value (theoretical concepts of intellectual capital). The items will later include the items in the International Accounting Standards and the actual practices of telecommunications companies in Iraq. The results show that there are significant differences between the theoretical concepts of IC in International Accounting and Reporting Standards - IAS 38. The areas where there is no match related to items that do not meet the condition of the concept of assets or do not achieve the rules of proof of accounting or measured to be reliable. The results were derived from the analysis of the financial statements of Zain and Asia Cell and its annual reports, including analytical tables and explanatory notes. The confidentiality of some data related to the work of companies is one of the difficulties of conducting such research, as some of these data are linked to the competitiveness of companies and tax matters, as well as the lack of companies operating in this sector in Iraq. This research is the first modest attempt on social environment of Iraqi knowledge economy, which represents telecommunications companies one of its components and associated components of intellectual capital. This study makes a contribution to the literature by conceptualizing the elements of management accounting in a context of the supply chain and by relating it to supply chain strategy and supply chain relationship structure

Optimizing Spare Parts Inventory and Logistics for Maximum Plant Uptime in the Energy Sector

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