Supply Chain Research

Abstract Industry 4.0 is revolutionizing manufacturing by integrating advanced technologies like IoT, AI, and robotics to enable higher levels of automation, real-time insights, and operational agility. This transformation offers substantial opportunities but also poses challenges in achieving manufacturing excellence through enhanced quality, efficiency, responsiveness, and sustainability. This paper reviews the key technological enablers, strategic priorities, and organizational changes driving smart, resilient, and sustainable manufacturing in the Industry 4.0 era. It proposes an integrated methodology that combines digital technologies—including IoT, AI, digital twins, and collaborative robotics—with a human-centered approach. The methodology organizes Industry 4.0 strategies into five domains: Quality & Innovation, Asset & Operations, Supply Chain & Logistics, Safety & Sustainability, and People & Customer Engagement, demonstrating how these areas improve efficiency, asset management, supply chain transparency, safety, and workforce capabilities to enhance agility and competitiveness. Central to this is the use of cyber-physical systems for real-time data exchange, transforming factories into adaptive smart ecosystems. Challenges such as legacy system integration, cybersecurity risks, skill gaps, and resistance to change are addressed through phased adoption, robust security, continuous workforce development, pilot projects, and strong leadership. The framework integrates Lean, Six Sigma, and Agile methods with real-time analytics, an enhanced DMAIC cycle, and adaptive KPIs to drive continuous improvement and operational excellence. By aligning technology, processes, people, and sustainability, it supports building agile, resilient, and sustainable production systems that secure long-term competitive advantage. This conceptual framework presents a practical roadmap for Industry 4.0 transformation and will be further refined and validated through future case studies and pilot projects to facilitate wider adoption and ongoing improvement.

Abstract To forecast future purchases across several product categories—including fresh milk, groceries, frozen foods, detergents, paper, and delicatessen—this study looks at the purchasing patterns of wholesale clients using machine learning models. Support Vector Machines, Generalised Linear Models, and Linear Regression were among the predictive models used. RMSE, MAPE, and R² metrics were used to assess each model's performance and ascertain its correctness. The results show that when it came to forecasting consumer purchases, GLM performed better than other models, including LR, SVM, RT, and ER.

Abstract This paper investigates the impact of Program Curriculum Development (PCD) and Instructor Portfolio Management (IPM) on producing Skilled Graduates (SG), and the subsequent effect on the Betterment of Society (BS) within the context of Bangladeshi universities. Building on the premise that aligning education with industry requirements is crucial for bridging the skills gap, this quantitative study utilized a sample of 400 respondents from educational institutions and employers in Bangladesh. Data were analyzed using Exploratory Factor Analysis, Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM). The findings reveal that both PCD (β=0.25, p<0.001) and IPM (β=0.16, p<0.001) significantly and positively influence the development of Skilled Graduates. Furthermore, Skilled Graduates significantly contribute to the BS (β=0.796, p<0.001). However, the direct impacts of PCD and IPM on the BS were found to be insignificant, highlighting that Skilled Graduates act as a crucial mediator in this relationship. The study concludes that an integrated approach focusing on robust curriculum development and effective instructor management is essential for producing graduates whose skills translate into tangible societal benefits. The findings offer significant implications for university administrators, policymakers, and industry stakeholders aiming to enhance the efficacy of the education supply chain in Bangladesh.

Abstract This research examines the critical challenges and emerging opportunities in the last-mile e-commerce logistics sector in Pakistan, a rapidly growing yet underdeveloped market segment. The study aims to analyze logistical inefficiencies, technological integration, and consumer expectations that influence the operational and strategic dynamics of last-mile delivery services. A mixed-methods approach was employed, combining quantitative surveys of e-commerce consumers and delivery service providers with qualitative interviews of industry experts. Key findings reveal that logistical bottlenecks, such as inadequate infrastructure, traffic congestion, and limited access to remote areas, significantly impede timely deliveries. Furthermore, the lack of standardized digital platforms and reliance on cash-on-delivery mechanisms exacerbate operational inefficiencies. On the other hand, the research identifies significant opportunities for improvement, including the integration of advanced logistics technologies such as route optimization and real-time tracking, along with the adoption of alternative delivery models like micro-fulfillment centers and crowd-sourced delivery networks. Additionally, consumer awareness and trust in digital payment systems have been steadily increasing, creating avenues for streamlined operations. The study concludes that addressing infrastructure deficits and leveraging technology-driven solutions can enhance efficiency, reduce costs, and improve customer satisfaction, positioning Pakistan’s e-commerce logistics sector for sustainable growth. Recommendations for policy and industry practices are also proposed to optimize last-mile delivery performance.