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All Journal International Journal of Supply Chain Management
Sharma, Aditya Kumar
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Decision Sciences, Operations Research & Management Engineering Environmental Science Industrial & Manufacturing Engineering Transportation

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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.
Predictive Analytics for Inbound Logistics: Optimizing Lead Times and Vendor Reliability Gupta, Vikas; Patro, Ravindra Kumar; Sharma, Aditya Kumar
International Journal of Supply Chain Management Vol 14, No 5 (2025): International Journal of Supply Chain Management (IJSCM)
Publisher : ExcelingTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59160/ijscm.v14i5.6348

Abstract

Lead time variability has significant impact on supply chain management (SCM) and is a critical factor that affects operational efficiency, cost management, and logistical aspects of a business. This coupled with vendor reliability is the key to quality assurance, waste reduction and cost rationalization. This article delves into the manner in which variations in lead time impact two important aspects of supply chain performance, lead time optimization and vendor management. The focus is on application of predictive analytics in this respect. The article underscores the strong potential that predictive analysis has in addressing the key threats and opportunities faced by modern supply chains. The key contribution of this research is addition to the pool of literature that covers the relatively less widely discussed areas - inbound logistics and the application of sophisticated modern technologies towards improvement of  supply chain visibility for enhancing efficiency of supply chain management. The article establishes that predictive analytics can be effectively used to facilitate data-driven decision-making in supplier management thus making decisions more proactive than reactive which significantly improves supply chain resilience.
Co-Authors Patro, Ravindra Kumar Vikas Gupta