Enhancing Supply Chain Agility and Sustainability through Machine Learning: Optimization Techniques for Logistics and Inventory Management

Vikram Pasupuleti, Bharadwaj Thuraka, Chandra Shikhi Kodete and Saiteja Malisetty ()
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Vikram Pasupuleti: School of Technology, Eastern Illinois University, Charleston, IL 61920, USA
Bharadwaj Thuraka: School of Computer Science and Information Systems, Northwest Missouri State University, Maryville, MO 64468, USA
Chandra Shikhi Kodete: School of Technology, Eastern Illinois University, Charleston, IL 61920, USA
Saiteja Malisetty: College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA

Logistics, 2024, vol. 8, issue 3, 1-16

Abstract: Background : In the current global market, supply chains are increasingly complex, necessitating agile and sustainable management strategies. Traditional analytical methods often fall short in addressing these challenges, creating a need for more advanced approaches. Methods : This study leverages advanced machine learning (ML) techniques to enhance logistics and inventory man-agement. Using historical data from a multinational retail corporation, including sales, inventory levels, order fulfillment rates, and operational costs, we applied a variety of ML algorithms, in-cluding regression, classification, clustering, and time series analysis. Results : The application of these ML models resulted in significant improvements across key operational areas. We achieved a 15% increase in demand forecasting accuracy, a 10% reduction in overstock and stockouts, and a 95% accuracy in predicting order fulfillment timelines. Additionally, the approach identified at-risk shipments and enabled customer segmentation based on delivery preferences, leading to more personalized service offerings. Conclusions : Our evaluation demonstrates the transforma-tive potential of ML in making supply chain operations more responsive and data-driven. The study underscores the importance of adopting advanced technologies to enhance deci-sion-making, evidenced by a 12% improvement in lead time efficiency, a silhouette coefficient of 0.75 for clustering, and an 8% reduction in replenishment errors.

Keywords: machine learning; supply chain optimization; logistics management; predictive analytics; inventory optimization; customer segmentation; time series analysis (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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