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

被引:18
作者
Pasupuleti, Vikram [1 ]
Thuraka, Bharadwaj [2 ]
Kodete, Chandra Shikhi [1 ]
Malisetty, Saiteja [3 ]
机构
[1] Eastern Illinois Univ, Sch Technol, Charleston, IL 61920 USA
[2] Northwest Missouri State Univ, Sch Comp Sci & Informat Syst, Maryville, MO 64468 USA
[3] Univ Nebraska Omaha, Coll Informat Sci & Technol, Omaha, NE 68182 USA
来源
LOGISTICS-BASEL | 2024年 / 8卷 / 03期
关键词
machine learning; supply chain optimization; logistics management; predictive analytics; inventory optimization; customer segmentation; time series analysis;
D O I
10.3390/logistics8030073
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
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.
引用
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页数:16
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