Descriptive and Predictive Analytics for Performance Improvement of Inbound Logistics

During the pandemic, many firms realized the importance of the supply chain in their business. Firms realized areas where they were lagging and worked to improve their supply chain operations. The buzzwords like data analytics and machine learning became even more popular at such times because these techniques are effective in making processes more efficient. In this paper, we present practical applications of data analytics for improving inbound logistics. The aim of this paper to improve the On-Time & In-Full (OTIF) metric by using data-driven approaches to distill insights and make appropriate recommendations. To date, this effort has resulted in a 15% improvement in the on-time metric for a particular vendor by introducing a drop-trailer program. These recommendations have been thoroughly discussed with the management team, which led to building a machine learning model to predict the number of incoming pallets. Accurate prediction of incoming pallets enables managers of distribution centers to plan their labor as per the number of pallets. Adopting the machine learning model enabled a reduction in MAPE by 14%, which means we improved the accuracy by 14%. Future work can focus on scaling this to all vendors, which can be expected to contribute significantly to improving productivity and customer service.

Advisor: Dr. Vittaldas Prabhu

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Work Title Descriptive and Predictive Analytics for Performance Improvement of Inbound Logistics
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Penn State
Creators
  1. Avinash Vijay Ahuja
License In Copyright (Rights Reserved)
Work Type Research Paper
Acknowledgments
  1. I am extremely grateful to my adviser, Prof. Vittaldas V. Prabhu for providing me this wonderful opportunity to learn. His constant guidance and support have motivated me to pursue my supply chain interest to another level. I am thankful to Prof. Paul Griffin for being the reader for my paper. I am thankful to Dr. Robert Voigt and the department for providing all the necessary facilities and project roadmap to support my research work.
Publication Date 2021
Deposited June 20, 2021

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    Acknowledgments
    • I am extremely grateful to my adviser, Prof. Vittaldas V. Prabhu for providing me this wonderful opportunity to learn. His constant guidance and support have motivated me to pursue my supply chain interest to another level. I am thankful to Prof. Paul Griffin for being the reader for my paper. I am thankful to Dr. Robert Voigt and the department for providing all the necessary facilities and project roadmap to support my research work.
  • Added Creator Avinash Vijay Ahuja
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