Data-driven reconfigurable supply chain design and inventory control

Dynamic capacity logistics;Mobile, transportable production;Demand learning;Joint capacity;Inventory control

Malladi, Satya Sarvani ; Erera, Alan L. White III, Chelsea C. Industrial and Systems Engineering Toriello, Alejandro Goldberg, David A. Zhou, Enlu Ghosh, Soumen ; Erera, Alan L.

University:Georgia Institute of Technology

Department:Industrial and Systems Engineering

关键词: Dynamic capacity logistics;    Mobile, transportable production;    Demand learning;    Joint capacity;    Inventory control;   

Others  :  https://smartech.gatech.edu/bitstream/1853/60237/1/MALLADI-DISSERTATION-2018.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

In this dissertation, we examine resource mobility in a supply chain that attempts to satisfy geographically distributed demand through resource sharing, where the resources can be inventory and manufacturing capacity. Our objective is to examine how resource mobility, coupled with data-driven analytics, can result in supply chains that without customer service level reduction blend the advantages of distributed production-inventory systems (e.g., fast fulfillment) and centralized systems (e.g., economies of scale, less total buffer inventory, and reduced capital expenditures). We present efficient and effective solution methods for logistics management of multi-location production-inventory systems with transportable production capacity. We present a novel, generalized representation of demand uncertainty and propose data-driven responses to the manage a single location inventory system under such demands.