Seasonal Inventory Management Model for Raw Materials in Steel Industry

Kosuke Kawakami (), Hirokazu Kobayashi () and Kazuhide Nakata ()
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Kosuke Kawakami: Department of Industrial Engineering and Economics, Tokyo Institute of Technology, Tokyo 152-8550, Japan
Hirokazu Kobayashi: Nippon Steel Corporation, Chiba 293-8511, Japan
Kazuhide Nakata: Department of Industrial Engineering and Economics, Tokyo Institute of Technology, Tokyo 152-8550, Japan

Interfaces, 2021, vol. 51, issue 4, 312-324

Abstract: We developed a seasonal inventory management model for raw materials, such as iron ore and coal, for multiple suppliers and multiple mills. The Nippon Steel Corporation imports more than 100 million tons of raw material annually by vessels from Australia, Brazil, Canada, and other countries. Once these raw materials arrive in Japan, they are transported to domestic mills and stored in yards before being treated in a blast furnace. A critical problem currently facing the industry is the limited capacity of the yards, which leads to high demurrage costs while ships wait for space to open up in the yards before they can unload. To reduce the demurrage costs, the inventory levels of the raw materials must be kept as low as possible. However, inventory levels that are too low may lead to inventory shortage resulting from seasonal supply disruptions (e.g., a cyclone in Australia) that delay the supply of raw materials. Because both excess and depleted inventory levels lead to increased costs, optimal inventory levels must be determined. To solve this problem, we developed an inventory management model that considers variations on the supply side, differences that should be observable upon looking at the ship operations. The concept is to model the probability distribution of ship arrival intervals by brand groups and mills. We divided ship operations into two stages: arrival at all mills (in Japan) and arrival at individual mills. We modeled the former as a nonhomogeneous Poisson process and the latter as a nonhomogeneous Gamma process. Our proposed model enables inventory levels to be reduced by 14% in summer and 6% in winter.

Keywords: statistical stock policy; safety stock management; multi-plants; multi-materials; two-stage model (search for similar items in EconPapers)
Date: 2021
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