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The most common challenge faced in managing the inventory replenishment is to prevent running out of inventory, eliminating higher safety stocks and ensuring right margins for the products. Effective inventory replenishment decisions are critical for businesses to maintain profitability while meeting customer needs. In this study we present mixed integer linear programming (MILP) models that formulates a coordinated inventory replenishment that simultaneously optimizes inventory classification and replenishment decisions while considering transportation requirements between multiple distribution centers (DCs) and many stores. The main objective of the model is to maximize the profits. In this study we explore and analyze replenishment needs by selecting cycle service level (CSLs) using the store level forecasted demand across multiple SKUs considering different attributes like cost of the products, store level budgets, holding cost and transportation cost. The proposed approach has also been experimented with stationary and nonstationary demand patterns generated using statistical distributions to bring out the business implications and scalability query.

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Integrated Dynamic Inventory Classification and Replenishment in Multi-echelon Supply Chain Networks

  • 会议论文

  • 发表日期：

    2025年

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  • R. Sendhil Kumar,
  • Viswanath Kumar Ganesan,
  • K. Haripriya,
  • Vijay Kumar Gundapuneedi,
  • Pradumna Krishna Kargi,
  • Usha Mohan

摘要

The most common challenge faced in managing the inventory replenishment is to prevent running out of inventory, eliminating higher safety stocks and ensuring right margins for the products. Effective inventory replenishment decisions are critical for businesses to maintain profitability while meeting customer needs. In this study we present mixed integer linear programming (MILP) models that formulates a coordinated inventory replenishment that simultaneously optimizes inventory classification and replenishment decisions while considering transportation requirements between multiple distribution centers (DCs) and many stores. The main objective of the model is to maximize the profits. In this study we explore and analyze replenishment needs by selecting cycle service level (CSLs) using the store level forecasted demand across multiple SKUs considering different attributes like cost of the products, store level budgets, holding cost and transportation cost. The proposed approach has also been experimented with stationary and nonstationary demand patterns generated using statistical distributions to bring out the business implications and scalability query.