Multi-Location, Multi-Product Inventory Management Applying Greedy Heuristics: A Case Of Dai Phat Company Limited

Abstract

Historically, the supply chain's echelons, such as warehouses, distributors, and retailers, were managed independently, buffered by large inventories. Increasing competitive pressures and market globalization are forcing firms to develop supply chains that can respond quickly to customer needs; therefore, using multi-location, multi-product inventory management interactively is recommended in assisting companies in reducing operating costs and improving customer service. The current paper analyses and evaluates the problem in the distribution system of a specific company - DAI PHAT with the aim to propose a collaborative multi-echelon lighting product supply chain composed of a central distribution center, multi-regional warehouses, and multi-retailers using a stochastic multi-period and multi-product mathematical model for the minimization of the total operating cost (simply expressed in 2 types of cost: inventory management costs, and ordering cost). The Greedy Heuristics is used to find out the solution of the problem built from a mix-integer linear program model, which is run by Python software. The yielded results help to define the location of the new warehouse, whether to order, the quantity of the order, optimal safety stock, and inventory at each level corresponding to each Regional Warehouse if needed for total cost reduction. Ultimately, to highlight the credibility of the results from this research, Google Sheet implementation was applied. Moreover, an execution of the CPLEX program is also applied to check the validity of the Greedy Heuristics for this case. By conducting a sensitivity analysis, it is discovered that certain parameters including inventory-holding cost and lead time are considered to be the significant data as they have the most influence towards the optimal result while ordering costs are seen to be a lesser influence on the ending total cost.