Utilizing Simulation to Build a Retail Store Inventory Optimization Model

Abstract

The researchers build an inventory model for retail stores by validating their economicorder quantity through data driven simulation. This paper created an inventoryoptimization model for a personal care retailing business, to avoid stock out and minimize their holding cost and ordering cost. Simulating a thousand different scenarios, the research come up with an optimal inventory model for the two most sellable products in the store. The t-test reveals that product A has a significantly higher demand than product B. The simulation model validates the optimal order quantity of 59 units, with a reorder point of 25 units for product A. However, the simulation model recommends an optimal order quantity of 37 units and a reorder point of 10 units for product B. The Kolmogorov-Smirnov Goodness of Fit Test reveals the normal distribution of the 30 days inventory for Product A but not for Product B. Confirming that stocks out will unlikely happen for product A but will probably occur for product B. The model confirms EOQ findings of product with relatively high demand but low price but a departure for products with low demand but the high price.Keywords: Operations management, retail inventory system, t-test, Monte Carlo Simulation,Kolmogorov-Smirnov Goodness of Fit Test, Davao City, Philippines, Southeast Asia