Inventory optimization proposal with a simulation based approach and nonlinear programming

Inventory decisions are extremely crucial for almost any businesses. Holding or not holding inventory on hand can lead to excessive costs. These costs may create competitive disadvantage to the business. Sometimes it can be hard to determine the right decision and implement it to the related areas. There are some models to help this decision making process. These models are called inventory management models and they help to answer two key questions about inventory decisions -"when to order" and "how much to order". To answer these two questions, models include some mathematical formulas. This study aims to answer these questions without any complicated mathematical formulas. Monte Carlo simulation has been used to forecast demand and through these forecasted data, cost calculations have been done in order to maximize profit or minimize loss. Non-Linear Programming in MS Excel Solver extension has been used to optimize inventory decisions. Moreover, secondary data sources have been used to determine demand and inventory costs. As a result of the optimization process, lot size and reorder point which maximize the profit have been determined and tested.