Probabilistic Modeling for Inventory Management of Consumer Products with Independent Demand

Abstract

The main objective of this research article is to optimize the control of products in a fast-food chain, considering the variability of demand. According to the data generated from the sample, they provide us with great randomness in the sale of products, which generates uncertainty when establishing a certain value in the initial inventory, sometimes obtaining too much stock or lack of products, generating loss for the business. To solve this problem, a one-month sample of historical hotdog sales was carried out and they were analyzed in a Stat::Fit program to determine the probability distribution that the data follows, then the Monte Carlo simulation method is used together with to the normal probability distribution to generate approximate values of the daily demand for this, the programming was done in Visual Basic for Applications (VBA) in Excel, in this way to execute the n times that is desired and have n observations on the behavior of the daily and annual demand for the product. To implement the EOQ model without shortage, which helps us with an optimal forecast of how much is the quantity to order and when to place the order, through the application of this model it is possible to obtain an order quantity of 46 packages. of sausages compared to the 50 packages requested by the local: thus, reducing purchasing costs by 8%.