An Analytical Approach to Inventory Management under Truncated Normal Demand Distribution

Abstract

This paper analyses inventory control strategies that function under a truncated normal demand distribution and account for lead time variations. Many inventory systems depend on precise demand and lead time forecasts to maintain ideal stock levels. The goal of this study is to incorporate the truncated normal distribution into a reliable decision support system because there is currently no closed-form solution for the demand distribution over the full lead period. The probability density function (PDF) that most closely matches past demand data is used by this approach to build the lead time demand distribution.

This is done by estimating the lead time demand distribution using a mixture of truncated exponentials (MTE) once the pertinent parameters have been established. Restocking decisions are dependent on unpredictable future variables, such as replenishment lead times, demand frequency, and quantities requested throughout this period, in situations where stock acts as a buffer between supply and demand.

According to experimental results, the suggested model outperforms alternative possible approximations in improving optimal inventory policies and lowering predicted inventory costs where the Truncated normal distribution is the best fit.