Inventory Model with Stochastic Demand Using Single-Period Inventory Model and Gaussian Process

Proper inventory management is vital to achieving sustainability within a supply chain and also related company’s cash flow through the funds represented by inventory. Therefore, it necessary balance excess insufficient However, this can be difficult achieve in presence of stochastic demand because decisions must made an uncertain environment policy bears risks associated with each decision. This study reports extension single-period model for problem under demand. We proposed incorporating Gaussian process into using posterior distribution as enables modeling data from historical theory, which adapts well small datasets provides measurements predictions made. Thus, unlike other works that assume follows autoregressive or Brownian motion model, among others, our approach adaptability different complex forms trends over time. offer several numerical examples explore aspects compare results those achieved state-of-the-art methods.