DYNAMIC PROBABILISTIC MODEL FOR MANUFACTURING AND PRODUCTION: AN ADAPTIVE APPROACH TO DEMAND AND SUPPLY UNCERTAINTIES

Abstract

In modern manufacturing and production systems, uncertainty in both demand and supply poses critical challenges in maintaining optimal inventory levels. This study proposes a dynamic probabilistic inventory model that incorporates stochastic demand following a normal distribution and lead time variability modelled through a lognormal distribution. The model provides an adaptive framework to support decision-making in uncertain environments, emphasizing cost minimization while ensuring service level efficiency. A detailed mathematical formulation is presented, along with a solution using probabilistic analysis and optimization. A numerical example illustrates the application, followed by a sensitivity analysis highlighting the impact of key parameters. This model offers significant improvements over traditional deterministic approaches and holds potential for broad application across manufacturing, supply chain, and logistics operations.