Significance of stochastic programming in addressing production planning under uncertain demand in the metal industry sector

Abstract

One of the most important disciplines for businesses is production planning. Production planning involves various cost elements such as labor, equipment, raw materials, and inventory while significantly impacting strategic aspects like sales, profit, and market share. Mathematical models used in production planning often address problems of cost minimization or profit maximization. However, besides deterministic-based linear programming applications, it is known that the effect of randomness also plays a significant role in production planning. When parameters are stochastic, meaning random, mathematical models must be capable of generating solutions under the influence of these random parameters. Stochastic modeling developed for problems affected by random parameters can yield the desired results. This study addresses the issue of production planning using stochastic modeling for a company that manufactures industrial-type pipe clamps and has two main product groups. The model that minimizes costs under demand uncertainty uses the Sample Average Approximation (SAA) approach. Initially, a deterministic model was established to obtain the solution when randomness was not included. Subsequently, the stochastic model was solved by creating different scenario sets using SAA, and comparison results were presented.