Analysis of inventory system with a Stochastic inventory model : case study of steel industries in Thailand

Abstract

The objectives of this research were to select the most appropriate forecasting model for uncertain demand of hot-rolled steel and to find the purchase amount and the reorder point of some raw materials in order to minimize the total cost of production in an iron and steel production planning. The three forecasting models which were exponential model, ARIMS model, and the proposed Bayesian model were studied. An iron and steel factory in Samutprakarn province was a prototype for this study. Because of their uncertain high demand, the Steel H-Beam, Steel plate, steel round bar, and black steel round bar with various sizes were selected, and their parameters including demand rate, inventory cost, back order cost, and setup cost were collected. The study found that the proposed Bayesian forecasting model is the most appropriate. The predicted demand and all parameter values were used to find the purchase amounts and the reorder points of the raw materials under the (r,Q) policy of the stochastic inventory model.