The objective of this research is to propose a forecasting model for uncertain demand index data of steel. The forecasting model proposed by Yelland (2010) was modified by adjusting the prior distributions of some parameters in the model in order that it is suitable for the demand of steel in Thailand. The algorithms for model fitting were written in OpenBUGS. The proposed model was compared to a classical exponential smoothing model. The research found that the forecast errors from the proposed forecasting model were minimum.

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 (rQ) policy of the stochastic inventory model.