A prediction model of the total cost considering functional risk and activity-based cost – A real case study
This paper studies on the cost of order, inventory and functional risk in supply chain. The purpose of this paper is to predict the total cost of hose production in different periods using moving average, exponential smoothing, and multiple linear regression methods. The data collection included the cost of maintenance, production, inventory and purchasing raw materials, the rate of purchasing raw material, production, inventory, and demand during March 2016 to March 2018. The best model was identified by comparison of three methods using MAPE and MSE. The results presented that the multiple linear regression model has the lowest MSE and MAPE values and can reduce error up to 54% of MAPE and 14% of MSE in comparison with moving average and up to 83% of MAPE and 76% of MSE in comparison with multiple linear regression. Forecasting the total cost in planning and manufacturing factories policy helps industrial engineers effectively.