Estimating and Modeling Uncertainties Affecting Production Throughput Using ARIMA-Multiple Linear Regression

Abstract

Throughput of each production stage cannot meet the demand in the real production system because of the disruptions and interruptions of the production line for example break time and scrap. On the other hand, demand changes over time due to volume variation and product redesign as the customers’ needs are changing. This situation leads to planning and controlling under uncertain condition. This paper proposes a hybrid model of autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR) for estimating and modeling the random variables of production line in order to forecast the throughput in presence of production variations and demand fluctuation. The random variables under consideration of this study are demand, break-time, scrap, and lead-time. The random variables are formulated in the MLR model, where the mean absolute percentage of error (MAPE) was 2.53%. Further, nine ARIMA models with different parameters in MLR model are fitted to the data and compared by their MAPE. The best model with the lowest MAPE was when the ARIMA parameters set for p=1, d=0, and q=3. Finally the proposed model using ARIMA-MLR is formulated by MAPE of 1.55%.