Order Demand Forecast Using a Combined Approach of Stepwise Linear Regression Coefficients and Artificial Neural Network

Abstract

Nowadays, businesses' forecasts to meet the demands have become more critical. This study aimed to predict the fifteen-day order demand for an order fulfillment center using a Multilayer Perceptron Neural Network (MLPNN). The dataset used in the study was created from a real database of a large Brazilian logistics company and thirteen variables. Linear Regression Coefficients (LRC) were used as a feature selection method to reduce estimation errors. The study showed that among the variables, order type_A (A5), order type_B (A6), and order type_C (A7) had the most significant impact on total order forecasting. The effect of A6 was found to be greater than the effect of A7 and A5. The performance of the proposed model was evaluated using the mean absolute percent error (MAPE). LRC-MLPNN provided a MAPE of 2.97%. The results showed that better forecasting performance was obtained by selecting the independent variables to be used as input to the forecasting model with LRC. The proposed model can also be applied to different estimation problems.