A demand forecast model using a combination of surrogate data analysis and optimal neural network approach

As rough or inaccurate estimation of demands is one of the main causes of the bullwhip effect harming the entire supply chain, we have developed a mathematical approach, the minimum description length (MDL), to determine the optimal artificial neural network (ANN) that can provide accurate demand forecasts. Two types of simulated customer and one practical demand are employed to validate the capability of the MDL method. Since stochastic factors hidden in the demand data disturb the prediction, the surrogate data method is proposed for identifying the characteristics of the demand data. This method excludes demands that are totally stochastic when forecasting. We demonstrate how optimal models estimated by MDL are consistent with the dynamics of demand data identified by the surrogate data method. The complementary approach of the surrogate data method and neural network constitutes a comprehensive framework for making various demand predictions. This framework is applicable to a wide variety of real-world data.

► This work attempts to improve the forecasting model using generalization.
► A MDL-optimal neural network model is proposed to avoid the overfitting trap.
► Historical, linear, nonlinear and practical demands are used to examine the model.
► All-round accurate prediction is achieved in comparison with those typical methods.