Evaluation of aggregate and individual forecast method selection rules
Management Science
Testing for nonlinearity in time series: the method of surrogate data
Conference proceedings on Interpretation of time series from nonlinear mechanical systems
Neural networks for decision support: problems and opportunities
Decision Support Systems - Special issue on neural networks for decision support
Neural network models for time series forecasts
Management Science
A Single-Item Inventory Model for a Nonstationary Demand Process
Manufacturing & Service Operations Management
How a Base Stock Policy Using "Stale" Forecasts Provides Supply Chain Benefits
Manufacturing & Service Operations Management
Demand forecast in a supermarket using a hybrid intelligent system
Design and application of hybrid intelligent systems
Agent-based demand forecast in multi-echelon supply chain
Decision Support Systems
Improved supply chain management based on hybrid demand forecasts
Applied Soft Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Information Sharing as a Coordination Mechanism for Reducing the Bullwhip Effect in a Supply Chain
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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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.