DISPO 4.0 - Simulationsgestützte Absatzprognoseoptimierung in der Investitionsgüterindustrie

Abstract

This paper presents a demand forecasting ap-proach that automatically selects optimal article specific forecasting methods and optimizes the method parame-ters, using deterministic simulation and a Genetic Algo-rithm (GA). For an efficient demand forecast, choosing the best forecasting method based on the item-specific his-torical requirements time series is key. The optimization of the forecast parameters is also crucial for efficient de-mand planning. Both decisions lack digital method sup-port, leading to suboptimal forecasts in practice and thus inefficient material requirements planning. This paper in-vestigates the optimization potential of an automatically optimizing forecasting approach, featuring a simulation-based comparison of six standard forecasting methods, evaluated using a case-study from the capital goods in-dustry. The methodological core of the optimization is a GA, which improves the underlying, method-specific fore-cast parameters. The simulation-based optimization pro-vides a rolling-horizon demand forecast for each item, and is determined through the application of a rule-based heuristic. The results show a significantly improvement potential through this form of efficient item-specific de-mand planning.