Using Machine Learning Methods to Improve Forecasting Support Systems
Kussmann Simon-Ulrich, 2019
Betreuende Dozierende: Thomas Hanne, Joachim Ehrenthal
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Forecasting remains one of the key drivers for successful implementations of Sales and Operations Planning. Companies pursue different strategies to create forecasts with the highest possible accuracy. Often the combination of statistical and judgemental forecasting methods is implemented that can be prone to problems and barriers like different incentives, systematic bias and human errors which lead to uncertainties and trust issues. These problems are the reason for the existence of forecasting support systems that provide meaningful support to the forecasting process or function. But existing knowledge and literature highlight that the maturity of FSS implementations is low and that improved FSS need to be developed that further support and guide forecasters by taking the advantages of the application of machine learning methods....
Studiengang: Business Information Systems (Master)
Fachbereich der Arbeit: Wirtschaftsinformatik & IT-Management