Title: Predictably unpredictable: How judgmental and machine learning forecasts complement each other
Authors: Ms. Devadrita Nair and Prof. Dr. Arnd Huchzermeier
Abstract: We propose a three-step demand forecasting framework that combines the expert's knowledge of the market with the machine learning algorithm's ability to leverage historical information to forecast seasonal demand for rapid innovation products. Using data from Canyon Bicycles, we find a 29% reduction in forecast error (measured by WMAPE) over a purely judgmental forecast.