Liebe CogSys-ler,
Kolloquium, 17. Juni 2018, 11:00 Uhr, WE5/05.013
Matthias Delfs (MA CitH): Forecasting in the Supply Chain with Machine Learning Techniques (joint with Siemens Healtheneers)
This thesis was written in cooperation with Siemens Healthineers. The goal was to predict future sales figures of X-ray systems for the supply chain management. The product line consists of five different systems. Sales figures are predicted for all systems as a single quantity and also individually. The historic data supplied covered 15 years and consisted of 180 data points. These data points are a monthly census of sales related figures. The research question was to compare the performance of traditional time series modeling techniques from statistics with newly emerging machine learning approaches. The established time series modeling techniques were exponential smoothing and ARIMA. The machine learning techniques consisted of modeling feedforward neural networks and random forests. The performance of all methods was measured with the mean absolute percentage error. The best performance of all implemented methods resulted from an extended ARIMA model (ARIMAX). Moreover, the thesis included implementing a software tool for the supply chain management to make forecasts in practice.