Phase-Out Demand Forecasting : Predictive modeling on forecasting product life cycle

Sammanfattning: The phase-out stage in a product life cycle can face unpredictable demand. Accurate forecast of the phase-out demand can help supply chain managers to control the number of obsolete inventories. Consequently, having a positive effect in terms of resources and lower scrap costs. In this thesis, we investigated if data-driven forecasting models could improve the accuracy of forecasting the phase-out stage when compared with domain experts. Since the space of available models is vast, a set of 11 best performing models according to literature were investigated. Furthermore, a thorough model selection based on performance suggested that the following three models were best suited to our dataset: Autoregressive Integrated Moving Average (ARIMA), Support Vector Regression (SVR), and Gaussian Process Regression (GPR). The final results showed that none of the models were able to improve the forecast accuracy overall. However, SVR displayed good performance close to the domain experts’ estimates across 14 unique products through variation of analysis. In addition to the comparative study, this study showed that using less data improved the models’ performances. Only 60% of the training data seemed optimal for ARIMA and GPR, while SVR had a good performance with only 80% of data. We present the results along with further research questions to be explored in this domain.