Machine Learning for Master Production Scheduling: Combining probabilistic forecasting with stochastic optimisation

This research paper delves into the challenges of Supply Chain (SC) planning under demand uncertainty, focussing on Master Production Scheduling (MPS) with capacity constraints. Traditional SC planning methods, often based on point forecasts and basic safety stock calculations, overlook the distinct demand distribution of each product in each planning period. Considering the demand uncertainty is crucial for robust decision making, especially for time series with seasonal and non-stationary demand. To address this gap, the paper introduces a pioneering Separated Estimation and Optimisation (SEO) approach that estimates demand uncertainty using Machine Learning (ML)-based probabilistic forecasting, subsequently solved by stochastic optimisation. Through a comprehensive analysis involving 17 datasets with a total of 303 products, the study confirms the robustness of stochastic optimisation approaches. It also demonstrates the superiority of ML-based forecasting, which is particularly adept at capturing the intricacies of complex demand patterns. The research challenges the conventional reliance on the Gaussian distribution, instead advocating for the adoption of more flexible parametric distributions such as the Negative Binomial (Neg.-Bin.) distribution. Furthermore, it illustrates how to leverage advances in the research areas of ML-based probabilistic demand forecasting and stochastic MPS, as well as providing a basis for future research. Such avenues include the exploration of testing these approaches with a rolling planning horizon and incorporating both demand and supply uncertainties. • Probabilistic demand forecasts for decision-making in Master Production Scheduling. • Machine Learning based Probabilistic forecasting using Deep Neural Networks. • Using real-world data, extending findings previously based on synthetic data only. • Valuable insights for supply chain practitioners on incorporating demand uncertainty.