The project’s point of departure is generally low forecast/delivery schedule accuracies, with negative impact on tied-up capital, transport costs, volume flexibility and environment in automotive industry supply chains. The aim is to generate a best practice description of how planning information is shared and used in the supply chains, and develop and field test new methods and models for measuring, visualizing and predicting delivery schedule variations in supply chains.
The new methods and models will generate improved demand visibility and allow for new ways of planning (e.g. conducting proactive scenario-based planning). As such the project intends to contribute to companies’ production and supply chain planning systems' abilities to compensate for and manage uncertainties, variations, and disturbances in supply chains.
The project is organized in six work packages. The first is a survey study of information usage in automotive supply chains. The second conducts data analytics of a large amount of delivery schedule data in order to identify common variations and patterns. The third conducts case studies to explain causes and consequences. The fourth develops new machine learning-based solutions/models for visualization and prediction. The fifth studies implementation and the sixth conducts dissemination.
Professor vid Chalmers, Technology Management and Economics, Supply and Operations Management
Forskare vid Chalmers, Technology Management and Economics, Supply and Operations Management
Doktorand vid Chalmers, Technology Management and Economics, Supply and Operations Management
Funding Chalmers participation during 2018–2021
Areas of Advance
Areas of Advance