Project Supply chain planning is complex because of complex dependencies of the delivery of the final product on the timely shipping, assembly, production, and procurement of its parts. Periodic supply chain plans are made. However, during the execution of the plan, it must often be adapted, for example, because parts are delivered late or because production is delayed. This leads to changes to the plan that are often ad-hoc and suboptimal, and cause planning ‘nervousness’, i.e. frequent planning changes. Consequently, in addition to periodic planning, supply chain planning can benefit from planning techniques that assist with the day-to-day adaptations of the supply chain plan, due to the unexpected situations that arise. These ‘online’ planning techniques must take the current periodic plan into account, as well as the current status of the procurement, production, and assembly of the parts. It should then advise on changes to the plan, while minimizing planning nervousness and costs. In this project we aim to develop such a technique for online supply chain planning, using novel techniques from the area of artificial intelligence that can learn to predict – based on the current situation and unexpected events that must be handled – what the best solution is to plan for the unexpected event. A general framework for these techniques is being developed in a related project, where applications in production and transportation planning are studied. The aim of this project is to make this general framework suitable for supply chain planning. Against this background, the project has a specific focus on encoding and learning the complex relations and patterns of dependency between different activities in supply chain planning, which to the best of our knowledge has not been studied before.

June 2023 – Annual AI Planner of the Future Event. Poster. 

 August 2024 – Finalized the second publication. Collaboration with master students and ESCF companies. Identify potential impovements intention to put in scientific paper to benefit both interested companies and the scientific community. Third research work is being developed. Dealing with problem of data drift AI-based decision systems in industrial settings. 

Riccardo Lo Bianco – Remco Dijkman – Willem van Jaarsveld 

More info: escf@tue.nl