Project Overview
Hybrid AI for supply chain optimization in Industry 4.0
Supply chains (SC) are increasingly challenged by uncertainty and risks in this high-tech era. In this context, to effectively identify and mitigate SC-related risks, data, information, and knowledge from different sources must be smartly exploited. This research project focuses on optimizing supply chains through the integration of symbolic and statistical AI. Knowledge models, particularly ontologies and knowledge graphs, will be used to conceptualize the problem area, providing rich domain knowledge and data semantics. Furthermore, statistical AI techniques, such as machine learning and mathematical optimization, help uncover the meaning behind specific supply chain data sources.
Hybrd AI for SCRM
Potential Use Cases
- Supply chain risk management (at the supply chain network level): The recent Covid 19 pandemic has highlighted the presence of vulnerabilities in industrial supply chains. In this context, predictive analytics can be used to mitigate risks.
- Production planning under uncertainties (at the node level in a supply chain): In this case, the uncertainties present on a single node of the supply chain will be analyzed using predictive analytics.
Project Aims
- To develop relevant knowledge representation models and reasoning mechanisms, utilizing ontologies and knowledge graphs to represent the knowledge in the domain of supply chain risk management.
- To explore and develop appropriate machine learning algorithms that support the effective mining of various forms of data.
- To identify and develop relevant strategies to combine methods in statistical and symbolic AI for risk identification and risk mitigation in supply chains.
Period: January 2023- December 2025
Impact: This project is part of a strategic partnership between Swansea University and Université of Grenoble Alpes, aligned with their 5-year strategic plan (2021-2026). The collaboration focuses on promoting cooperation in areas such as artificial intelligence and resilience. This project contributes significantly to the field of artificial intelligence and has potential contributions to the resilience of supply chain. Furthermore, this project benefits from local developments, including the technological platforms being developed for Industry 4.0, such as the Platform OM at UGA/Grenoble INP and Festo Industry 4.0 CP Lab at Swansea University. The project also leverages the opportunities and environment provided by Swansea University. Notably, Swansea University is home to the EPSRC Centre For Doctoral Training in Enhancing Human Interactions and Collaborations with Data and Intelligence Driven Systems, which further supports and enhances the project’s goals.
Project Team
Mrs Zuha Shahid, PhD Student
Prof. Arnold Beckmann, Department of Computer Science
Prof. Cinzia Giannetti, Department of Mechanical Engineering
Prof. Gülgün Alpan, G-SCOP laboratory
Dr Abdourahim Sylla, G-SCOP laboratory
Collaborators
Swansea University
Université Grenoble Alpes
