Explainable AI for resilient supply chains: Choosing backup suppliers only when needed
Supply chain disruptions can have swift and compounding consequences for manufacturers, leading to production delays, rising costs and a scramble to secure alternative suppliers.
A framework developed by researchers at Constructor University uses logic-driven AI to enhance supply chain resilience by making supplier selection decisions more transparent and responsive to disruptions.
What happened
Two researchers from Constructor University created an AI framework that uses logic-driven artificial intelligence to support more resilient and responsive supplier selection decisions, published in Engineering Applications of Artificial Intelligence.
Why it matters
This framework addresses the critical need for transparent and adaptable supplier selection in supply chains, which is essential for maintaining operational continuity, managing costs, and adhering to sustainability standards in the face of disruptions.
- AI can enhance supply chain resilience through transparent decision-making.
- Logic-driven AI frameworks like ASP can support complex supplier selection processes.
- Integrating AI with existing systems like RPA and ERP improves operational efficiency.
- Backup suppliers are activated only when necessary, balancing cost and sustainability.
- Transparency in AI decisions allows for human oversight and adjustment.
The bigger picture
The framework has the potential to improve supply chain resilience by enabling more informed, transparent, and sustainable supplier selection decisions, reducing the risk of disruptions and ensuring compliance with environmental and social standards.
Researchers at Constructor University have developed an AI framework that uses logic-driven technology to improve supply chain resilience, ensuring transparent and sustainable supplier selection decisions.
Technical details
- Answer set programming (ASP) as a decision-making layer
- Integration with robotic process automation (RPA) and enterprise resource planning (ERP) systems
- Closed-loop system for decision-to-execution
- Audit trail for transparency and human review
- Simulation of supplier network scenarios with synthetic data
FAQ
What is the main goal of the AI framework developed by the researchers?
The main goal is to create a transparent and explainable system for supplier selection that enhances supply chain resilience by making decisions responsive to disruptions while maintaining sustainability and cost efficiency.
How does the framework use answer set programming (ASP)?
ASP serves as a decision-making layer that translates procurement rules, supplier capacities, disruption conditions, and sustainability requirements into clear instructions for the system to evaluate transparently.
What are the key benefits of the framework's approach to backup suppliers?
The framework activates backup suppliers only when necessary, ensuring that decisions are not made automatically and that cost, sustainability, and operational needs are balanced.
This summary and analysis were generated with AI from the original article and may contain errors. Read the original for the full story.
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Originally published by TechXplore AI on Oct 5, 2026.
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