Adaptive AI Decision Agents for Holistic Supply Chain Optimisation: Merging Societal and Business Objectives

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Supply chain efficiency is a global problem that affects almost every person on earth. Having new and improved methods to optimise supply chain efficiency at a global level will be hugely beneficial to humanity, reducing the cost of goods, wastage, and carbon emissions, and increasing wealth generation. The problem is often too complex to optimise at a global level, and instead the parts of the system are decoupled, modelled separately and optimised separately. The project aims to investigate whether adaptive AI decision agents could be used to make competent decisions about which actions to take in a supply chain, to get closer to a global optimum. To conduct this research, our methodology will draw on concepts from reinforcement learning, multi-objective optimization, and multi-agent systems, and then be validated using several open-source supply chain datasets (e.g. as available on OpenML) and customer supply chain activity data provided by Peak AI.