This innovative AI is moving the dial in the way we run supply chains and find solutions to important global challenges such as supply chain resilience and sustainability goals. Such AI models are generally bespoke compilations of elements that are themselves state-of-the-art. Much of the active innovation being worked on here is still in development, often featuring collaboration between academia and industry. This is due to the scale of the problem, the uniqueness of supply chains, and the required access to data and computing resources.

An example of a component where advancements are being made is process mining. Here we may use commercial tools to understand how the system is configured as a starting point for our model. We may then pull on another active area of AI, time-series forecasting. ML models are starting to outperform statistical methods when it comes to accuracy and performance in real-world applications. In the M5 fore-casting competition, there was compelling evidence of this when LightGBM (a decision tree-based machine-learning model) had wide-scale adoption by most leading entries. Additionally, in complex sys-tems we are often considering multiple forecasts, solutions such as LightGBM that support hierarchical forecasting are therefore beneficial.

Other AI technologies whose outputs may be included in complex systems are Deep Learning applications such as Computer Vision or NLP. Here we see rapid innovation on a different trajectory. The scaling of models and available training sets that have been made widely available by technology companies such as Google and non-profits such as ImageNet, has led to commoditisation of Computer Vision ap-plications. There are now numerous off-the-shelf solutions that can be applied to real-world problems with relatively small amounts of customisation. This is feasible via Transfer Learning, that is, the process of using a pre-trained model as a starting point for a model for a new task. For example, your starting model may be able to identify dogs in a pho-to, you can then additionally train it to identify your pet among others, similarly to how Apple and Meta can start to quickly recognise your friends in photos. For example, say we want to use Computer Vision to measure quality on the manufacturing line, we can very quickly build a good proof of concept using offerings such as Microsoft’s Cognitive Services or Google’s AutoML.

We then need to use optimisers to build our scenarios and solve the problem. Here again, there are many different solutions available. Searching the problem space and optimising for solutions in complex systems is a big problem and can quickly become computationally ex-pensive. It’s an area where Quantum computing holds great potential and could cause a real step-change, in particular Quantum Annealing. This refers to an optimisation process for finding the global minimum of an objective function, particularly effective when there are many local minima, such as across our complex supply chain.

Finally, supply chains are also areas where developments in the governance of AI, accountability of model recommendations, and model transparency and explainability are very important. Unlike many digital/ web applications of AI, we are faced with the challenge of how to ‘start small’ when innovating & testing a model that may e.g. deploy a shipping container full of products. Supply chain scenarios require high confidence in models when applying recommendations to a real-world system for the first time. The implications of errors can be significant, particularly in highly regulated supply chains where there are safety requirements on the quality of the output. It is therefore critical that academia & industry work closely to develop frameworks so that business and regulators are in-step with the cutting-edge algorithms. Collaboration is a massively important aspect of state-of-the-art AI in-novation. Only by working with regulators we can deploy state-of-the-art AI and realise the potential of this incredible technology.