Advanced machine learning methods such as reinforcement learning are able to take challenging problems just like this and learn from real-world data. They can help decision-makers find optimal policies that improve supply chain competitiveness and adapt to changing circumstances in ways that static, deterministic mathematical models simply can't. They can be "model-free," focused on continually improving supply chain decision-making statistically without a big up-front modelling exercise, or they can refine and adapt an existing model based on real data. But even the best such algorithm is only as good as the data it is given.
Today, modern sensor systems can provide a flood of valuable data providing visibility of the supply chain at incredible granularity. When every shipment is a source of previously unimaginable quantities of data, it becomes vital for competitiveness to be able to take that data and turn it into actionable information quickly and accurately: fortunately, modern Bayesian sensor fusion methods can go hand-in-hand with machine learning algorithms to take all sorts of information, from weather reports and currency fluctuations to demand forecasts and historical data and fuse those with the live sensor data into an optimal policy that delivers real business benefits.
Advanced machine learning algorithms are poised to help make sense of the information and the uncertainties that face decision makers in a constantly changing world, reduce cost and waste in the supply chain, and be a source of major competitive advantage.