We look towards the future of digital disruption by considering the past of disruption, with a particular focus on the production and movement of goods. We introduce the notion of the 'smith', and consider how, by localizing the provision, or supply, a 'smith' can ensure high added value for their skills. Using analogies from pull and push supply chains, We argue that our future economy needs to include an environment where smiths prosper. From craft coffee to craft software, to add value in a global marketplace we argue that we need to exploit localization.
Machine Learning in Supply Chain 
Containerization has had a dramatic effect on global economics, placing many people in the developing world at the end of the supply chain.
For example, you can buy Wild Alaskan Cod fished from Alaska, processed in China, sold in North America. This is driven by the low cost of transport for frozen cod vs the higher relative cost of cod processing in the US versus China. Similarly, Scottish prawns are also processed in China for sale in the UK.
Supply chain is a large scale automated decision making network. Our aim is to make decisions not only based on our models of customer behavior (as observed through data), but also by accounting for the structure of our fulfilment center, and delivery network.
Many of the most important questions in supply chain take the form of counterfactuals. E.g. “What would happen if we opened a manufacturing facility in Cambridge?” A counter factual is a question that implies a mechanistic understanding of a system. It goes beyond simple smoothness assumptions or translation invariants. It requires a physical, or mechanistic understanding of the supply chain network. For this reason the type of models we deploy in supply chain often involve simulations or more mechanistic understanding of the network.
In supply chain Machine Learning alone is not enough, we need to bridge between models that contain real mechanisms and models that are entirely data driven.
This is challenging, because as we introduce more mechanism to the models we use, it becomes harder to develop efficient algorithms to match those models to data.