Depending on the industry, a supply chain could be as straightforward as a few retail shops, warehouses, and trucks, or as complex as a sprawling network of mine sites and processing plants connected by rail and sea transport. From a higher perspective, however, all these supply chains are linked in one way or another through an intricate web of interactions. Each of these interactions has its own challenges and complexities. Let's take the mining supply chain as an example to illustrate these challenges and complexities. Planning a mine site requires consideration of what grade of ore is required at what point in time, coupled with truck and digger availability, workforce rosters, maintenance schedules, and more—which is just the first step in the process—followed by the scheduling of trains that will transport the ore from various mine sites to the port, where the coordination of stackers and reclaimers happens to ensure that each ship is loaded on time. After that, there is more, as the ore arrives in another country and is heated by coal (which arrived at the furnace through a similarly complex supply chain) to become steel, which in turn is molded into the car’s frame and doors and hood—components that represent just a handful of the 30,000 parts that make up the average car, each of which has their own supply chain from raw materials to finished part. On top of this, the automaker needs to predict consumer demand for its cars across different countries—a difficult problem in itself—all while the cars are being assembled and placed on ships for transport to those markets. At every step, there is complexity, and the more steps we consider together, the more complex the problem becomes. This inherent complexity makes supply chain problems particularly well suited for the application of Artificial Intelligence. At the highest level, these are the core components of a supply chain operation: predicting demand, planning and scheduling production (whether it be the production of iron ore from a mine or the assembly of cars in a factory), and then organising logistics and distribution. Each component represents a complex business problem in itself, and together, an almost impossible challenge for humans to solve. This is where leveraging Artificial Intelligence can optimise decision-making across a variety of supply business functions, be it improving the accuracy of demand forecasts, or optimising inventory levels, or creating schedules that are optimised for various constraints and objectives. Contact us to request a soft copy of Chapter 10 of the book The Rise of Artificial Intelligence discussing supply chain optimisation.