Autonomous vehicles (AV) are designed to get your destination from you, plan how to get there, based on fastest and least expensive route, and deal with road delays and blockages, changing course as needed and even take care of every bump on the road using real-time signals and sensors. As you can see there are different levels of intelligence needed to plan and change course when needed compared to shock absorbers simply absorbing bumps on the road requiring almost no intelligence. At the same time, what is not as apparent to a casual observer is that they are also constantly monitoring their own performance and making sure that enough diagnostic data is used to ensure every part is working as well as it should be.
Wouldn’t it be nice to have a supply chain planning operation that acts the same way? Yes, and we are almost there! We now have the capability to plan the entire supply chain of major organizations from suppliers all the way to logistics and distribution to customers and optimize it based on corporate objectives (destination) of cost and on-time delivery amongst others. Then be able to execute plans based on real-time information (received from IoT or Transaction systems) of possible delays in materials, changing orders, weather conditions and so on. Reacting to these changes by re-planning and taking care of every little bump such as machine breakdowns on the shop floor or missing tools or even delayed truck arrivals on the day of production until the order is built and delivered to the end customer. In the meantime, the system is capable of monitoring trends that can impact the operation of the supply chain. Some examples are impact of weather and region in deliveries, supplier lead-time variations, or equipment availability and how they impact the overall cost and delivery performance. Such trends are used to constantly correct the model of the supply chain, a phenomenon which we refer to as self improving supply chains. But they can do even more. They can even enhance their own performance, something AVs cannot do on their own. By learning about future potential risks of how materials and resources are used, self-improving supply chains can provide for better material and resource availability depending on the customers, regions or (say) weather conditions. Such trends are picked up during the planning process and potential causes of delays are detected and easily avoided using prescriptive techniques such as MEIO and or simply addition of resources as needed.