The two ingredients above are critical to have before you can make systems create plans, learn and become more intelligent. Imagine a very skillful driver with blurry vision and foggy mirrors, i.e. not having a mental model of the physical world trying to operate the vehicle. Intelligence combined with accurate model and relevant data allows finding patterns of behavior that leads to causes of events. This means gaining the ability to predict the likelihood of events in the future. Examples are: predicting which materials or suppliers may cause lateness in winter, what equipment are likely to become bottlenecks when the temperature goes above 85 degrees or how the yield is going to change over the next few months. Once we know the causes then prescriptive algorithms are deployed to build resiliency into the plan.
With above three ingredients, companies can get very close to autonomous supply chain planning. Just like an autonomous vehicle, the system offers a roadmap to get to its destination, it senses the environment and reacts accordingly. And by maintaining relevant data it can improve to avoid potential road blocks and delays in future. For more information on digitalization click Here.