Supply chain leaders deploy prediction strategies to respond better and faster. For example, extra inventory is kept in case an unexpected order from a high priority customer arrives. Without that inventory, your response would be very limited or perhaps none at all. The reality is that the better you predict the less you need to “respond.” In other words, when something unexpected happens, you have already accounted for it. You may also call this redundancy to mitigate risk. We do this in all aspects of our lives, airplanes have cold redundancies, parachutes have a spare one, grocery stores are hoarded when there is scarcity. These are all responses to potential risks. The latter one is one without prediction element. The key to responsiveness is being able to predict events. To know the latter, one needs to identify the cause(s). For example, as temperature rises beer consumption goes up and perhaps workers show up late to work the next day. How can we do this? AI and ML to the rescue.
There are several areas where predictions can be deployed. One is the behavior of suppliers and customers as well other resources such as equipment or even trucks and delivery services. By learning their actions over time, we can project their patterns of behavior depending on the season or other causes such as tariffs, price variations, and government regulations. Moreover, predictions can be used not just to understand the structure of the supply chain, but also the behavior of the supply chain. That is, the right policies to be deployed. An example of the latter would be what inventory safety levels should be deployed. Other examples are the extent to which more expensive substitute parts should be used, or how the cost is increasing because of lack of proper maintenance of key equipment or turnover of labor.
Not every event is predictable as we have seen with the pandemics. However, having the agility to re-structure the supply chain and knowing what works best can be deployed to better respond to such unusual and impactful events. This capability comes from having a true digital twin of the supply chain. A great example of this is, one of our clients, a global electronic manufacturer, re-shaped their supply chain, virtually overnight, when tariffs and geopolitical factors made it too expensive to continue with the status quo. The digital twin is also the most important ingredient of intelligence and ability to predict. How else can you do it unless you have a complete view of the world and what the possibilities are. S&OP fails in this respect since it has a very high-level view of the supply chain and the visibility that it provides is rather myopic.
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