General Eisenhower has been quoted as saying: “what is more important than planning is re-planning!” I am sure we all relate to this especially when it comes to supply chain planning. Things go wrong all the time and we have to make adjustments to the plan, change the plan or simply re-plan. We should consider this normal planning process not exceptional planning process. This is what makes continuous planning important. Continuous planning is not planning all the time. It is planning as needed all the time. What changes the plan are events that are potentially predictable but we don’t know where and when and the level of impact they can have on the objectives of the plan, e.g. lateness or cost.
Given the size of supply chain operations, it is absurd to assume that we have one big engine that keeps running and updating the plan. We, as human beings, have a well developed mechanism using different levels of intelligence to execute our plans. Take a simple example of going out to eat! We plan which restaurant, how to get there, get off the car, walk to the front entrance, sit at a table, order food and start eating making sure that we have enough money to pay for it etc. Each one of these tasks requires a different level of intelligence to plan and execute. Walking does not require much intelligence although you may want to avoid carelessly crossing the street. What to eat needs more intelligence than how to eat. Thus, while I walk and there is a worker’s zone sign in front of me, there is no need to re-plan the entire trip and choice of restaurant once again, you simply walk around the construction area. Similarly, in supply chain every equipment breakdown, late arrival of parts, new orders etc does not necessarily require a re-plan! “shock absorbers” with less intelligence can handle the problem until the bumps are too much and we need to change course. At this point a higher intelligence can take over.
Thus, continuous planning becomes more of an event-driven planning with a sensory mechanism to determine what level of “intelligence” needed and what amount of processing power is required to respond to the changes. To this end, we at Adexa have designed a unified data model with intelligent agents, we refer to as Digital Experts, that can perform such functions and more importantly get better at their job by use of machine learning algorithms. Typical examples are disruption on the shop floor or late arrival of parts can be sensed and picked up by appropriate Digital Expert and examined to see what the impact might be and based on that trigger appropriate action to keep the operation and its highest level of intended objectives.