Predictive (Risk-Resilient) Planning
How do you plan in the face of uncertainty? Produce more than expected? If so, by how much? Re-plan when an undesired event occurs? It might take too long and it may be too late! How do you even know if a Re-plan is needed? How do you assess the impact of the event on your supply chain? On your profitability and market share?
Traditional planning systems rely on a deterministic model of the supply chain. The approach has been the ability to simulate events using what-if planning and decide what precautions to take to mitigate risk. In addition, so called response planning has been promoted as a way of dealing with unexpected events! Response planning, however, may be just too late. Your choices are limited as to what can be done. If a large order comes in and you do not have enough of the right material or capacity, how would you respond? Similarly, how do you respond if a major bottleneck resource goes down for extended period. Your response planning will not be that useful! As for what-if scenario planning, it might seem to be more proactive than response planning, however there are too many variables to consider. In reality, there are thousands of different scenarios that can be looked at but we are generally limited to a handful of them. But so many things can go wrong, even thousands of scenarios cannot help despite having the right computing power at your disposal and the time to examine each one of them!
A whole new paradigm is needed to address risk when it comes to supply chain planning. Let’s examine why we plan first. We plan for Responsiveness and we execute for reliability. The closer you are to execution of the plan the faster you can respond. Therefore, less planning is needed. Imagine the function of shock absorbers in a car. Having made a plan to drive on a particular road, shock absorbers execute the plan by absorbing all the bumps and potholes on the road in order to make the ride as smooth as possible. As long as you are not too uncomfortable, you stay with the plan. Once it gets too uncomfortable, meaning the shocks are incapable of handling the bumps, then you decide to take a different route. Note that Shock absorbers need very little planning and are reactive. The next level of execution is the driver, who uses gas pedal and brake as well as other controls in order to drive the car towards the destination. If a person or another car suddenly appears in front of the car, the driver slows down or stops. None of this was in the original plan. But it happens in execution; no planning is needed, and there is enough time for reaction. So, what happens if we cannot or fail to react, such as an accident or road block?
Above example illustrates how planning and execution are so tightly linked together and “sense” the time to kick-in and re-plan at every level. At Adexa, we have implemented a Unified Data Model that precisely mimics the same behavior merging planning and execution in one autonomous environment. Much like the above examples, we have created intelligent “sensors” that can detect the impact of events and trigger various levels of planning or adjust control variables. They are called Adexa Intelligent and Distributed Agents (AIDA©) intended to measure the impact of events and learn from their past experiences. For example, Supplier AIDA decides on potential impact of suppliers on their deliveries, Planning AIDA performs analysis of events, both external and internal, in order to decide what level of re-planning, if any, is needed, as described above. Asset Management AIDA examines the impact of usage of equipment and frequency of breakdowns and their impact on service level.