Production planning in manufacturing is a vital part of operations management, despite the fact that most production planning applications don't really plan anything. Traditional planning systems, including material requirements planning and its derivatives, do a great job of sorting out the varying requirements and dates. They then apply a type of fixed logic to that mass of data and lay out a plan or schedule that seems reasonable and may actually work. But any experienced planner will tell you that a typical computer-generated plan might contain elements that are impractical or downright impossible -- like launching a production order two weeks ago and finishing it today, or having a work center commit to three or four times as much production as it can complete in a day.
AI to improve production planning in manufacturing
The new generation of so-called advanced planning and scheduling (APS) overcomes many of those limitations by applying optimization algorithms instead of fixed logic that can trade off priorities and alternatives to come up with a more feasible plan that balances both resources and material date and quantities. But even APS, as "smart" as it is, still has its limitations. It takes a dedicated, knowledgeable human planner to interpret and apply the recommendations in the context of what's really happening out on the plant floor and in the customer's world by using intelligence that is not found in the data. Based on experience and knowing, or at least being able to anticipate, what is likely to happen under various scenarios moving forward?
The use of the word intelligence in the preceding paragraph is significant, for this is the human-like thinking that AI is designed to do. These production planning systems "learn" by collecting a massive amount of data and thoroughly analyzing it for cause and effect to build a model of the process. Then, as new information comes in, the system can perform thousands of what-if simulations to find the best path forward. The model is also refined based on additional data as these scenarios play out.
Production planning systems that incorporate AI capabilities will not replace human planners -- at least, not anytime soon. It will take some time to build up the necessary level of confidence needed to trust a computer to carry out this vital task. That being said, AI-developed plans and schedules will undoubtedly be "better" than any human could create. Plants will run more efficiently, product quality will improve and more work will be completed on time and at a lower cost.
So, what will the planner do after AI takes over the job of production planning in manufacturing? In addition to adding the "sanity check" on plans and planning system operations, human planners can focus on managing any special exceptions that the system might not be able to address. In addition, human insight and ingenuity can be directed toward process improvement.
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