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Learning factory machining3/9/2023 ![]() ![]() Machine learning, at first glance, doesn’t appear to solve the problem. This very simple example already shows that it’s very hard to predict what data needs to be collected and combined to analyze what needs to be done. It could also be that too many operators take time off at the same time in the last week of the month or that the ordering process is such that in that fourth week certain parts or materials are no longer in stock. It could be that maintenance is scheduled for that fourth week in such a way that it affects production processes. If our KPI is directly based on the number of finished products leaving the factory and the downtime of the final packaging department, for example, that gives us a clue where to look but not what to look for. To find out the cause, we have to look at the underlying data for a trigger. Say we have a KPI dashboard that tells us that production rates go down every fourth week of the month. ![]() Let’s look at that from the human perspective. The challenge is allowing the system itself to collect as much data as possible. The challenge here is collecting the right data. The scope of such algorithms may vary based on the plant’s needs: to improve production rates, reduce material losses or make the processes more sustainable. It’s my vision to be able to feed the data we collect on different levels (machines, processes, production numbers, material flow, and so on) to a machine learning algorithm, or set of algorithms, focused on optimizing the factory. ![]()
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