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Picture this scenario:
A building materials company has just produced a fresh batch of cement. To test the quality of the cement, the company then produces a concrete element. Weeks later, once the concrete has hardened, the quality control turns up an issue. The cement wasn’t up to standard.
Why wasn’t the issue detected earlier? In this scenario, there wasn’t a live sensor that could predict the quality of the finished product early on in the production process. But even in the cases where sensors do exist, they sometimes stop working — or are never installed in the first place.
The manufacturing industry has a sensor problem. To fix it, we need to look at how other heavy-asset industries use machine learning and root cause analysis to produce data-driven predictions that manufacturers can act on with confidence to optimize production and reduce waste.
