Manufacturing businesses do not usually have a data problem. They have a data access problem. The numbers exist somewhere, in the MES, in a quality system, in someone’s spreadsheet, and getting a straight answer out of them takes a request, a queue, and three days. A UK-based industrial machinery manufacturer got tired of that cycle and brought in Eerly’s AI-powered data insights to close the gap between question asked and answer available.
The trigger was a fairly ordinary frustration. Production leadership wanted to understand why throughput on one line had drifted down over a quarter. The data to answer that sat across four systems. By the time the analysis came back, the line had drifted further. Eerly Insights connects those sources and runs AI-driven analysis on top of them, which means the same question now gets answered in the meeting rather than after it.
Accely’s deployment work started with source mapping. Which systems hold what, where the definitions conflict, and which numbers people actually trust. That last part matters more than it sounds. Plenty of manufacturers have two versions of the same metric and a quiet internal disagreement about which one is right.
The first workstream covers production performance, scrap and rework analysis, and machine availability. Second wave picks up supply chain and order fulfilment.
One early finding was unexpected. Scrap rates that had been attributed to material quality turned out to correlate more strongly with a particular shift pattern. That is not a conclusion the reporting was ever going to surface, because nobody was looking at those two variables together.
The platform is live with the production and operations teams. Adoption has been faster than the project plan assumed, mostly because the interface does not require anyone to learn a query language.
Accely’s role as an AI strategy consulting partner on this engagement runs alongside the build. Deciding what the business should measure, and what it should stop measuring, is turning out to be the harder half.
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