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- Judge an autonomous maintenance program on results, not on checklists completed.<\/li>\n
- Five weekly indicators are enough: route completion, abnormalities found, short stops per shift, unplanned downtime, availability.<\/li>\n
- Short stops are the earliest result signal, but they rarely appear on paper logs.<\/li>\n
- Expect findings and backlog to rise first: that is a sign the inspections work.<\/li>\n<\/ul>\n<\/div>\n
Autonomous maintenance looks simple on paper: operators take on cleaning, inspection, lubrication and small adjustments, and maintenance technicians get time back for planned work. In practice, most programs are judged on feelings. The routes are walked, the checklists are signed, and six months later nobody can say whether the line runs better than before. This is where measurement earns its place.<\/p>\n
What autonomous maintenance is supposed to change<\/h2>\n
Autonomous maintenance (AM), sometimes called operator-based reliability, is one of the pillars of Total Productive Maintenance. Its promise is not “operators do maintenance”. Its promise is earlier detection: a loose guard, a leaking fitting or a worn belt gets noticed by the person who sees the machine every hour, before it turns into a breakdown or a string of small stops.<\/p>\n
So the outcome to measure is not the number of checklists completed. It is whether abnormal conditions are caught earlier, and whether the machine loses less time as a result. Those are two different signals, and they do not move at the same time.<\/p>\n
Lessons from IDCON on running an AM program<\/h2>\n
IDCON, the reliability and maintenance consultancy based in Raleigh, North Carolina, has published one of the most practical guides on the subject: How to Implement Autonomous Maintenance<\/a>, by Torbj\u00f6rn Idhammar. It walks through the whole work system: defining operator tasks, documenting inspection routes in walking order, keyword checklists, measuring points, the backlog that grows when inspections start finding problems, and the role of leadership.<\/p>\n
Two pieces of advice from that guide matter directly for measurement. The first is about restraint: “Do not collect more measuring points than the plant can handle and have time to analyze.” The second is a warning: “A good way to kill an AM\/OBR process is to ignore failure reports and collected data.”<\/p><\/blockquote>\n
Both point to the same thing. Data only helps an AM program if it is small enough to be read and if someone visibly acts on it. Operators who report a problem and never see a response stop reporting. The guide also explains that backlog and maintenance costs tend to rise before reliability improves, which is exactly why leadership needs a way to see early progress.<\/p>\n
Five indicators that show whether AM is working<\/h2>\n
A practical AM scorecard does not need dozens of KPIs. Five indicators, read every week, are enough to tell the difference between a program that is taking root and one that is drifting.<\/p>\n
![\"Autonomous](\"https:\/\/teeptrak.com\/wp-content\/uploads\/2026\/10\/teeptrak-autonomous-maintenance-kpis.png\")
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