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Production downtime tracking<\/strong> is the discipline of systematically measuring every period during which production equipment is not running at planned capacity \u2014 capturing stoppages at the machine level, aggregating them at the line and facility level, and using the resulting data to drive measurable improvement in production availability and OEE. According to Industry Week<\/a>, unplanned downtime costs industrial manufacturers an estimated $50 billion annually in North America alone \u2014 the majority of which is recoverable through systematic tracking and targeted corrective action.<\/p>\n This guide provides the complete framework for production downtime tracking: what to measure, how to measure it accurately, how to structure the data for maximum improvement impact, and how manufacturers consistently recover 15 to 25 percentage points of production capacity once they move from estimated to measured downtime.<\/p>\n Effective production downtime tracking captures four dimensions of every stoppage event:<\/p>\n Together, these four dimensions produce the complete production downtime dataset needed to calculate OEE availability rate, identify improvement priorities through Pareto analysis, track the impact of corrective actions, and feed JEMBA AI for predictive maintenance signal detection.<\/p>\n Machine downtime tracking<\/strong> focuses on individual equipment \u2014 each machine’s availability, performance and downtime patterns in isolation. This is the granular level required for root cause analysis and targeted maintenance decisions.<\/p>\n Production downtime tracking<\/strong> operates at the line and facility level \u2014 aggregating machine-level downtime data into production line OEE, shift-level downtime summaries, and facility-wide availability trends. This is the level required for production planning, shift management and multi-line improvement prioritisation.<\/p>\n The distinction matters operationally: a supervisor managing 12 production lines needs production-level downtime visibility \u2014 which lines are down right now, for how long, and what the impact on today’s production plan is. They do not need to drill into machine-level data in real time. TeepTrak provides both levels simultaneously, with role-appropriate dashboards for operators, supervisors, production managers and industrial directors.<\/p>\n For a deeper dive into machine-level measurement, see our complete machine downtime tracking guide<\/a>.<\/p>\n Most manufacturing facilities progress through four maturity levels in their production downtime tracking capability:<\/p>\nWhat Production Downtime Tracking Measures<\/h2>\n
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Production Downtime Tracking vs Machine Downtime Tracking: The Scope Hierarchy<\/h2>\n
The Production Downtime Tracking Maturity Model<\/h2>\n