Downtime Tracking

Production downtime is typically categorized to enable meaningful analysis. Planned downtime includes scheduled maintenance, changeovers, breaks, training, and planned shutdowns — these are expected and accounted for in the schedule. Unplanned downtime includes equipment breakdowns, material shortages, quality holds, operator absence, and other unexpected stoppages. Within unplanned downtime, further categorization is essential: mechanical failure, electrical failure, tooling failure, material jam, material shortage, quality issue, operator error, upstream starvation, and downstream blockage are common categories. Each category points to a different root cause and solution path. Equipment-related downtime drives TPM and maintenance improvement. Material-shortage downtime points to procurement or scheduling issues. Quality-hold downtime indicates process capability problems. Effective categorization requires a standardized reason code system that operators can apply quickly and consistently — typically 15–25 codes organized into 4–6 groups.

Start with a clear definition of downtime: when does the clock start (machine stops, or operator reports?), and when does it stop (machine restarts, or first good part?). Choose a tracking method appropriate for your environment: Manual logs work for small operations — operators record stoppages on paper or a whiteboard, which a supervisor enters into a spreadsheet or database at end of shift. Digital entry via tablets at each workstation speeds data collection and reduces end-of-shift memory errors. Automatic detection via machine sensors (PLC signals, power monitoring, vibration sensors) captures the exact start and end of every stoppage without relying on operator memory. The most effective approach combines automatic duration detection with operator-entered reason codes — the machine knows when it stopped, and the operator knows why. Regardless of method, the key success factor is making data entry fast and easy: if it takes an operator more than 15 seconds to log a downtime event, compliance will be poor.

Downtime data directly improves production scheduling in several ways. Realistic capacity calculation: instead of scheduling against theoretical capacity, use actual uptime data (from downtime tracking) to calculate demonstrated capacity — what the resource actually produces, including all downtime. This prevents over-scheduling and the cascade of missed commitments that results. Predictive scheduling: analyze downtime patterns to anticipate problems — if a machine consistently breaks down after 200 hours of run time, schedule preventive maintenance before that threshold. Buffer planning: understand the statistical distribution of downtime events to calculate appropriate schedule buffers — enough to absorb typical disruptions without excessive slack. Root cause elimination: Pareto analysis of downtime causes reveals which problems, if solved, would recover the most capacity — enabling targeted improvement that progressively makes the schedule more reliable. LinePlanner's production calendar benefits from downtime intelligence by incorporating realistic capacity assumptions and scheduling maintenance windows during appropriate low-impact periods.