Maintenance work orders track parts costs and labor hours, but the bigger operational impact is often how long equipment was out of service. A 2-hour repair that takes a critical pump offline during production costs more than the parts and labor—it costs lost production time. Downtime tracking in Antero records when equipment went offline and came back online, so you can measure true operational impact and manage equipment reliability strategically.
Why track downtime?
Equipment availability drives production capacity. If a critical pump is available 99% of the time, you can plan production around rare outages. If it’s available 85% of the time, you need backup pumps or process changes. Downtime tracking provides the data to calculate mean time between failures (MTBF), mean time to repair (MTTR), and overall equipment effectiveness (OEE). These metrics inform capital planning, maintenance strategy, and staffing decisions.
How to record downtime on a work order
Open a work order in Antero. Go to the Downtime section (usually a tab or button within the work order interface). Click Add Downtime Entry. Enter the Down Time (when equipment went offline) and Up Time (when it returned to service). Antero calculates total downtime automatically. Add notes if needed to explain why downtime was longer than expected (waiting for parts, waiting for operator, coordinating with production). Save the entry. The downtime is now recorded on the work order and available for reporting.
Capture downtime for emergency repairs
Downtime tracking is most critical for unplanned failures. When a motor fails at 2:00 AM and the night shift scrambles to repair it, the work order should record: Down Time 2:00 AM, Up Time 6:30 AM, total downtime 4.5 hours. This data shows the true cost of the failure beyond parts and labor. If that motor fails every 6 months and averages 4 hours downtime per failure, you’re losing 8 hours of production capacity annually. Multiply by production value per hour, and suddenly motor replacement looks cost-effective even if the motor “still runs.”
Track downtime for scheduled maintenance too
Even planned preventive maintenance causes downtime. An annual pump overhaul might take 8 hours while production runs on backup systems. Record that 8-hour downtime on the PM work order. Over time, you can calculate how much production capacity is lost to scheduled maintenance versus unscheduled failures. If scheduled maintenance accounts for 20% of total downtime, that’s acceptable. If it’s 60%, you might be over-maintaining or scheduling maintenance during production hours unnecessarily.
Differentiate types of downtime
Not all downtime is equal. Some plants use downtime tracking to differentiate: equipment waiting for repair (true downtime), equipment under active repair (maintenance downtime), equipment waiting for production to restart (queued time). Antero’s notes field lets you add context. This granularity helps identify bottlenecks. If equipment repairs take 2 hours but waiting for parts takes 6 hours, the problem isn’t repair speed—it’s parts availability.
Calculate equipment availability metrics
With downtime tracking data accumulated over months or years, Antero reporting can calculate equipment availability: (Total Operating Hours – Total Downtime) / Total Operating Hours. A pump that ran 8,600 hours last year with 200 hours downtime has 97.7% availability. Industry benchmarks vary, but most plants target 95%+ for critical equipment. Anything below 90% flags a reliability problem requiring root cause analysis or capital replacement.
Support root cause analysis
When downtime patterns emerge—same equipment failing every 3 months, same type of failure—downtime trackingdata supports root cause analysis. Pull a report showing all work orders with downtime for that equipment. Review failure descriptions, parts replaced, and conditions at failure. The pattern might reveal operator error, inadequate maintenance intervals, environmental factors (heat, vibration), or end-of-life issues. Data-driven root cause analysis beats guessing.
Justify capital equipment replacement
When arguing for budget to replace aging equipment, downtime tracking provides the financial justification. Calculate total downtime hours for the equipment over the past year. Multiply by production value per hour (or cost of downtime in other terms). Add parts and labor costs from work orders. Compare that total cost of ownership to the price of new equipment. If an aging compressor costs $50,000/year in maintenance, parts, and downtime, and a new compressor costs $80,000, the payback period is under 2 years. That’s a compelling business case built on downtime tracking data.
Inform maintenance vs replacement decisions
Downtime tracking also informs smaller repair-vs-replace decisions during active work orders. If a pump fails and repair will take 12 hours downtime plus $5,000 in parts, but a new pump costs $8,000 and installs in 4 hours, the downtime cost difference might tip the decision toward replacement. Without tracking downtime historically, you wouldn’t know whether 12 hours is typical or excessive for this equipment, making the decision harder.
Set downtime reduction targets
Once you start downtime tracking, you can set improvement targets. If last year’s total downtime for critical equipment was 400 hours, target 300 hours this year through better PM, operator training, or targeted replacements. Track progress monthly. Celebrate wins when downtime drops. Investigate when it spikes. Continuous improvement requires measurable baselines—downtime tracking provides that baseline.
Why this metric matters most
Parts costs and labor hours matter, but downtime is often the biggest cost. A $500 repair that takes a production line offline for 4 hours might cost $10,000 in lost production. Downtime tracking makes that invisible cost visible, which changes how maintenance teams prioritize reliability, how managers justify budgets, and how plants think about asset management strategy.
Next Steps: Track equipment downtime and reliability in Antero →
