MTBF — Mean Time Between Failures
MTBF measures how long, on average, a piece of equipment runs between unplanned failures. It is the primary indicator of asset reliability: a rising MTBF means your preventive maintenance programme is working; a falling MTBF is an early warning that something is getting worse.How Bold calculates MTBF
Bold calculates MTBF for each asset from the corrective work orders logged against it:How to use MTBF
- Compare assets — Sort your equipment register by MTBF ascending to instantly see which machines fail most frequently. These are your priority targets for deeper preventive investment.
- Measure the effect of changes — If you increased lubrication frequency on a machine last month, check whether MTBF improved in the weeks that followed. MTBF is the clearest way to verify that a maintenance change is working.
- Set replacement thresholds — When an asset’s MTBF falls below a threshold you define — for example, under 200 hours — it may be more economical to replace it than to keep repairing it.
MTBF is only meaningful for assets with multiple logged corrective incidents. For new equipment or assets that rarely fail, monitor MTBF directionally over a longer time window rather than treating a single data point as representative.
MTTR — Mean Time To Repair
MTTR measures how long it takes, on average, to resolve a failure from the moment it is reported to the moment the asset is back in service. It is the primary indicator of your maintenance team’s responsiveness and the effectiveness of your repair workflows.How Bold calculates MTTR
Bold derives MTTR from the timestamps on your corrective work orders:How to use MTTR
- Identify slow repairs — Filter MTTR by failure category to see which types of breakdown take the longest to resolve. Long MTTR on a specific category often points to spare parts availability issues, skill gaps, or unclear repair procedures.
- Benchmark across shifts — Break MTTR down by shift to see whether night or weekend crews take longer to resolve the same types of failure. A significant difference usually reveals a training or tooling gap on specific shifts.
- Track improvement over time — If you introduced a new spare parts stocking policy or updated a repair procedure, MTTR is the fastest way to see whether the change had the intended effect.
Equipment availability
Availability ties MTBF and MTTR together into a single percentage that represents how much of the scheduled operating time an asset was actually available to produce:Interpreting availability
| Availability range | What it typically signals |
|---|---|
| > 98% | High reliability; focus on sustaining the current preventive regime |
| 95–98% | Acceptable but improvable; investigate the most frequent failure modes |
| 90–95% | Significant reliability gap; prioritize this asset for deeper preventive work |
| < 90% | Critical; consider whether the current maintenance approach is sufficient or whether capital investment is warranted |
Breaking down KPIs by machine, cause, and shift
Aggregate KPIs tell you how the operation is performing overall. Breakdowns are what tell you where to act. Bold’s CMMS analytics let you slice every KPI along three key dimensions:By machine
By machine
Filter MTBF, MTTR, and availability to a single asset or a group of assets (by line, by area, or by asset type). Ranking machines by availability ascending gives you an instant priority list of the assets that are costing you the most production time.
By failure cause
By failure cause
Filter by the incident categories you defined when logging corrective work orders. If 60% of your corrective hours last quarter were categorized as “bearing failure,” you now have a specific, actionable target — and you can trace exactly which machines and which operating conditions are involved.
By shift
By shift
Compare KPIs across day, evening, and night shifts. Systematic differences in MTTR by shift point to resource, training, or handover issues. Differences in failure rate by shift can indicate operating practice problems or environmental conditions worth investigating.
Identifying your most problematic assets
Use Bold’s asset reliability dashboard to run a quick Pareto analysis:Sort by total corrective hours
Open the asset reliability table and sort by total corrective maintenance hours over your chosen period. The machines at the top of this list consume the most repair effort and likely represent the largest opportunity.
Check the failure frequency
For each high-cost asset, check its MTBF. Is the cost driven by many small failures or a few long repairs? The answer determines the right response.
Look at failure causes
Click into the asset’s incident history and review the categorized failure causes. If the same failure mode appears repeatedly, you are looking at a systemic problem — not random bad luck.
Detecting root causes and escaping the firefighting loop
The most common maintenance trap is spending so much time on corrective repairs that there is never time for the preventive work that would reduce them. Bold’s analytics help you break that cycle by making root causes visible. Here is the analytical workflow:- Identify recurring failure modes — Use the failure cause breakdown to find categories that keep reappearing. A failure mode that accounts for more than 20% of your corrective incidents is a root-cause candidate.
- Trace it to specific assets — Filter that failure mode to see which machines it affects most. Often a single asset or a group of similar assets in the same operating condition is responsible.
- Check the intervention history — Review the corrective work orders on that asset in sequence. Look for patterns: does the failure always occur at a similar point in the production cycle? After a specific maintenance action? In a particular shift?
- Design a preventive response — Convert your finding into a preventive maintenance plan: a new inspection task, an adjusted lubrication frequency, a scheduled component replacement before the failure threshold is reached.
- Measure the effect — Monitor the asset’s MTBF over the following months. A rising MTBF confirms that the root cause has been addressed, not just patched.
Bold’s dashboards display all of this data in real time as you close work orders. The more consistently your team logs incidents with accurate categories and timestamps, the faster the analytics become actionable. Even two to three months of clean data is enough to reveal your most significant reliability patterns.