Bereinigung der Anlagenhistorie: Die vierteljährliche Disziplin, die MTBF- und Kostenberichterstattung verlässlich hält.

Key takeaways

• Asset history cleanup = quarterly discipline of reviewing and correcting work order data.
• Asset history decays as WOs close incomplete, reason codes drift, costs get miscoded.
• Dirty history produces wrong MTBF, wrong cost reporting, wrong reliability decisions.
• Quarterly cleanup: sample WOs, verify completeness, fix issues, identify systematic problems.
• Plants that never clean up the history end up with reliability data nobody trusts.

Short answer: Asset history cleanup is the quarterly discipline of reviewing and correcting work order data. WO history decays — labor hours unrecorded, parts uncoded, reasons left vague, costs misallocated. Dirty history produces wrong MTBF, wrong cost reports, wrong reliability decisions. Quarterly cleanup keeps the data honest. See also CMMS Asset Hierarchy.

Why history decays

• Technicians close WOs at end of shift with minimum required data.
• Reason codes are picked under time pressure.
• Parts pulled without scanning.
• Labor hours estimated rather than tracked.
• Costs misallocated to wrong asset.
• Description fields filled with shorthand or skipped.

Each shortcut is small. Over months they accumulate into unreliable history.

Why dirty history matters

Reliability decisions built on history:

• MTBF calculations.
• MTTR calculations.
• Asset cost reporting.
• Spare parts usage patterns.
• PM interval optimization.
• Repair vs replace decisions.

If history is dirty, all of these are wrong. Decisions follow the wrong directions.

The quarterly cleanup protocol

1. Sample. Pick a representative sample (50-100 WOs across asset types).
2. Verify completeness. Required fields filled. Labor recorded. Parts coded. Reasons clear.
3. Score data quality. Per WO and per data type.
4. Identify patterns. Which technicians, shifts, or asset types have systematic issues.
5. Fix individual WOs. Where corrections are possible.
6. Address systematic issues. Training, process change, validation rules.
7. Track quality over time. Trend the score.

What to look for

Labor hours:

• Missing or zero hours on completed WOs.
• Suspiciously round numbers (always 2.0 hours).
• Total team hours not matching work order count.

Parts:

• WOs closed without parts when parts were known to be used.
• Parts pulled from stockroom but not on any WO.
• Cost allocated to wrong asset.

Reason codes:

• "Other" rate high.
• Same code used for very different issues.
• Reason and description contradict.

Description fields:

• "Repair complete" without specifics.
• Empty.
• Single character.

How to score data quality

Simple per-WO scoring:

• Labor hours present and plausible: 1 point.
• Parts scanned: 1 point.
• Reason code specific: 1 point.
• Description meaningful: 1 point.
• Cost properly allocated: 1 point.

5-point WOs are clean; 0-2 point WOs are noise. Trend the score.

What systematic problems look like

1. Single technician with low scores. Coaching opportunity.

2. Single shift with low scores. Supervision or training gap.

3. Single asset type with low scores. WO template may be misaligned with workflow.

4. Single reason code dominating. Taxonomy or process problem.

Address the pattern, not just individual WOs.

Common mistakes

1. No cleanup discipline. History decays indefinitely.

2. Cleanup once. Single cleanup followed by months of decay.

3. Cleanup without root cause. Same patterns recur.

4. Punishment for low scores. Technicians game the metric or hide problems.

What changes with cleanup discipline

• MTBF data trustworthy.
• Cost reporting accurate.
• Spare parts patterns visible.
• Audit-ready records.
• Reliability decisions defensible.

How OEE relates

OEE-CMMS integration depends on clean WO data. When OEE triggers a WO and the WO closes with poor data, the downtime cause is unclear and the loop does not close.

How a modern CMMS supports cleanup

A modern CMMS surfaces data quality scores, identifies low-quality WOs, supports correction workflows, and reports trend.

Fabrico's CMMS includes data quality scoring per WO, identifies cleanup candidates, supports correction workflows, and trends data quality over time.

See how Fabrico captures this automatically — explore OEE for manufacturing or book a demo.

Related reading

• CMMS Asset Hierarchy
• Asset Tag Naming Convention
• Asset Replacement vs Repair

Frequently asked questions

How long does quarterly cleanup take?

2-4 hours per quarter for a mid-sized plant. Less if data quality is sustained.

Who should do the cleanup?

Reliability engineer or maintenance planner. Cross-functional review for systemic issues.

Should I correct old WOs?

Selective. Critical assets yes. Routine work, fix forward only.

What is acceptable data quality?

Average score above 4 out of 5 with clear improvement trend.

How do I get technician buy-in?

Show how clean data helps them: faster part lookups, fewer surprises, better PMs. Avoid blame framing.