Planned vs Unplanned Maintenance Mix

Goal of the analysis:

Measure the proportion of maintenance effort that is proactively planned (preventive, predictive/condition‑based, and planned corrective) versus reactive (unplanned corrective, breakdowns, emergencies), diagnose why reactive work occurs, and shift the mix toward planned work. A healthier planned mix reduces unplanned downtime, stabilizes Availability/OEE, lowers overtime and premium parts costs, and improves safety and quality. Executives use this analysis to size planner/scheduler capacity, optimize PM/CBM content (RCM), align maintenance windows with production, and prioritize spares and skills investment on critical assets.

Data required:

• Work management (CMMS/EAM):
  • Work orders with type (PM, CBM, planned corrective, unplanned corrective, emergency), priority, asset, planned start/finish, actual start/finish, planned vs actual labor hours, status (completed, deferred, cancelled), deferral reasons.
  • Backlog by type/craft/asset and age; planner/scheduler assignments; weekly frozen schedule and break‑in work.
• Asset criticality and reliability context:
  • Asset hierarchy and criticality (A/B/C), constraint assets, failure history, MTBF/MTTR, downtime minutes by cause.
• Production and downtime (MES/SCADA/OEE):
  • Planned production windows, changeovers/shutdowns; unplanned downtime events and durations linked to assets and causes.
• Labor and workforce (LMS/HR):
  • Technician rosters, skills/certifications by craft, overtime, contractor usage; response SLAs.
• Spares and materials (Storeroom/ERP):
  • Parts availability, kit completeness, reservations against PMs, stockouts, lead times, min/max for critical spares.
• Policies and normalization:
  • Definitions of “planned” (frozen ≥1 week out), “unplanned,” and “emergency”; schedule freeze horizon; holiday/shift calendars; time zones.

Detailed step-by-step instruction on how to conduct the analysis:

1. Define metrics and classification rules.
   • Planned Work Ratio (%) = (PM + CBM + planned corrective labor hours) ÷ Total maintenance labor hours × 100.
   • Unplanned Work % = Unplanned corrective labor hours ÷ Total × 100; Emergency Work % = Emergency labor hours ÷ Total × 100.
   • Weekly Schedule Compliance (%) = Work orders completed from frozen weekly schedule ÷ Scheduled WOs × 100.
   • Break‑in Work % = Hours added after freeze ÷ Total scheduled hours × 100.
   • Backlog mix and age by type; Overdue PMs (count/age). Establish what counts as “planned corrective” (identified, job‑planned, parts reserved before freeze).
2. Extract and align data.
   • Pull 12–13 weeks of CMMS work orders with types, timestamps, labor, parts; import weekly frozen schedules and break‑in flags.
   • Join to MES downtime logs for the same assets/period to relate mix to Availability and unplanned downtime; bring asset criticality and technician rosters.
3. Cleanse and standardize.
   • Deduplicate split/child WOs; map reissued PMs to original due date; normalize priorities and types (e.g., “urgent” vs “emergency”).
   • Correct misclassified work (e.g., retroactively approved PMs logged as emergency); standardize deferral reasons (parts/access/labor/production).
4. Compute the mix and compliance.
   • Calculate Planned Work Ratio, Unplanned %, Emergency %, Schedule Compliance, Break‑in %, and backlog/overdue PMs by site, asset family, criticality, craft, and planner.
   • Produce time series (weekly) and quartiles to show stability and improvement opportunities.
5. Segment and localize root causes.
   • Slice by shift/crew, day vs night/weekend, contractor vs internal, and constraint vs non‑constraint assets.
   • Attribute unplanned/emergency hours to primary causes: chronic failure modes (from CMMS), parts stockouts, access/production conflicts, skill gaps/response delays, ineffective PM (failures right after PM).
6. Link to reliability and production outcomes.
   • Correlate Planned Work Ratio vs Unplanned Downtime Hours, Availability, and MTBF by asset; quantify capacity days lost due to unplanned/emergency work.
   • Estimate $ impact: overtime, premium parts/freight, lost contribution from downtime at the constraint.
7. Trend and control.
   • Plot 12–16 weeks of Planned Work %, Unplanned %, Emergency %, and Schedule Compliance with control limits; annotate shutdowns, planner changes, kitting rollouts.
   • Track backlog age reductions for A‑critical assets.
8. Scenario and what‑if.
   • Model lift in Planned Work % and reduction in unplanned downtime from: planner staffing + kitting (−break‑ins), PM/RCM optimization, CBM deployment on top assets, coordinated windows with production, and spares min/max updates.
   • Quantify recovered availability and $ savings; build a 90‑day roadmap with milestones.
9. Integrity checks.
   • Reconcile CMMS labor to payroll; ensure hours sum to 100% across categories; keep “unknown/other” <10%.
   • Audit a sample of “planned corrective” jobs for plan quality (job plan, parts reserved, scheduled prior to freeze).

Format of the output of analysis:

• Executive scorecard: Planned Work Ratio %, Unplanned % and Emergency %, Weekly Schedule Compliance %, Break‑in %, backlog/overdue PMs (count/age), Availability/MTBF overlay, $ impact, trend vs target.
• Heatmaps: mix by site × asset family/criticality and by planner/craft/shift; highlight bottom quartile cells.
• Cause bridge: contribution of chronic failures, parts stockouts, access conflicts, response/skill gaps, and PM ineffectiveness to unplanned/emergency hours.
• Backlog dashboard: PM/planned corrective backlog by age and criticality; at‑risk A‑assets.
• Scenario/ROI panel: projected Planned Work % increase, downtime reduction, and $/capacity gains from kitting, RCM/CBM, staffing, and window alignment.

How to interpret results:

• Planned Work Ratio ≥70–80% with Emergency ≤10%: Healthy mix; expect lower unplanned downtime and stable Availability. Optimize PM content for cost-effectiveness (RCM/CBM).
• High Unplanned/Emergency (≥25–30%): Reactive culture—typically manifests as higher unplanned downtime, overtime, and premium parts. Root causes are often parts readiness, lack of frozen schedule, and chronic failures without RCAs.
• High Break‑in % with low Schedule Compliance: Weak planning/freeze discipline or poor coordination with production; expect techs to be constantly rescheduled and wrench time to drop.
• High planned % but downtime unchanged: PM/CBM content may be misaligned or intrusive; review effectiveness, shift to condition‑based tasks, and coordinate windows better.
• Unplanned hours concentrated on A‑assets/constraint: Largest throughput risk; prioritize RCAs, CBM, and spares kitting there first.
• Unplanned driven by parts wait: Storeroom min/max and kitting gaps; address before adding tech headcount.

Steps a company can take to improve on this measure:

• Planning and scheduling discipline:
  • Establish a frozen weekly schedule (e.g., freeze ≥70–80% of capacity) with joint Ops–Maintenance review; limit break‑ins to approved emergencies.
  • Right‑size planner/scheduler ratio (≈1 planner per 15–20 techs); standardize job plans with estimated hours and task lists.
• Spares and kitting readiness:
  • Build PM/planned corrective kits; reserve parts at plan time; clean up BOMs; maintain min/max and lead times for critical spares; measure kit completeness as a release gate.
• Optimize PM/CBM content (RCM):
  • RCM review on A‑assets to remove low‑value PMs and add predictive checks (vibration, thermography, oil); time‑box intrusive PMs and align to demand valleys.
• Reliability and RCA on chronic failures:
  • RCA the top repeat failure modes; implement design upgrades/derating; standardize centerlines and operator care to prevent induced failures.
• Operations alignment:
  • Coordinate maintenance with changeovers and low‑demand windows; include maintenance windows in the production plan; define access SLAs to cut deferrals.
• Workforce capability and governance:
  • Balance shift coverage; certify techs on top failure diagnostics; weekly huddles on Planned vs Unplanned mix, Schedule Compliance, and Emergency % with owner actions.
  • Track Wrench Time and reduce travel/wait via staging/permitting readiness.
• Digital enablement:
  • Deploy mobile CMMS for real‑time updates; integrate MES–CMMS to trigger planned corrective work from condition alarms; analytics to predict parts demand.
• Example scenarios:
  • If Planned Work is 55% and Emergency 18% with 30% parts‑caused deferrals, implement PM kitting and spares min/max for top 50 A‑assets; target Planned ≥75% and Emergency ≤10% in 8–10 weeks.
  • If Break‑in is 25% and Schedule Compliance 62%, enforce a 1‑week freeze and joint scheduling; expect +15–20 pts compliance and −10–15 pts unplanned share.
  • If planned share is high but failures persist, run RCM on the top 10 assets and add CBM; aim for MTBF +20% and Unplanned Downtime −30% within a quarter.

Benchmark comparisons:

General benchmarks (directional):

• Planned Work Ratio: typical 60–75%; world‑class ≥75–85% with Emergency ≤10% of total hours.
• Weekly Schedule Compliance: 80–90% in disciplined programs; Break‑in ≤10–15% of weekly hours.
• Backlog health: A‑asset PM/planned corrective backlog <2–4 weeks of work; overdue PMs on A‑assets near zero.

Constructing internal benchmarks:

• Track 12–16 weeks of Planned vs Unplanned/Emergency mix by site/asset family/planner; set guardrails (e.g., Emergency >15% or Break‑in >20% triggers action).
• Set criticality‑based targets (A‑assets: Planned ≥85%; B: ≥75%; C: ≥65%) and prioritize backlog burn‑down accordingly.
• Pair mix metrics with Unplanned Downtime Hours, Availability, and MTBF/MTTR to confirm that mix improvements translate to reliability and throughput gains.
• Rebaseline after CMMS cleanup, kitting, RCM/CBM rollouts, or staffing changes; codify best‑performing sites’ planning cadence and kitting standards.