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mfg-oee-analysis

mfg-oee-analysis

github.com/asgard-ai-platformOther24338 installsUpdated 21w ago
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About mfg-oee-analysis

An OEE analysis skill for calculating and diagnosing equipment effectiveness on production lines and machines. It decomposes results into availability, performance, and quality, then guides loss prioritization and root-cause analysis.

Key features

  1. 01Availability, performance, and quality calculation
  2. 02Six-loss mapping to OEE factors
  3. 03Weakest-factor diagnosis
  4. 04Pareto loss prioritization
  5. 05Root-cause analysis and remeasurement

Use cases

  1. 01Diagnosing machine or line efficiency
  2. 02Finding leading causes of production loss
  3. 03Tracking results after process improvements

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/asgard-ai-platform/skills --skill mfg-oee-analysis

After installing, type/mfg-oee-analysisin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

examples/
sample_scenario.md
references/
oee-automation.md
tpm.md

Details

From the project README

OEE Analysis

Framework

IRON LAW: OEE = Availability × Performance × Quality

OEE is a MULTIPLICATIVE metric. 90% × 90% × 90% = 72.9%, not 90%.
Each factor compounds the loss. World-class OEE is 85%+. Most plants
operate at 60-65%. Knowing the TOTAL is useless — you must decompose
to find which factor is dragging performance down.
The Three Factors
FactorFormulaMeasuresLoss Categories
AvailabilityRun Time / Planned Production TimeUptime vs downtimeEquipment failures, changeovers, material shortages
Performance(Ideal Cycle Time × Total Count) / Run TimeActual speed vs design speedMinor stops, slow running, idling
QualityGood Count / Total CountYield, first-pass qualityDefects, rework, scrap, startup rejects
Six Big Losses (mapped to OEE factors)
LossOEE FactorExample
1. Equipment failureAvailabilityMachine breakdown, unplanned repair
2. Setup & changeoverAvailabilityProduct changeover, die change, cleaning
3. Idling & minor stopsPerformanceSensor blockage, jam clearing, small adjustments
4. Reduced speedPerformanceRunning below rated speed due to wear or material
5. Process defectsQualityIn-process rejects, rework
6. Startup rejectsQualityScrap during warm-up, first-article failures
Calculation Example
Planned Production Time: 480 min (8-hour shift)
Downtime (breakdowns + changeover): 60 min
Run Time: 420 min

Ideal Cycle Time: 1 min/unit
Total Units Produced: 380

Good Units: 360
Defective Units: 20

Availability = 420 / 480 = 87.5%
Performance = (1 × 380) / 420 = 90.5%
Quality = 360 / 380 = 94.7%

OEE = 87.5% × 90.5% × 94.7% = 75.0%
Diagnosis Steps

Phase 1: Calculate OEE for each production line/machine Phase 2: Identify the weakest factor (Availability, Performance, or Quality) Phase 3: Pareto the losses within that factor (which specific loss is biggest?) Phase 4: Root cause analysis on the top loss (5 Whys, fishbone) Phase 5: Improve and remeasure

Benchmarks
OEE LevelRatingTypical
> 85%World-classTop manufacturers
60-85%TypicalRoom for improvement
40-60%LowSignificant losses, urgent action needed
< 40%CriticalEquipment or process fundamentally broken

Output Format

# OEE Report: {Production Line}

## OEE Summary
| Factor | Value | Benchmark | Status |
|--------|-------|-----------|--------|
| Availability | {%} | >90% | 🟢/🟡/🔴 |
| Performance | {%} | >95% | 🟢/🟡/🔴 |
| Quality | {%} | >99% | 🟢/🟡/🔴 |
| **OEE** | **{%}** | **>85%** | 🟢/🟡/🔴 |

## Loss Breakdown
| Loss | Minutes Lost | % of Total Loss | Priority |
|------|-------------|----------------|---------|
| {loss type} | {min} | {%} | 1/2/3 |

## Root Cause (Top Loss)
{5 Whys or fishbone analysis}

## Improvement Plan
| Action | Target Impact | Timeline | Owner |
|--------|-------------|----------|-------|
| {action} | +{X%} OEE | {weeks} | {who} |

Gotchas

  • OEE is per machine, not per plant: Plant-level OEE averages hide that one machine at 95% and another at 45% average to 70%. Analyze individually.
  • Planned downtime is excluded: OEE measures losses against PLANNED production time. Scheduled maintenance, no-production shifts, and planned shutdowns are excluded from the denominator.
  • 100% OEE is not the goal: It would mean zero changeovers, zero defects, running at max speed 100% of the time. Pursuing 100% can increase costs (e.g., never doing preventive maintenance). Target 85%+ for critical lines.
  • Data collection is the real challenge: Manual OEE tracking is inaccurate. Invest in automated data collection (sensors, MES integration) for reliable measurement.

References

  • For TPM (Total Productive Maintenance) methodology, see references/tpm.md
  • For automated OEE data collection, see references/oee-automation.md

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