Calculate and diagnose Overall Equipment Effectiveness (OEE) by decomposing into Availability, Performance, and Quality rates. Use this skill when the user needs to measure production line efficiency, identify equipment losses, benchmark manufacturing performance, or justify capital investment — even if they say 'why is our output low', 'machine utilization report', 'production efficiency', or 'how much capacity are we losing'.
npx skills add https://github.com/asgard-ai-platform/skills --skill mfg-oee-analysis
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.
| Factor | Formula | Measures | Loss Categories |
|--------|---------|----------|----------------|
| Availability | Run Time / Planned Production Time | Uptime vs downtime | Equipment failures, changeovers, material shortages |
| Performance | (Ideal Cycle Time × Total Count) / Run Time | Actual speed vs design speed | Minor stops, slow running, idling |
| Quality | Good Count / Total Count | Yield, first-pass quality | Defects, rework, scrap, startup rejects |
| Loss | OEE Factor | Example |
|------|-----------|---------|
| 1. Equipment failure | Availability | Machine breakdown, unplanned repair |
| 2. Setup & changeover | Availability | Product changeover, die change, cleaning |
| 3. Idling & minor stops | Performance | Sensor blockage, jam clearing, small adjustments |
| 4. Reduced speed | Performance | Running below rated speed due to wear or material |
| 5. Process defects | Quality | In-process rejects, rework |
| 6. Startup rejects | Quality | Scrap during warm-up, first-article failures |
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%
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
| OEE Level | Rating | Typical |
|-----------|--------|---------|
| > 85% | World-class | Top manufacturers |
| 60-85% | Typical | Room for improvement |
| 40-60% | Low | Significant losses, urgent action needed |
| < 40% | Critical | Equipment or process fundamentally broken |
# 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} |
references/tpm.mdreferences/oee-automation.mdCreate new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
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Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
Convert PyTorch AT_DISPATCH macros to AT_DISPATCH_V2 format in ATen C++ code. Use when porting AT_DISPATCH_ALL_TYPES_AND*, AT_DISPATCH_FLOATING_TYPES*, or other dispatch macros to the new v2 API. For ATen kernel files, CUDA kernels, and native operator implementations.
Write docstrings for PyTorch functions and methods following PyTorch conventions. Use when writing or updating docstrings in PyTorch code.
Take asgard-ai-platform/mfg-oee-analysis from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.