What Is OEE for Test Stations

What Is OEE with TofuPilot

Overall equipment effectiveness (OEE) measures how productively a machine or station is used. It combines three factors: availability, performance, and quality. For test stations, OEE reveals how much of your capacity is actually producing good tested units. This guide covers how to calculate OEE for test stations and how to track its components with TofuPilot.

The OEE Formula

OEE = Availability x Performance x Quality

Factor	What It Measures	Formula
Availability	How much of planned time the station is running	Run time / Planned production time
Performance	How fast it runs compared to ideal	(Ideal cycle time x Total units) / Run time
Quality	How many units pass first time	Good units / Total units

Each factor is a percentage. OEE is their product.

Example

Factor	Value	Calculation
Planned time	8 hours (480 min)	Shift duration
Downtime	45 min (setup + fixture change)
Run time	435 min	480 - 45
Availability	90.6%	435 / 480
Ideal cycle time	1 min/unit
Total units tested	400
Performance	92.0%	(1 x 400) / 435
Good units (first pass)	380
Quality	95.0%	380 / 400
OEE	79.2%	90.6% x 92.0% x 95.0%

World-class OEE is 85%+. Most manufacturing operations run 60-75%.

The Six Big Losses

OEE breaks down into six loss categories. Each maps to one of the three OEE factors:

Loss	OEE Factor	Test Station Example
Unplanned stops	Availability	Fixture jam, instrument error, PC crash
Setup and adjustments	Availability	Product changeover, calibration, fixture swap
Small stops	Performance	Operator absent, DUT loading delay
Slow cycles	Performance	Instrument settling, retests, slow communication
Production rejects	Quality	Units that fail test and go to scrap
Startup rejects	Quality	First units after setup that need retesting

Prerequisites

Python 3.10+
OpenHTF installed (pip install openhtf)
TofuPilot Python SDK installed (pip install tofupilot)

Step 1: Capture Test Data

Every test run logged to TofuPilot contributes data to the three OEE factors. The test duration feeds performance. The pass/fail result feeds quality. Station uptime feeds availability.

production_test.py

import openhtf as htf
from openhtf.util import units


@htf.measures(
    htf.Measurement("output_voltage_V")
    .in_range(minimum=4.9, maximum=5.1)
    .with_units(units.VOLT),
    htf.Measurement("current_draw_mA")
    .in_range(minimum=90, maximum=110)
    .with_units(units.MILLIAMPERE),
)
def phase_functional(test):
    """Production functional test."""
    test.measurements.output_voltage_V = 5.01
    test.measurements.current_draw_mA = 99.2

production_test.py

from tofupilot.openhtf import TofuPilot

test = htf.Test(phase_functional)

with TofuPilot(test):
    test.execute(test_start=lambda: input("Scan serial: "))

Step 2: Track OEE Components in TofuPilot

TofuPilot tracks the data you need for each OEE factor:

OEE Factor	TofuPilot Data
Availability	Station uptime, gaps between test runs
Performance	Test duration per unit, throughput per hour
Quality	First pass yield per station

Open the Analytics tab to monitor:

Station throughput (units per hour) tracks performance
First pass yield tracks quality
Test gaps (time between consecutive runs) reveal availability losses

Improving OEE

Focus on the lowest factor first. If availability is 70% but quality is 98%, fixing availability gives the biggest return.

Low Factor	Where to Look	Action
Availability below 85%	Downtime log, changeover time	Reduce setup time, improve fixture reliability
Performance below 90%	Phase durations in TofuPilot	Optimize instrument settings, reduce settling time
Quality below 95%	Failure Pareto in TofuPilot	Fix top failure modes, tighten process controls

Small improvements compound. Improving each factor by 5% (from 85% to 90%) increases OEE from 61.4% to 72.9%. That's 19% more capacity from the same equipment.