oee 0.1.0

Overall Equipment Effectiveness for Python: availability, performance and quality with the full time waterfall, TEEP, and correct multi-machine roll-up. Computed from the standard definitions and validated against published examples.

oee

Overall Equipment Effectiveness for Python.

Compute OEE (Availability x Performance x Quality) from machine times and piece counts, get the full time waterfall and the three loss categories, TEEP and utilization, and roll figures up correctly across machines and shifts. Computed from the standard definitions and validated against published worked examples.

Motivation

OEE is the standard manufacturing efficiency metric, but Python has no library for it: what exists is monitoring applications (Flask/Django dashboards) or one-off tutorial scripts. The arithmetic looks trivial (three numbers multiplied) and that is exactly why it is usually done wrong:

• the time waterfall (planned -> run -> net run -> fully productive) and where each loss sits is skipped;
• TEEP and utilization (which capture schedule loss) are left out;
• and figures are averaged across machines, which is incorrect: a fast machine and a slow one do not combine to the mean of their OEEs.

oee does these properly, from the standard definitions, and returns one result with the factors, the waterfall, every loss, and provenance.

pip install oee

No runtime dependencies.

Usage

The canonical worked example (Vorne's Fast Guide to OEE):

import oee

r = oee.oee(
    planned_production_time=420,   # minutes (480 shift - 60 of breaks)
    downtime=47,
    ideal_rate=60,                 # pieces per minute
    total_count=19271,
    reject_count=423,
    all_time=480,                  # optional, for TEEP and utilization
)
r.availability   # 0.888
r.performance    # 0.861
r.quality        # 0.978
r.oee            # 0.748
r.teep           # 0.654
print(r.summary())

Roll up across machines (correctly, not by averaging):

m1 = oee.oee(planned_production_time=100, run_time=90, ideal_cycle_time=1,
             total_count=80, good_count=80)     # OEE 0.80
m2 = oee.oee(planned_production_time=300, run_time=150, ideal_cycle_time=1,
             total_count=150, good_count=135)   # OEE 0.45

line = oee.aggregate([m1, m2])
line.oee         # 0.5375, not the 0.625 average of the two

When you already have the three factors:

oee.oee_from_factors(0.90, 0.95, 0.999).world_class   # True (OEE >= 85%)

Every result carries the factors, the time waterfall, the losses, world_class and meets_target flags, summary(), and a JSON-safe to_dict() with provenance (version, input hash, timestamp).

What it computes

Group	Output
Factors	availability, performance, quality, OEE
Extended	TEEP, utilization (when total calendar time is given)
Waterfall	planned -> run -> net run -> fully productive time, with schedule, availability, performance and quality losses
Roll-up	correct aggregation across machines, lines and shifts

All times must be in the same unit; ideal_cycle_time is that unit per piece (or pass ideal_rate in pieces per that unit). Performance above 100% is capped and flagged, since it means the ideal rate or counts are off.

Status

Version 0.1.0. Single-machine OEE, the time waterfall, TEEP/utilization, and correct roll-up. The OEEResult contract is append-only from here.

Roadmap

Version	Scope
0.2	the six big losses (breakdown/setup, minor stops/speed, defects/startup) and a downtime-reason Pareto; computing OEE from an event log
0.3	plotting (the OEE waterfall, six-big-losses and trend charts) as an optional extra
0.4	an MCP server so an agent can compute and explain OEE

Out of scope: data collection / machine connectivity (that is the job of an MES or an IoT dashboard); oee is the calculation layer they can build on.

License

MIT. Written and maintained by Atakan Arikan, MSc Student at Tsinghua University and Politecnico di Milano.