oee 0.2.0

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

Break the losses down into the six big losses:

r = oee.oee(
    planned_production_time=480, downtime=80, ideal_cycle_time=0.5,
    total_count=700, reject_count=100,
    setup_time=30,        # of the 80 min down, 30 was setup
    startup_rejects=40,   # of the 100 rejects, 40 were at startup
)
r.six_losses
# {'breakdowns': 50.0, 'setup_and_adjustments': 30.0,
#  'minor_stops_and_reduced_speed': 50.0,
#  'process_defects': 30.0, 'reduced_yield': 20.0}

Rank the losses (or any downtime-reason breakdown) with a Pareto:

for e in oee.pareto(r.six_losses):
    print(f"{e.label:30} {e.value:5.0f}  {e.share:5.0%}  cum {e.cumulative:5.0%}")
# breakdowns                        50    28%  cum   28%
# minor_stops_and_reduced_speed     50    28%  cum   56%
# process_defects                   30    17%  cum   72%
# setup_and_adjustments             30    17%  cum   89%
# reduced_yield                     20    11%  cum  100%

Or compute it from an event log of production runs and downtime events:

r = oee.from_log(
    planned_production_time=420,
    runs=[{"count": 19271, "good": 18848, "ideal_rate": 60}],
    downtime_events=[
        {"reason": "changeover", "duration": 30, "planned": True},
        {"reason": "jam",        "duration": 17},
    ],
)
r.oee                  # 0.748
r.downtime_reasons     # {'changeover': 30, 'jam': 17} - ready for pareto()

When you already have the three factors:

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

Charts come with the optional plot extra (pip install oee[plot]):

oee.waterfall(r)              # the OEE time waterfall
oee.losses_pareto(r)          # a Pareto of the six big losses
oee.trend(shifts, factors=True)   # OEE and the factors across a sequence

Each draws onto a matplotlib Axes and returns it (ax.figure.savefig(...) to save); matplotlib stays an optional extra, so the core has no dependencies.

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
Six big losses	breakdowns, setup and adjustments, minor stops and reduced speed, process defects, reduced yield
Pareto	rank any loss breakdown by share and cumulative share
Roll-up	correct aggregation across machines, lines and shifts
Charts	waterfall, six-big-losses Pareto and trend (optional plot extra)

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.

Beyond OEE

The same data supports the metrics that surround OEE, each in the same Result style with provenance.

The effectiveness family. Pass the planned downtime and oee() adds OOE (measured over operating time = planned production time + planned downtime), so you get all three at once (TEEP <= OOE <= OEE):

r = oee.oee(planned_production_time=420, downtime=47, ideal_rate=60,
            total_count=19271, reject_count=423, all_time=480, planned_downtime=33)
r.oee, r.ooe, r.teep        # OEE >= OOE >= TEEP

Reliability, the maintenance driver of the availability factor:

oee.reliability(operating_time=1000, failures=5, total_repair_time=50)
# MTBF 200, MTTR 10, inherent availability 95.2%

Yield, the multi-step quality view that extends the single-step quality factor:

oee.rolled_throughput_yield([0.99, 0.98, 0.97]).rty    # 0.941

Capacity, and the money behind the losses:

oee.takt_time(available_time=480, demand=240)          # 2.0 per unit
oee.loss_value(r, value_per_unit=12.0)                 # losses as units and money

Metric	What it adds
oee() with planned_downtime	OOE alongside OEE and TEEP
reliability()	MTBF, MTTR, inherent availability
first_pass_yield(), rolled_throughput_yield()	multi-step quality
takt_time(), capacity()	the pace needed to meet demand
loss_value()	losses in lost units and money

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	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.

References

Definitions

OEE follows the standard definitions: Availability x Performance x Quality, the time waterfall, the six big losses, TEEP and the world-class benchmark.

• Nakajima, S. (1988). Introduction to TPM. Productivity Press. The origin of OEE and the world-class benchmark (availability >= 90%, performance >= 95%, quality >= 99.9%, OEE >= 85%).
• SEMI E79. The semiconductor industry standard for equipment efficiency and OEE.
• Vorne Industries, The Fast Guide to OEE (oee.com).

Worked examples

The validation suite checks the computation against published worked examples, each cited in its case:

Example	OEE	Source
8-hour shift (widgets)	74.79%	Vorne, The Fast Guide to OEE (oee.com)
CNC machining shift	68.52%	TeepTrak, How to Calculate OEE
Shift, clean numbers	72.4%	ReliaMag, How to Calculate OEE
Longer-horizon run	70.6%	ReliaMag, How to Calculate OEE
Packaging line	67.5%	FIRGELLI Automations, OEE Calculator
World-class benchmark	85.4%	Nakajima (1988), Introduction to TPM

License

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