oee_calculator

oee_calculator ¤

Overall Equipment Effectiveness (OEE) calculator.

OEE = Availability x Performance x Quality

Industry-standard metric for manufacturing productivity: - Availability: actual run time / planned production time - Performance: actual throughput / ideal throughput - Quality: good parts / total parts

OEECalculator ¤

OEECalculator(
    dataframe: DataFrame, *, time_column: str = "systime"
)

Bases: Base

Calculate Overall Equipment Effectiveness from timeseries signals.

Combines availability (from run/idle state), performance (from part counters and ideal cycle time), and quality (from total/reject counters) into a single OEE metric per day.

Example usage

oee = OEECalculator(df)

Individual components¤

avail = oee.calculate_availability('machine_state') perf = oee.calculate_performance('part_counter', ideal_cycle_time=30.0, run_state_uuid='machine_state') qual = oee.calculate_quality('total_counter', 'reject_counter')

Combined daily OEE¤

daily = oee.calculate_oee( run_state_uuid='machine_state', counter_uuid='part_counter', ideal_cycle_time=30.0, total_uuid='total_counter', reject_uuid='reject_counter', )

Initialize OEE calculator.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame with timeseries data.	required
`time_column`	`str`	Name of timestamp column (default: 'systime').	`'systime'`

calculate_availability ¤

calculate_availability(
    run_state_uuid: str,
    planned_time_hours: Optional[float] = None,
    *,
    value_column: str = "value_bool"
) -> pd.DataFrame

Calculate availability percentage from run/idle intervals.

Availability = run_time / planned_time. When planned_time_hours is None the planned time is derived from the first-to-last timestamp span for each day.

Parameters:

Name	Type	Description	Default
`run_state_uuid`	`str`	UUID of the boolean run-state signal (True = running).	required
`planned_time_hours`	`Optional[float]`	Fixed planned production hours per day. If None, the time span covered by data each day is used.	`None`
`value_column`	`str`	Column holding the boolean state.	`'value_bool'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns:
`DataFrame`	date
`DataFrame`	run_seconds
`DataFrame`	planned_seconds
`DataFrame`	availability_pct

calculate_performance ¤

calculate_performance(
    counter_uuid: str,
    ideal_cycle_time: float,
    run_state_uuid: Optional[str] = None,
    *,
    value_column: str = "value_integer",
    run_value_column: str = "value_bool"
) -> pd.DataFrame

Calculate performance percentage (actual vs ideal throughput).

Performance = (actual_parts * ideal_cycle_time) / run_time. If run_state_uuid is None, the total time span per day is used as run time.

Parameters:

Name	Type	Description	Default
`counter_uuid`	`str`	UUID of the monotonic part counter.	required
`ideal_cycle_time`	`float`	Ideal cycle time in seconds per part.	required
`run_state_uuid`	`Optional[str]`	Optional UUID for run-state to compute actual run time.	`None`
`value_column`	`str`	Column holding counter values.	`'value_integer'`
`run_value_column`	`str`	Column holding boolean run state.	`'value_bool'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns:
`DataFrame`	date
`DataFrame`	actual_parts
`DataFrame`	ideal_parts
`DataFrame`	run_seconds
`DataFrame`	performance_pct

calculate_quality ¤

calculate_quality(
    total_uuid: str,
    reject_uuid: str,
    *,
    value_column: str = "value_integer"
) -> pd.DataFrame

Calculate quality percentage (good parts / total parts).

Parameters:

Name	Type	Description	Default
`total_uuid`	`str`	UUID of the total-parts counter.	required
`reject_uuid`	`str`	UUID of the reject-parts counter.	required
`value_column`	`str`	Column holding counter values.	`'value_integer'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns:
`DataFrame`	date
`DataFrame`	total_parts
`DataFrame`	reject_parts
`DataFrame`	good_parts
`DataFrame`	quality_pct

calculate_oee ¤

calculate_oee(
    run_state_uuid: str,
    counter_uuid: str,
    ideal_cycle_time: float,
    total_uuid: Optional[str] = None,
    reject_uuid: Optional[str] = None,
    *,
    planned_time_hours: Optional[float] = None
) -> pd.DataFrame

Calculate daily OEE = Availability * Performance * Quality.

When total_uuid / reject_uuid are not provided, quality is assumed to be 100 %.

Parameters:

Name	Type	Description	Default
`run_state_uuid`	`str`	UUID of the boolean run-state signal.	required
`counter_uuid`	`str`	UUID of the monotonic part counter.	required
`ideal_cycle_time`	`float`	Ideal cycle time in seconds per part.	required
`total_uuid`	`Optional[str]`	Optional UUID of total-parts counter (for quality).	`None`
`reject_uuid`	`Optional[str]`	Optional UUID of reject-parts counter (for quality).	`None`
`planned_time_hours`	`Optional[float]`	Fixed planned production hours per day.	`None`

Returns:

Type	Description
`DataFrame`	DataFrame with columns:
`DataFrame`	date
`DataFrame`	availability_pct
`DataFrame`	performance_pct
`DataFrame`	quality_pct
`DataFrame`	oee_pct