oee.computeAPQ() function

The oee.computeAPQ() function computes availability, performance, and quality (APQ) and overall equipment effectiveness (OEE) using two separate input streams—production events and part events. oee.computeAPQ() is an aggregate function.

import "experimental/oee"

oee.computeAPQ(
  productionEvents: exampleProductionScheme,
  partEvents: examplePartsStream,
  runningState: "running",
  plannedTime: 8h,
  idealCycleTime: 2m,
)

Output schema

For each input table, the oee.computeAPQ function outputs a table with a single row and the following columns:

• _time: Timestamp associated with the APQ calculation.
• availability: Ratio of time production was in a running state.
• oee: Overall equipment effectiveness.
• performance: Ratio of production efficiency.
• quality: Ratio of production quality.
• runTime: Total nanoseconds spent in the running state.

Parameters

productionEvents

(Required) Production events stream that contains the production state or start and stop events. Each row must contain the following columns:

• _stop: Right time boundary timestamp (typically assigned by range() or window()).
• _time: Timestamp of the production event.
• state: String that represents start or stop events or the production state. Use runningState to specify which value in the state column represents a running state.

partEvents

(Required) Part events that contains the running totals of parts produced and parts that do not meet quality standards. Each row must contain the following columns:

• _stop: Right time boundary timestamp (typically assigned by range() or window()).
• _time: Timestamp of the parts event.
• partCount: Cumulative total of parts produced.
• badCount Cumulative total of parts that do not meet quality standards.

runningState

(Required) State value that represents a running state.

plannedTime

(Required) Total time that equipment is expected to produce parts.

idealCycleTime

(Required) Ideal minimum time to produce one part.

Examples

The following example uses two streams of data (productionData and partsData) and oee.APQ() to calculate the APQ and OEE of an eight hour production window.

Input data

productionData

partsData

Query

import "experimental/oee"

productionData = // ...
partsData = // ...

oee.computeAPQ(
  productionEvents: productionData,
  partEvents: partsData,
  runningState: "running",
  plannedTime: 8h,
  idealCycleTime: 21s
)
|> drop(columns: ["_start","_stop"])

Output data