Explain OEE Changes Faster
When the monthly plant governance review comes up, the Continuous Improvement / Process Excellence Manager has to pull OEE component data from MES or BI OEE reporting, reconcile it in spreadsheets, compare the current month to the prior month, and then defend which component actually drove the change. In the candidate value model, about 50% of the role is spent on execution work such as data pulls and scorecard preparation, and this workflow automates roughly 65% of that standardized variance-table work. That means the manager is still spending meaningful time each month assembling KPI evidence instead of using the review to diagnose the problem and direct corrective action.
OEE Variance Attribution is a Decision Intelligence workflow designed for the Continuous Improvement / Process Excellence Manager. Its execution layer handles cross-period KPI retrieval, OEE and component variance calculation, primary-driver identification, and executive-ready table formatting—about 32.5% of total role time recovered based on the candidate's automation model. The manager gets back time to frame the issue correctly, challenge plant assumptions, and route the right improvement workstream.
See the Decision Intelligence Workflow in Action
This workflow handles a recurring comparative analysis task: calculate plant OEE for the current and prior periods, decompose the change into Availability, Performance, and Quality, and identify the largest negative component variance. The compound task is not the math by itself; it is the repeated work of pulling the same KPI inputs, aligning period definitions, checking consistency, and packaging the result in a form stakeholders will accept.
What the Decision Intelligence Workflow Does
This workflow handles a recurring comparative analysis task: calculate plant OEE for the current and prior periods, decompose the change into Availability, Performance, and Quality, and identify the largest negative component variance. The compound task is not the math by itself; it is the repeated work of pulling the same KPI inputs, aligning period definitions, checking consistency, and packaging the result in a form stakeholders will accept.
Click any step below to see the business logic, data query, and sample output for that step of the workflow.
The workflow produces a standardized variance table showing prior period, current period, absolute variance, and percent variance for OEE and each component, plus a one-line directional statement naming the primary driver. That output can be inserted into a governance scorecard, monthly plant slide, or team channel update.
Data Warehouse Integration
The workflow runs against a manufacturing KPI mart where daily plant-level Availability, Performance, and Quality measures are already stored and standardized. It uses those records to compute month-over-month OEE comparisons consistently, without relying on manually maintained spreadsheet logic.
That matters because the review itself is cross-functional: Operations, Supply Chain, and Finance all need the same period comparison and driver attribution from a common data foundation.
CREATE TABLE pura_vida_foods_dev.manufacturing_mart.mart_manufacturing_oee_daily (
plant_id STRING,
work_date DATE,
availability DOUBLE,
performance DOUBLE,
quality DOUBLE
);
WITH cur AS (
SELECT
plant_id,
AVG(availability) AS availability,
AVG(performance) AS performance,
AVG(quality) AS quality
FROM pura_vida_foods_dev.manufacturing_mart.mart_manufacturing_oee_daily
WHERE plant_id = string">'{plant_id}'
AND work_date BETWEEN string">'{current_start_date}' AND string">'{current_end_date}'
GROUP BY plant_id
),
prior AS (
SELECT
plant_id,
AVG(availability) AS availability,
AVG(performance) AS performance,
AVG(quality) AS quality
FROM pura_vida_foods_dev.manufacturing_mart.mart_manufacturing_oee_daily
WHERE plant_id = string">'{plant_id}'
AND work_date BETWEEN string">'{prior_start_date}' AND string">'{prior_end_date}'
GROUP BY plant_id
)
SELECT
cur.plant_id,
prior.availability AS prior_availability,
cur.availability AS cur_availability,
prior.performance AS prior_performance,
cur.performance AS cur_performance,
prior.quality AS prior_quality,
cur.quality AS cur_quality,
(prior.availability * prior.performance * prior.quality) AS prior_oee,
(cur.availability * cur.performance * cur.quality) AS cur_oee
FROM cur
JOIN prior USING (plant_id);Where the Work Sits in the Labor Stack
Not all cognitive labor is equally automatable. The KWF analysis breaks the workflow into three layers — execution, judgment, and strategic — and maps each step to the layer it belongs to. Execution-layer work is automatable. Judgment and strategic work stays with the manager.
The Business Case for Automation
Primary Valuation Metric: Percent of monthly plant KPI reviews using the standardized OEE variance table, with a target adoption rate of at least 80%.
Next Step
Run your own value estimate for OEE variance reporting — or talk to us about your continuous improvement team.
The Cognitive Labor Value Calculator models team size, role cost, and automation coverage so you can estimate the impact of standardizing OEE variance attribution work. It takes under two minutes to complete.