Workbook

OEE Diagnosis Workbook: Interactive Loss Capture & Experiment Planner

An interactive, fillable workbook that helps frontline teams measure OEE consistently, capture shift losses, drill into root causes, design short experiments, verify impact, and sustain improvements with clear owners and dates. Designed for repeatable use and for saving structured submissions to the platform.

Interactive Tool

OEE Diagnosis Workbook

Use this workbook to diagnose your biggest OEE losses and run short, operator-centred experiments that produce measurable gains.

This interactive form guides a frontline team through reliable measurement, shift-by-shift loss capture, root-cause drill-down for the top three losses, an experiment canvas, verification & sustainment checks, and a simple 30/60/90 results tracker.

How to use it: Fill the Measurement section for a representative shift or period. Use the Loss Capture entries to record the most important events during a shift (repeat daily or per shift). Complete the Root-Cause and Experiment sections for your top 1–3 losses. Save the form each time you complete an experiment cycle; submissions are stored so teams can compare results over time.

Key formulas (for manual calculation):

  • Availability % = (Planned Production Time - Unplanned Stop Time) / Planned Production Time × 100
  • Performance % = (Ideal Cycle Time × Total Count) / Net Run Time × 100
  • Quality % = Good Count / Total Count × 100
  • OEE % = Availability × Performance × Quality (expressed as percentages, multiply as fractions)

If you'd like automatic calculations, consider enabling a connected OEE dashboard or adding calculated fields via platform enhancements (see Capability Notes).

Date and shift (e.g., 2026-08-27, Shift A).
Where this measurement was taken (plant, line, cell).
Name or role of the person reporting.
Fill these numbers for the period or shift you are diagnosing.
Total scheduled production time for the shift/period (exclude planned breaks).
Total minutes lost to unplanned stops during the period.
Manufacturer or target cycle time per good part.
Total number of parts started/produced in the period.
Count of parts meeting quality acceptance.
Enter calculated availability if you compute it offline. Formula in the intro.
Enter calculated performance if you compute it offline.
Enter calculated quality if you compute it offline.
Enter overall OEE if you compute it.
Use one row per loss event. The form provides multiple slots — add the top 6 losses for this period.
Choose the primary loss category.
Total minutes for this loss event (or aggregated for similar events).
Number of occurrences or defective parts related to this loss.
Short description of what happened.
For each top loss, work through root causes and specify owner and target dates.
Short label (e.g., 'Hydraulic pump failure').
Write a concise 5-why chain or key contributing causes.
People, parts, process, environment, tools, data, training, documentation.
Shortlist practical fixes to test.
Time to run the first experiment trial (e.g., 7).
Design short, testable experiments for your top countermeasures.
Short name for the experiment.
Make the expected cause-effect clear and measurable.
Short, timeboxed trial length (e.g., 7).
Example: 3 (meaning +3 percentage points).
Be specific and measurable.
People, spares, tools, and any safety/quality mitigations.
Use this checklist after an experiment shows positive results.
Have data capture templates, stop codes, and owners been updated?
Is there an updated standard operating procedure?
Have impacted operators been trained?
Are controls and monitoring in place to sustain the gain?
Who owns long-term sustainment?
When will the sustainment be reviewed?
Record OEE or component results to see if gains hold.
OEE before the experiment.
Observations, unexpected consequences, lessons learned.
What to do next and who will do it.
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