Workbook

OEE Top-Loss Diagnosis Workbook

Interactive workbook for frontline teams to intake OEE loss data, rank top availability/performance/quality losses, run 5‑Whys per loss, design small experiments, record success metrics, and capture sustainment steps — saved for team follow-up and continuous learning.

Interactive Tool

OEE Top-Loss Diagnosis Workbook

Purpose

This interactive workbook helps frontline teams quickly identify, quantify, and prioritize the top Availability, Performance, and Quality losses affecting your OEE. Use it to capture the facts, run a lightweight 5‑Whys for each top loss, propose and plan small experiments, and record the success metrics and sustainment steps that keep improvements in place.

How to use

Complete the data intake for the line, shift and date range you are diagnosing.
Enter up to five candidate losses (start with the ones that feel biggest).
Estimate minutes lost and frequency so you can rank by impact; rank losses from 1 (highest priority) to 5 (lowest).
For each top loss, run a short 5‑Whys, capture a hypothesis, and plan a small experiment you can try within a shift or two.
Save the workbook, assign owners, run the experiment, record results, and use the sustainment checklist to lock in the gain.

Tip: If you have a continuous data source (MES/MES/OEE tool), use these fields to record the measured values and references so the team can later verify results.

Plant / Site

Enter plant or site name (e.g., Plant A).

Area / Line / Cell

Where these losses are occurring (line name or number).

Shift

Which shift is being diagnosed.

Date range or diagnosis date

Specify the date range or single date for this analysis (e.g., 2026-08-01 to 2026-08-07).

Primary data source

Where OEE / downtime numbers came from (MES, manual log, supervisor report).

Context / Quick notes

Short context for the team (product mix, recent changes, known issues).

Loss ranking instructions

To prioritize, estimate impact = (minutes lost per event × events per period) or use your best available measure. Enter ranks 1 (highest) to 5 (lowest).

Loss 1 — name or short description

Describe the observed loss (e.g., Machine warm-up delay, sensor faults, scrap at operation X).

Loss 1 — estimated minutes lost per occurrence

Estimate minutes lost each time this loss happens.

Loss 1 — estimated occurrences per week

How often this typically occurs in a week.

Loss 1 — primary category

Choose the main category that best fits the loss.

Loss 1 — priority rank

Assign a rank where 1 = highest priority (use unique ranks where possible).

Loss 1 — observed immediate cause

Briefly describe the immediate cause you observed.

Loss 1 — Why #1

Why did this happen? (first answer)

Loss 1 — Why #2

Ask why again; build on prior answer.

Loss 1 — Why #3

Third why. Keep going until root cause feels actionable.

Loss 1 — Why #4

Fourth why.

Loss 1 — Why #5

Fifth why — capture the likely root cause.

Loss 1 — experiment hypothesis

Short hypothesis: if we [change / fix], then [expected measurable outcome].

Loss 1 — small experiment plan

Plan a small, time-boxed test (who, what, where, how long, data to collect). Keep it low-cost and reversible.

Loss 1 — success metrics

How will you know the experiment worked? (e.g., reduce minutes lost per occurrence from X to Y, reduce frequency from A to B). Include measurement method and sample period.

Loss 1 — owner for experiment

Who will run the experiment and collect results?

Loss 1 — target completion date

Target date for experiment or evaluation (YYYY-MM-DD).

Loss 1 — sustainment actions

Actions to lock in the improvement if the experiment succeeds (training, standard work, poka-yoke, spare parts, schedule change).

Loss 2 — name or short description

(Optional) Enter another loss to analyze.

Loss 2 — estimated minutes lost per occurrence

Loss 2 — estimated occurrences per week

Loss 2 — primary category

Loss 2 — priority rank

Loss 2 — observed immediate cause

Loss 2 — Why #1

Loss 2 — Why #2

Loss 2 — Why #3

Loss 2 — Why #4

Loss 2 — Why #5

Loss 2 — experiment hypothesis

Loss 2 — small experiment plan

Loss 2 — success metrics

Loss 2 — owner for experiment

Loss 2 — target completion date

Loss 2 — sustainment actions

Loss 3 — name or short description

(Optional)

Loss 3 — estimated minutes lost per occurrence

Loss 3 — estimated occurrences per week

Loss 3 — primary category

Loss 3 — priority rank

Loss 3 — observed immediate cause

Loss 3 — Why #1

Loss 3 — Why #2

Loss 3 — Why #3

Loss 3 — Why #4

Loss 3 — Why #5

Loss 3 — experiment hypothesis

Loss 3 — small experiment plan

Loss 3 — success metrics

Loss 3 — owner for experiment

Loss 3 — target completion date

Loss 3 — sustainment actions

Loss 4 — name or short description

(Optional)

Loss 4 — estimated minutes lost per occurrence

Loss 4 — estimated occurrences per week

Loss 4 — primary category

Loss 4 — priority rank

Loss 4 — observed immediate cause

Loss 4 — Why #1

Loss 4 — Why #2

Loss 4 — Why #3

Loss 4 — Why #4

Loss 4 — Why #5

Loss 4 — experiment hypothesis

Loss 4 — small experiment plan

Loss 4 — success metrics

Loss 4 — owner for experiment

Loss 4 — target completion date

Loss 4 — sustainment actions

Loss 5 — name or short description

(Optional)

Loss 5 — estimated minutes lost per occurrence

Loss 5 — estimated occurrences per week

Loss 5 — primary category

Loss 5 — priority rank

Loss 5 — observed immediate cause

Loss 5 — Why #1

Loss 5 — Why #2

Loss 5 — Why #3

Loss 5 — Why #4

Loss 5 — Why #5

Loss 5 — experiment hypothesis

Loss 5 — small experiment plan

Loss 5 — success metrics

Loss 5 — owner for experiment

Loss 5 — target completion date

Loss 5 — sustainment actions

Sustainment checklist — actions to confirm when experiment succeeds

Select actions you will perform if the experiment succeeds.

Measurement plan for the diagnosis

Where and how you will collect data to validate the experiment result (who, what, when, sample size).

Estimated OEE gain if successful

Express as minutes saved, % availability/performance/quality, or other measurable benefit.

Next steps and team commitments

Who will do what next and by when.

Team reflection after experiment

After the experiment, record outcomes, unexpected learnings, and further actions.

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Make this tool part of your work

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OEE Top-Loss Diagnosis Workbook