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PDCA Experiment Log & Learning Record

Interactive experiment log to design, run, measure, and capture learning from iterative PDCA tests. Includes structured fields for hypothesis, measurement plan, results, decisions, and next-cycle adjustments plus an embedded example to help teams start quickly.

Interactive Tool

PDCA Experiment Log & Learning Record

Capture learning from each PDCA cycle

Use this experiment log to make your tests of change clear, measurable, and shareable. Fill the fields below to record a hypothesis, plan how you will measure it, capture results, and decide whether to adopt, adapt, or abandon the change. Saving structured records makes organizational learning repeatable and searchable.

Example completed log (brief)

Experiment Title: Reduce order entry errors by clarifying customer codes
Owner: Priya R.
Hypothesis: If we add a short validation step to confirm customer code format during order entry, then errors will drop by at least 50% within two weeks.
Primary metric: Order entry errors per 1,000 orders (baseline 12 / 1,000)
Plan: Add a format check and 10-minute training for 2 order clerks, run for 10 business days.
Result: Errors dropped to 5 / 1,000 (58% reduction).
Conclusion: Adopt locally and roll out with revised training.
Key learning: Small UI nudges plus short training quickly reduce errors; measure early to confirm.

Give the experiment a concise, descriptive name (what you're testing and where).
Who is accountable for designing, running, and recording results? Include name and role.
Which team, site, or Deck is running this experiment? Useful for search and ownership.
Use YYYY-MM-DD to keep dates searchable. This is the planned or actual start date.
Planned or actual end date for the experiment period.
State the change you will test, the expected effect, and the rationale. Example: 'If we X, then Y will improve because Z.'
Name the metric you will use to judge the experiment (e.g., defects per 1,000, cycle time minutes).
Enter the current value for the primary metric so you can compare results.
State the expected numeric or qualitative change and the success threshold (e.g., reduce errors by 50%).
Describe the sequence of actions, who will do them, and any materials or training needed. Keep steps clear and time-boxed.
Describe data sources, sampling frequency, instruments, and who collects or verifies the measurements.
Summarize what happened and include key measurements or links to raw data. Be factual and include dates and any notable context (absences, outages).
Enter the measured value for the primary metric at the end of the experiment period, if available.
Choose the team decision based on results and confidence.
Capture causal insights, unexpected effects, and practical considerations that explain the results.
If iterating, describe what will change next and why. If adopting, describe immediate next steps for standardization.
Rate your confidence that the results reflect a real effect (1 = low, 5 = high).
1.0 10.0
Reference other experiment IDs, documents, or decks that relate to this test.
Paste URLs to data files, screenshots, dashboards, or SOP drafts.
Add tags such as 'safety', 'quality', 'order-entry', or 'pilot' to help search.
Choose an appropriate visibility setting for this experiment record.
Optional metadata such as experiment ID, version, or keywords for future search.
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