Template

PDCA Experiment Plan & Results Tracker

Interactive PDCA experiment template with structured fields, guidance, and saved responses so teams can run repeatable Plan-Do-Check-Act cycles with clear baselines, success criteria, and documented learning.

Interactive Tool

PDCA Experiment Plan & Results Tracker

Use this form to design, run, and record small, measurable experiments using the Plan-Do-Check-Act cycle. Clear baselines and success criteria help avoid wasted effort. Keep experiments time-boxed and focused on one measurable change.

Quick tips: pick one metric, record an observable baseline, define a target (success criterion), choose a short measurement window, and decide in advance how you'll measure results.

State the specific problem or opportunity this experiment addresses. Explain who is affected and why it matters. Example: '30% of tickets exceed 20 minutes during dinner service, causing complaints and lost covers.'
Write a testable hypothesis linking the proposed change to the expected metric change. Example: 'If we pre-portion sauces for dinner service, then average ticket time will drop by 15%.'
Name the metric you'll measure (e.g., average ticket time, food cost %, plates remade per shift, waste weight).
Describe exactly how the metric is measured and calculated and where the data comes from (POS report, inventory, manual count). Example: 'Average ticket time = time of order to food served, measured per guest, averaged across service.'
Record the current metric value before the experiment (use the same measurement method you'll use during the test).
Note how the baseline was collected (dates, number of shifts, weekday/weekend mix). This helps verify that comparisons are fair.
Define what counts as success before you start. Prefer numeric thresholds or statistical scope if applicable. Example: 'Reduce average ticket time by >= 10% over a 7-day window without increasing remakes.'
Optional numeric target tied to the success criteria.
Where will you get the numbers? (e.g., POS report 'TicketTimeByOrder', manual count sheet, inventory report).
How often will you record the metric during the experiment?
Time-box for the experiment (e.g., '7 days from 2026-09-01 to 2026-09-07' or '10 dinner shifts'). Include dates or number of shifts.
List the concrete steps you'll take during Do. Include who does what, required materials, training notes, and any scripts or checklists staff will use. Use bullet points or numbered steps.
Name the person responsible for executing the experiment and collecting results.
People who should be informed or consulted (shift leaders, chef, GM).
Optional. Use YYYY-MM-DD or a descriptive label like 'Next Monday'.
Optional. Use YYYY-MM-DD or number of shifts/days.
Enter the metric value measured during the experiment using the same method as the baseline. If you measured more than once, record the aggregate (average, median) and attach notes in result_notes.
Describe what happened during the test: contextual factors, unexpected events, staff feedback, data quality issues, or deviations from the plan.
Capture the team's interpretation of results. Identify root causes, constraints, and conditions necessary for success. Be specific about what you learned about the process or people.
Select the team's decision based on the predefined success criteria.
Be explicit: if adopting, list the steps to scale and who owns them; if stopping, note any cleanup actions; if re-testing, describe changes and timing.
Describe any supporting files or reports you will attach in your system (e.g., POS export, photos, spreadsheets). The platform will store this description; attach files via your standard content workflow.
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