Template

Design of Experiments (DOE) Project Template & Experiment Tracker

A project-level DOE template that guides small-factor experiments from charter to conclusions. Collects the experiment charter, factors and levels, randomization checklist, sample-size inputs, measurement and analysis plans, result capture fields, visualization checks, and next steps. Saves structured project data for reuse and reporting.

Interactive Tool

DOE Project Template & Experiment Tracker

Use this template to plan, run, and record a small-factor Design of Experiments (DOE). The form guides you through a clear experiment charter, factor and level definitions, randomization checks, measurement and analysis plans, result capture, visualization checklist, and practical next steps. Save one record per DOE project so teams can compare experiments, track decisions, and avoid common design mistakes.

Helpful hints: keep the charter specific, record assumptions, state the primary response metric, and document how you'll randomize and measure. If you need a sample size calculation or automated randomization lists, consider the capability enhancements noted at the end of this item.

Short descriptive title: what you're changing and why.
Who is accountable for this DOE?
YYYY-MM-DD or project milestone.
Purpose, business context, outcome(s) of interest, and how results will be used.
The single metric you'll use to decide success (e.g., yield %, cycle time, defect rate).
Recent historical value of the primary metric (units).
Smallest meaningful change you want to detect (same units or percent); used for sample-size thinking.
Estimate of SD for response (same units).
Common choices: 0.05 or 0.01.
Typical: 0.8 or 0.9.
If known, enter it here. Otherwise compute with your sample-size tool and record result.
List each factor and its levels. Example: Temperature: Low (180°C), High (200°C) Catalyst: A, B Operator: 1,2. Use commas or new lines.
Choose an appropriate small-factor design.
Describe blocks or strata and why they are used.
Describe how experimental runs will be randomized, any blocking, and the planned run order or paste the random sequence here.
Which instruments, calibration, who measures, how often, data formats, QC checks.
Place to find raw data, scripts, or datasets (URL or repo path).
Tests or models to use (e.g., ANOVA, regression), transformations, outlier rules, model terms, interaction checks.
Record summary stats: group means, SDs, n, test statistic, p-value, effect size, 95% CI. Example table format: Condition | n | mean | sd | p vs baseline | effect size.
What do the results suggest about causal relationships and improvement levers? Note limitations and threats to validity.
Decisions to make, process changes, follow-up experiments, or scale-up recommendations.
Who will do what and by when if recommended changes are adopted.
URLs, repository paths, or document names.
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