Template

Research Project Brief & Success Criteria

Interactive template for launching and tracking emerging-technology or menu research projects (robotics pilot, new protein test, forecasting model). Guides teams to state hypotheses, collect the right data, define success metrics, de-risk live operations, and record go/no-go criteria.

Interactive Tool

Research Project Brief & Success Criteria

Use this brief to design, run, and evaluate a pilot or research project with clear hypotheses, measurable success criteria, and required de-risking steps. Fill each field with specific, actionable details. Prefer numeric baselines and targets where possible so the decision at the end is objective.

Concise name (e.g., 'Robotic Fryer Pilot - Line 2').
Name and role of the person who will make the go/no-go decision.
List names and responsibilities (operations, chef, safety, data, supplier).
Why this project matters. Keep to 1–3 short paragraphs.
State testable hypotheses (e.g., 'Robotic fryer will reduce labor minutes per ticket by 30% without increasing remakes').
For each metric include current baseline and numeric target (e.g., 'Labor minutes per ticket — baseline 6.2, target ≤4.5').
Summarize the data window used for baseline (dates, sample size) and key baseline values.
Be specific: data element, source (POS, timeclock, sensors), frequency (per shift, daily), and responsible data owner.
Where and when the pilot will run; which menu items, shifts, stations, or customer segments are included.
Choose the environment that balances learning and risk.
Number of shifts, days, orders, or locations (e.g., '2 locations for 4 weeks' or '80 orders').
How many calendar days the pilot will run.
Estimate labor hours, equipment, supplies, and a simple USD budget.
List specific risks and relevant regulations (temperature control, allergens, mechanical hazards) and how they will be mitigated.
Check every item before launch. Add local items in 'required_approvals' or 'notes_and_attachments'.
List milestone dates: preparation, launch, mid-point review, data collection end, final decision.
Specify explicit pass/fail thresholds tied to metrics and acceptable negative impacts (e.g., 'If order accuracy drops >2 percentage points or guest satisfaction drops ≥5 points, stop').
Who will sign off and the target decision date.
How results will be analyzed (statistical methods, comparison groups, handling variability, significance thresholds).
How staff and stakeholders will be informed about changes, alerts, and results, and where results will be stored.
List operational, safety, legal, supplier, or corporate approvals needed before launch.
Provide links or references to supporting documents stored in your shared folder or project workspace. Use the project record to attach files externally.
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