Research Template

Advanced Scheduling Research Protocol

A practical, fillable research protocol for short, controlled scheduling pilots using heuristics or solvers. Define the constraint model, baseline, experiment conditions, evaluation metrics, risks, and scaling criteria so pilots produce measurable throughput or lead-time improvements and integrate safely with shopfloor operations.

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Advanced Scheduling Research Protocol

Use this structured protocol to design, run, and record short, measurable scheduling pilots that compare heuristics or optimization solvers against a clear baseline. The form helps you capture constraints, sequencing rules, objective functions, data sources, experiment design, evaluation metrics, and scaling criteria so results are reproducible and actionable.

Why this matters: many scheduling pilots fail because they lack a clear baseline, measurable success criteria, or a practical plan to integrate winning approaches with the shopfloor. Keep pilots small and controlled, measure the same metrics before and during the trial, and include integration steps up front.

Suggested primary metrics: throughput (units per time), mean flow time / lead time, on-time % (by due date), average tardiness, schedule adherence, and changeover time. Capture baseline data using the same definitions you will use for the pilot.

Short descriptive name for this pilot (e.g., 'Line A - Sequencing Heuristic Pilot').
Person responsible for execution, communications, and decisions. Include role and contact info if helpful.
Describe the outcome you expect to achieve (e.g., increase throughput, reduce tardiness, improve on-time delivery). Be specific and measurable.
Which resources, product families, shifts, or time windows are included and which are excluded? Keep pilot scope narrow enough to control variables.
List resource types (machines, operators), capacities, setup/changeover constraints, tooling limits, and any maintenance windows to model.
Document current rules used on the floor (e.g., FIFO, EDD, critical ratio). This is the common baseline behavior you'll compare against.
What the heuristic/solver will change (e.g., priority values, batch sizes, sequence windows).
Express the objective clearly (e.g., minimize weighted tardiness + changeover cost). Include units and any weighting choices.
List the metrics you will measure (use the same definitions during the pilot) and record current baseline values or attach how you will capture them.
Length and dates of the baseline period (e.g., 2 weeks, 2026-06-01 to 2026-06-14). Use the same operational cadence during the pilot.
Where will you get timestamps, production counts, setups, and other needed data? Note owners and any access or quality issues.
Choose the safest, most informative approach given risk and shopfloor readiness.
How long or how many runs will you test? Keep pilots large enough to detect change but small enough to mitigate risk.
List the scenarios you will run (parameter sets, random seeds, traffic/load levels). Include control group details if applicable.
Select a method that matches operational constraints to isolate the effects of the scheduling change.
Define the primary metric(s) used to decide success and any secondary metrics you will monitor for unintended consequences. Specify units and calculation formulas if needed.
State the numeric thresholds that indicate success (e.g., >= 8% throughput gain vs baseline with no increase in tardiness). Include statistical or practical significance rules if used.
Identify potential operational, quality, or safety risks and steps to detect and mitigate them during the pilot. Include rollback criteria.
How would a winning approach be integrated into shopfloor systems (MES, dispatching), staff training, and standard work? Estimate effort and timelines.
What conditions must be met to scale (data quality, performance stability, tool readiness)? Outline next steps for pilot-to-production.
List people or roles to notify during the pilot and who must approve rollout. Include operations, maintenance, quality, IT, and leadership as applicable.
How will you monitor performance during the pilot (dashboards, sampling cadence, who watches, alerts)? Include what data will be saved for analysis.
Record important assumptions, open questions, and immediate next actions after saving this protocol.
Helps stakeholders prioritize limited experiment capacity.
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