Lab automation pilot planning checklist

Lab automation pilot planning checklist

Use this checklist to scope, run, and evaluate a focused automation pilot that reduces manual error, shortens cycle time, and produces measurable evidence for scaling. For each item, assign an owner, target dates, and acceptance criteria.

1. Clarify pilot objectives and scope
   • State the primary business/hunger the pilot addresses (e.g., reduce pipetting variability in assay X; increase daily throughput of sample prep by 3x).
   • Define success in measurable terms (target % reduction in error, seconds saved per sample, cost per run).
   • Limit scope: which workflow steps, which assays/samples, and which shifts or operators are included.
2. Assemble pilot team & governance
   • Identify: pilot sponsor/champion, lab lead, automation engineer, IT/data steward, QA/regulatory contact, operations rep, vendor/ integrator contact.
   • Create a simple RACI for decisions (who must approve go/no-go, who operates, who captures data).
3. Baseline measurements
   • Capture current cycle time, hands-on time, throughput, error/rework rates, yield, and cost-per-run. Record sample size and measurement window.
   • Use clear definitions (e.g., what counts as an 'error' or 'cycle') and store raw logs or timestamps for traceability.
4. Select automation candidate steps
   • Map the workflow and identify high-value automation targets using criteria: repetitive, high-volume, error-prone, well-defined SOPs, and minimal edge-case branching.
   • Document hardware footprint, consumables, environmental constraints, and required throughput.
5. Technical fit & vendor selection
   • Assess compatibility with existing instruments, LIMS, and lab network (connectivity, protocols, data formats).
   • Evaluate vendor support, maintenance, spare parts, training, and upgrade path. Prefer vendors with reference labs and demonstrable reliability.
6. Integration & data capture requirements
   • Define the minimal dataset to capture during the pilot (timestamps, operator ID, sample IDs, run ID, sensor logs, exception events).
   • Decide where data will be stored, how it will be labeled, and who owns it. Plan for provenance and future analysis (e.g., CSV export, LIMS writeback, structured logs).
7. Safety, compliance & risk assessment
   • Conduct a hazards review and identify mitigations (e-stops, guarding, SOP updates). Plan validation steps needed for regulated work.
   • Document change control and traceability requirements before altering critical workflows.
8. Pilot execution plan & training
   • Define a short timeline: setup, dry runs, controlled pilot runs, data collection period, and analysis window.
   • Train operators and define a simple escalation path for anomalies. Capture operator feedback and pain points as structured notes.
9. Evaluation metrics & go/no-go criteria
   • Set quantitative targets (e.g., reduce error by ≥25%, decrease hands-on time by ≥50%, payback within X months) and qualitative checkpoints (operator acceptance, maintainability).
   • Decide statistical approach and sample sizes needed to make a defensible decision. Define thresholds for go, revise, or stop.
10. Costing, procurement & support
    • Estimate pilot costs (hardware, consumables, integration, vendor setup/time, training). Include ongoing maintenance estimates for scaling decisions.
    • Confirm warranties, SLAs, spare-parts availability, and internal support responsibilities.
11. Post-pilot & scale plan
    • Document recommended next steps for each go/no-go outcome: template for SOP updates, scaling roadmap, integration requirements, and a timeline for rollout.
    • Capture lessons learned, an issues register, and a short operator-facing playbook so knowledge is preserved and reusable.
12. Communication & change management
    • Notify impacted teams, plan go-live communications, and outline how operator feedback will be incorporated.

Quick evaluation checklist (use after pilot)

• Did measured metrics meet predefined targets? (Yes / No / Partial — document rationale)
• Were data capture and provenance adequate for root-cause and reproducibility analysis?
• Did operators and QA trust the results and find the solution maintainable?
• Is there a clear ROI timeframe and funding path for scale?

Tip: record pilot inputs and results in a reusable template so future pilots are faster and comparable. Consider packaging this checklist with a simple interactive Pilot Planner (fields for objectives, baseline metrics, owners, and outcomes) so results are stored and tracked over time.