Availability Loss Rapid-Experiment Pack

A short playbook of five proven, low-effort experiments to reduce common availability losses — plus an interactive experiment recorder teams can use to plan, run, measure, and sustain each experiment.

Interactive Tool

Availability Loss Rapid-Experiment Pack — Experiment Recorder

Run short, focused experiments to improve availability

This pack collects five proven, low-effort experiments teams can run in a shift or a day to chip away at the biggest availability losses. Use the interactive form below to plan each experiment, record baseline and result data, capture failure modes and lessons, and create a short sustainment checklist so small wins stick.

Suggested quick experiments (examples)

  • Quick motor-start check — confirm starter wiring, interlocks, and clearances; reduce startup failures.
  • Standardized daily checks — a 5-minute checklist for critical equipment to catch imminent failures before they cause downtime.
  • Changeover containment — temporary guards, jigs, or simple visual cues to prevent repeated changeover mistakes for a shift.
  • Startup splice of standard work — add a brief startup step to operator standard work that ensures critical items are set before running.
  • Fast parts staging — pre-stage next-job parts and tooling at the point of use to reduce startup time between runs.

Each experiment below captures objective, steps, materials, what to measure, expected lift, likely failure modes, sustainment actions, and a place to record results and lessons learned.

Give this run a short descriptive name (e.g., 'Quick motor-start check - Line 2 AM').
Date of the experiment (YYYY-MM-DD).
Who is responsible for running and following up on the experiment.
What specific availability loss are you targeting and why this experiment should help.
Step-by-step actions the team will take during the experiment.
List items the team needs (tape, temporary guards, labels, spare parts, stopwatch, etc.).
What you will measure (e.g., downtime minutes, fail count, time-to-start) and how often.
Numeric baseline (minutes of downtime, number of stoppages, seconds to start, etc.) before the experiment.
Estimated percent improvement (helpful for prioritizing experiments).
Short explanation of where the improvement will come from (e.g., fewer startups lost due to mis-adjustment).
What could make the experiment fail or cause unintended consequences?
Clear, measurable criteria for deciding whether the experiment succeeded.
If the experiment works, what immediate small steps make it repeatable and lasting? (who does what, when).
After running, summarize measured results vs baseline and note any surprises.
What the team learned about the process, people, materials, or data collection.
Track the state of this experiment run.
Suggested agenda items or owners for the follow-up review.
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