A bounded prediction experiment with a decision, dataset, baseline, review owner, and acceptance criteria agreed before model work expands.
Name the outcome and the decision window
Define the failure, degradation, demand, or quality outcome the team wants to anticipate. Record how much warning is useful and what action becomes possible inside that window.
- Outcome to predict and how it is confirmed.
- Useful warning horizon.
- Person or workflow that receives the prediction.
- Safe action available when the prediction is raised.
- Cost of a false alarm and a missed event.
Check whether the history can answer the question
Historical volume alone is not enough. Confirm that timestamps, operating context, maintenance records, labels, and representative failure examples are available and can be joined to the correct asset identity.
- 01Inventory candidate variables and their missing-data periods.
- 02Confirm how outcomes and maintenance actions were recorded.
- 03Separate time periods or assets for training and evaluation.
- 04Define a simple rule or current process as the comparison baseline.
Keep the first model advisory
Run predictions alongside the existing process before allowing automated action. Review false positives, missed events, drift, and the operational response with domain experts.
Completion check: the team can state whether the prediction improved the decision compared with the baseline, and which risks remain before operational use.