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Predictive Maintenance Use-case Evaluation Canvas

An interactive canvas to evaluate predictive maintenance opportunities. Guides teams through asset selection, failure-mode definition, data readiness, labeling approach, modeling and validation, operational integration, pilot success criteria, and a basic ROI estimate. Saves responses so pilots can be compared and tracked.

Interactive Tool

Predictive Maintenance Use-case Evaluation Canvas

Use this guided canvas to decide whether a predictive maintenance pilot is feasible and valuable. Capture asset details, failure definitions, available signals, labeling strategy, modeling and validation approach, operational integration and human oversight, pilot success criteria, risks, and a simple ROI estimate. Save responses so teams can compare, prioritize, and iterate on pilots.

Identify the specific asset, equipment class, or system. Include location, model, and any identifiers.
Describe what fails and how it manifests. Give short examples of past incidents and typical failure signatures.
Explain how this failure affects operations: average downtime per event, safety implications, repair cost, lost production, customer impact. Include numeric targets if known.
Select signals that are already collected or accessible from systems. Use 'Other' to describe additional sources.
List additional signals, external feeds, or notes about access, retention, sampling rate, and ownership.
Assess the historical data quality for the selected signals (coverage, gaps, sampling rate, labels).
How will you define training labels or events? Indicate how many labeled events you expect to have and the source of truth.
Approximate count of usable events for training/validation. Enter 0 if unknown or none.
Consider explainability, data needs, lead time, and maintenance burden when choosing an approach.
Describe test/validation strategy (holdout periods, cross-validation), target metrics (precision, recall, lead time), acceptable false positive rate, and any field trials.
Explain how alerts will be delivered, who takes action, how the prediction ties into work planning/scheduling, escalation, and model ownership. Include any required UI or notification channels.
List objective acceptance criteria for the pilot (e.g., precision >= 0.6 at X-hour lead time, reduction in emergency repairs by Y%, tech time saved). Be specific and measurable.
If the model works, how many hours per year do you expect to avoid? Use conservative estimates.
Fully loaded cost per hour of downtime including lost production, labor, and penalties.
Estimate downtime_hours * cost_per_hour. Enter your calculated estimate; the platform can later compute this automatically if connected to site data.
High-level estimate of time and engineering needed for a pilot (data integration, sensors, labeling, model dev).
How confident is the team that this use case is feasible and valuable?
1.0 10.0
List risks such as false positives, false negatives, data drift, overfitting, operational disruption, and proposed mitigations or guardrails.
Concrete next actions to launch a pilot: data extraction, labeling tasks, sensor upgrades, stakeholder sign-offs, pilot timeline, and owners.
Paste URLs to asset masters, sensor specs, historical logs, diagrams, dashboards, or work orders.
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