How do I choose between different predictive maintenance tools for a workforce planning team in an electric utility?

For a workforce planning team in an electric utility, the “best” predictive maintenance tool is usually the one that best supports crew forecasting, outage risk reduction, and work prioritization—not just the one with the fanciest AI. A good way to choose is to compare tools against the planning decisions you actually need to make.

1) Start with the planning use cases

Ask: what decisions will the tool help us make?

Common workforce-planning use cases in utilities:

• Where and when will work be needed?
• How many crews do we need by region and skill type?
• Which assets should be inspected or replaced first?
• How will predicted failures affect overtime, storm response, and contractor use?
• What backlog will we inherit if we delay work?

If a tool does not help translate predictions into crew demand, labor hours, skill mix, and schedule impact, it may be more of an engineering tool than a workforce planning tool.

2) Evaluate the quality of the predictions

Compare tools on:

• Asset coverage: poles, transformers, switchgear, lines, substations, meters, vehicles, etc.
• Data inputs required: SCADA, AMI, CMMS/EAM, inspection data, weather, vegetation, outage history
• Prediction type: failure probability, remaining useful life, condition scoring, anomaly detection
• Explainability: can it show why an asset is at risk?
• Accuracy and false alarms: too many false positives can overload crews and planning teams

A tool that produces highly accurate predictions for only one asset class may be less useful than a broader tool that supports your biggest maintenance workload.

3) Check whether it fits your operational systems

For workforce planning, integration matters a lot.

Look for:

• Integration with EAM/CMMS (SAP PM, Maximo, Oracle, etc.)
• Integration with GIS and OMS
• API access / data export
• Ability to map predicted work to work orders
• Support for labor standards, crew types, and geographic constraints

If the tool can’t connect predicted maintenance needs to your work-order and scheduling process, planners will end up doing manual translation.

4) Assess how well it supports labor planning

A strong fit for workforce planning should help with:

• Demand forecasting by craft/skill
• Regional workload balancing
• Seasonality and storm preparedness
• Backlog and capacity planning
• Scenario analysis: “What if we delay replacement by 6 months?” “What if wildfire risk increases?”
• Prioritization based on risk, regulatory requirements, and service reliability

This is especially important in electric utilities where work is constrained by:

• union rules
• certifications
• geography
• safety requirements
• outage windows
• weather dependencies

5) Look at usability for planners, not just data scientists

A tool may be technically strong but still poor for a planning team if it requires heavy analytics support.

Prefer tools with:

• Clear dashboards
• Simple filtering by territory, asset type, and risk
• Scenario tools
• Easy report generation
• Role-based views for planners, operations, and management

If the tool requires a specialized analyst for every question, adoption may be low.

6) Compare implementation effort and total cost

Don’t compare license price alone. Include:

• Data cleanup and integration
• Configuration and model training
• Ongoing model maintenance
• Training for users
• Support and vendor services
• Change management
• Time to first value

A cheaper tool that takes a year to implement may be worse than a more expensive one that delivers usable planning insights in 3 months.

7) Make sure it supports compliance and risk management

Utilities often need to defend maintenance decisions to regulators, auditors, and leadership.

Look for:

• Audit trails
• Decision logs
• Traceable risk scores
• Support for reliability metrics
• Documentation of assumptions
• Security and role-based access

8) Use a scorecard to compare vendors

A simple weighted scorecard helps. Example categories:

• Prediction quality: 25%
• Workforce planning fit: 25%
• Integration with current systems: 20%
• Usability: 10%
• Implementation effort: 10%
• Compliance/security: 5%
• Total cost of ownership: 5%

Adjust weights based on your priorities.

9) Run a pilot with a real planning problem

Before buying, test the tool on a narrow but meaningful case, such as:

• one region
• one asset class
• one season
• one maintenance program

Measure:

• reduction in unplanned work
• forecast accuracy for crew demand
• backlog reduction
• overtime impact
• planner time saved
• number of false positives/false negatives

10) A practical rule of thumb

Choose the tool that best answers:

“Can this tool help us plan the right people, in the right place, at the right time, using maintenance risk data we trust?”

If not, it may be better suited for reliability engineering than workforce planning.

If you want, I can also give you:

1. a vendor comparison checklist,
2. a weighted scoring template, or
3. a shortlist of questions to ask in demos.