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Director of Generation

Fleet performance review

Enhances✓ Available Now

What You Do Today

Review overnight generation reports across all plants — capacity factors, forced outage rates, heat rates, and emissions. Prioritize any unit that tripped or derated overnight for root-cause investigation.

AI That Applies

AI aggregates SCADA, historian, and CMMS data across the fleet into a single dashboard, flagging anomalies against rolling 90-day baselines and ranking units by economic dispatch impact.

Technologies

How It Works

For fleet performance review, the system draws on the relevant operational data and applies the appropriate analytical models. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

Manual morning report compilation from multiple plant control rooms becomes an auto-generated fleet scorecard with anomaly flags.

What Stays

Deciding whether to curtail a unit, call in maintenance crews, or accept reduced output based on market conditions and reliability obligations.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for fleet performance review, understand your current state.

Map your current process: Document how fleet performance review works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Deciding whether to curtail a unit, call in maintenance crews, or accept reduced output based on market conditions and reliability obligations. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support PI System tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long fleet performance review takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your VP Operations or COO

What data do we already have that could improve how we handle fleet performance review?

They're prioritizing which operational processes to automate

your process improvement or lean lead

Who on our team has the deepest experience with fleet performance review, and what tools are they already using?

They understand the workflow dependencies that AI tools need to respect

a frontline supervisor

If we brought in AI tools for fleet performance review, what would we measure before and after to know it actually helped?

They see the daily reality that AI tools need to fit into

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.