Case File 03 · Maintenance
Cutting unplanned downtime across distributed assets
Representative — industrial operator
▸ ENGAGEMENT DETAILS
Representative scenario modeled on published industry benchmarks. Not a named client.
The Challenge
A representative industrial operator runs rotating equipment across a dozen sites on a purely time-based maintenance schedule.
Calendar-based maintenance fails in two directions at once: it services healthy machines, and it misses the ones degrading between intervals. The cost of getting it wrong is well documented — Siemens/Senseye put unplanned downtime at roughly $1.4T a year across the Global 500, around 11% of revenue, reaching about $2.3M an hour in automotive.
The sensor data needed to do better already exists. It sits in historians and CMMS records that no one has time to read together, in formats that differ site to site.
Our Agent Solution
We deploy MaintainAI against the operator's existing sensor estate, SCADA/PLC historians, and CMMS. Ingestion agents normalize the streams into one asset model; a prediction agent learns failure signatures from the operator's own historical maintenance and sensor records under a signed data agreement, validated against known past failures; a scheduling agent proposes maintenance windows against production plans, parts availability, and technician skills. Work orders are proposed, not issued — a planner approves every one before it reaches a technician.
Sensor Agent
Ingests and normalizes heterogeneous sensor, historian, and CMMS data into one asset model
Prediction Agent
Identifies failure signatures using pattern matching and anomaly detection, with a confidence score attached
Scheduling Agent
Proposes maintenance windows against production schedules, parts availability, and technician skills
Reporting Agent
Generates health dashboards, alert thresholds, and reliability summaries for the maintenance team
Implementation Timeline
A representative 10 weeks delivery path, from discovery to handover.
Sensor & Data Audit
Mapped sensor types, data formats, historian access, and the CMMS work-order model across sites
Weeks 1-2
Pipeline Design
Designed the real-time ingestion pipeline and built the failure-signature library with the reliability team
Weeks 3-4
Model Development
Tuned and evaluated prediction models on the operator's own historical maintenance and sensor records under a signed data agreement, validated against known past failures
Weeks 5-8
Shadow-Mode Pilot
Ran at two sites in shadow mode, measuring warning lead time and false-alarm rate against the reliability team's own judgement
Weeks 9
Handover & Rollout
Extended to the remaining sites with health dashboards, alert thresholds, and an operator runbook
Weeks 10
Target ranges — and today's baseline
Both sets of figures are drawn from published research, not from VelocityMind client results. Targets are the ranges this scenario is scoped against; baselines describe where the status quo sits. Actual results depend on your data, systems, and scope.
Less unplanned downtime
Source · McKinsey — 30-50% range for condition-based programs
40%
Lower maintenance cost
Source · McKinsey — 10-40% range
25%
Longer equipment life
Source · McKinsey — 20-40% range
30%
Where the baseline sits today
Published measurements of the problem, shown so the target ranges above can be read against something. These are not outcomes we delivered.
Annual cost of unplanned downtime today
Source · Siemens/Senseye, 2024 — Global 500, ~11% of revenue
$1.4T
What ships — and what stays human
The artifacts you own at handover, and the decisions the agents never take.
▸ DELIVERABLES
What you receive
- Prediction and scheduling agents running against your own historians, sensors, and CMMS
- Normalized asset model and failure-signature library built with your reliability team
- Evaluation report covering warning lead time and false-alarm rate against your recorded history
- Alert thresholds, health dashboards, and an operator runbook
- All source code in your repository, with the deployment owned by your team
▸ GUARDRAILS
What stays human
- Work orders are proposals. A planner approves every one before a technician is dispatched.
- The system runs in shadow mode through the pilot so predictions are scored against reality before anything changes.
- Every alert carries a confidence score and the signal that triggered it, so a reliability engineer can overrule it with the evidence in front of them.
- Safety-critical interlocks stay in SCADA/PLC. The agent system reads from the control layer and does not write to it.
Method and sources
This scenario is constructed, not reported. Every figure on this page traces to one of the published sources below.
30-50% less unplanned downtime, 10-40% lower maintenance cost, 20-40% longer equipment life
McKinsey
Uptime +10-20%, planning time -20-50%, maintenance cost -5-10%
Deloitte
Unplanned downtime costs the Global 500 ~$1.4T/yr (~11% of revenue); up to ~$2.3M/hr in automotive
Siemens/Senseye, 2024
Ranges are published industry benchmarks. They describe what programs of this shape have achieved elsewhere, not a commitment for your environment — the range we would scope against for you is set after discovery.
A representative perspective
“We moved from calendar-based to condition-based maintenance, and early-warning signatures have already prevented outages that would have been expensive.”
▸ NEXT STEP
Run this against your own workflow
Send us one workflow, its monthly volume, and the systems it touches. We reply within one business day with a first read on whether an agent system is the right tool, the agent shape we would propose, and an engagement range.
No commitment. We reply within one business day.