Cutting unplanned downtime across distributed assets

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.

Setting

Representative — industrial operator

System concept

MaintainAI

Illustrative duration

10 weeks

The architecture

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.

A proposed delivery path

This sequence illustrates the engagement. The actual scope, timeline and quotation are agreed around your requirements.

Benchmark context

Published figures used to frame this scenario. Baselines describe the referenced setting; targets are illustrative, not measured project outcomes.

Deliverables & controls

What the scope can include

• 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

Where people stay in control

• 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.

Sources behind the scenario

1. 30-50% less unplanned downtime, 10-40% lower maintenance cost, 20-40% longer equipment life
   McKinsey
2. Uptime +10-20%, planning time -20-50%, maintenance cost -5-10%
   Deloitte
3. Unplanned downtime costs the Global 500 ~$1.4T/yr (~11% of revenue); up to ~$2.3M/hr in automotive
   Siemens/Senseye, 2024