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IAS Downtime Intelligence Engine
Why did it happen?
Analyze downtime events, timelines, evidence, contributing factors, and recurring causes.
AvailabilityAvailable for Customer Implementation
The operating challenge
Downtime records rarely tell the whole story.
A stop code or work order is only one part of an event. Downtime Intelligence reconstructs the sequence, separates observation from hypothesis, and helps reliability teams compare recurrence without inventing a cause.
Why the current approach falls short
Downtime reviews are often built from incomplete timestamps, inconsistent event codes, disconnected maintenance records, and conclusions that mix observations with assumptions.
Product characterInvestigative · Analytical · Evidence-led
People and responsibilities
Reliability, maintenance, and operations review
- Reliability engineers
- Operations analysts
- Maintenance managers
- Continuous-improvement teams
Working sequence
Reconstruct the event before judging the cause
- 01Define the event and impact
- 02Assemble a time-ordered record
- 03Attach evidence
- 04Assess causal hypotheses and confidence
- 05Compare recurrence and assign action
Information entering Downtime Intelligence
Events, timestamps, observations, and work records
- Event timestamps and operating states
- Maintenance actions and work records
- Operator observations and supporting documents
- Production impact and comparable prior events
What users receive
Timelines, recurrence patterns, and corrective-action context
- Evidence-linked event timeline
- Separated observations, factors, and hypotheses
- Cause-confidence and recurrence views
- Corrective-action and reliability-review support
Product integrity
Observation, factor, hypothesis, and confirmed cause
The engine is designed to separate observations, contributing factors, hypotheses, and confirmed findings while preserving links to the underlying event records.
Review IAS security responsibilities →Deployment and connections
Align event time, asset identity, and production impact
Event, historian, production, and maintenance data require common time, equipment, and event definitions. The system must preserve uncertainty rather than convert incomplete evidence into a false conclusion.
Review local deployment options →Illustrative workflow — no customer data shown.
Recurring process-interruption review
A recurring process interruption is reconstructed across operator observations, work orders, timestamps, production impact, and similar prior events. The team compares contributing factors and records the confirmed finding separately from earlier hypotheses.
Recommended starting scope
Start with one recurring event family
- 01Select one family of recurring events
- 02Normalize event, asset, and time references
- 03Assemble records for representative cases
- 04Review timelines and causal assessments with reliability staff
Questions from prospective users
Questions reliability teams investigate
Does it declare root cause automatically?
No. It supports investigation and comparison; unsupported root-cause conclusions are not presented as facts.
Can it compare events across sites?
Yes, when event definitions, equipment context, permissions, and data quality make comparison valid.
Who confirms a cause?
Authorized customer personnel remain responsible for confirmed findings and corrective actions.
Talk with IAS
Evaluate recurring downtime with IAS
IAS will review the Downtime Intelligence workflow, information sources, users, infrastructure, and recommended starting scope with your team.