Predictive maintenance: move from alert to decision
A signal becomes useful when connected to an asset, criticality, history, work capacity and a documented decision.
The decision thread
This dossier follows 5 control points, from “Improve master data” to “Measure the effect”.
Systems to align: OTILA GMAO · IoT / Machines · ERP · Stocks pièces · Mobilité · BI.
Improve master data
Stabilise asset identity, location, hierarchy and history before interpreting signals.
- Which data establishes “Improve master data”?
- Who decides when “Improve master data” reveals a variance?
- Which record confirms the outcome of “Improve master data”?
Qualify signals
Distinguish raw measurement, threshold, drift, confirmed anomaly and false positive with timestamp and source.
- Which data establishes “Qualify signals”?
- Who decides when “Qualify signals” reveals a variance?
- Which record confirms the outcome of “Qualify signals”?
Assess criticality
Combine safety, production, quality, cost and repair-time consequences to prioritise the intervention.
- Which data establishes “Assess criticality”?
- Who decides when “Assess criticality” reveals a variance?
- Which record confirms the outcome of “Assess criticality”?
Prepare work
Verify skills, parts, tools, lockout and machine window before turning an alert into a work order.
- Which data establishes “Prepare work”?
- Who decides when “Prepare work” reveals a variance?
- Which record confirms the outcome of “Prepare work”?
Measure the effect
Compare before/after condition, recurrence, downtime and full cost to determine whether the decision produced the expected effect.
- Which data establishes “Measure the effect”?
- Who decides when “Measure the effect” reveals a variance?
- Which record confirms the outcome of “Measure the effect”?
Associated solutions
Turn these questions into a scoping grid.
Questions, ownership, evidence and comparison criteria for workshops.
- Scoping questions
- Points to watch
- Comparison criteria
Sources & reference framework
The scope must be tested against the definitions, events and responsibilities of each system below.
Maintenance becomes predictive when a signal triggers a prepared, verifiable decision—not when a dashboard shows more alerts.
Frequently asked questions
How should these insights be used?
Each insight structures the discussion. Scope, data and ownership still need to be contextualised with your teams.
How can a topic be connected to my IT landscape?
Each insight structures the discussion. Scope, data and ownership still need to be contextualised with your teams.
Do these resources replace scoping?
Each insight structures the discussion. Scope, data and ownership still need to be contextualised with your teams.
Recommended insights
WMS · INVENTORYInventory optimisation: seven questions to ask before choosing a WMS
A high-performing WMS is more than a feature list. It must align with operational priorities, data, processes and responsibilities.
12 min
TMS · TRANSPORTNext-generation TMS: five levers to reduce cost and emissions
Transport performance starts before departure: demand quality, scenario choice, allocation, evidenced execution and financial reconciliation.
11 min
INDUSTRY · PLANNINGIndustrial planning: align demand, capacity and disruption
Useful planning connects demand, materials, resources and field events without hiding assumptions or trade-offs.
10 min
COMMERCE · OMNICHANNELOmnichannel: align inventory, promise and customer experience
Customer promise depends on interpretable availability, allocation rules and shared status through returns.
8 min