Featured/16 July 2026
When the factory forgets its own patient file: the industrial memory crisis
When the factory loses its maintenance lessons: how the industrial memory crisis paralyses your operations, and how to structure it.
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How do you support a maintenance technician working alone at night?
On a three-shift rota, the maintenance technician faces the breakdown alone, with no expert at night. How can you structure the diagnosis and capitalise on every intervention?
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The real cost of unplanned downtime in industry
Beyond the part and the labour, what unplanned downtime truly costs, and how a faster diagnosis changes the picture.
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Integrating CMMS / ERP into an existing information system
Integrate a CMMS or ERP into an existing information system without a full rebuild: the lightweight approach that reads your systems in place, plus IT prerequisites and steps.
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ERP or CMMS for maintenance: differences, complementarity and coupling
ERP or CMMS for maintenance? The differences, how the two tools complement each other, coupling them, and making the most of what you already have.
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Condition-based maintenance: intervene at the right moment, neither too early nor too late
Vibration, oil, temperature, cycles: which signals to monitor to intervene at the right moment, and when condition-based maintenance is worth the effort.
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Voice instead of typing: how AI simplifies the maintenance intervention report
How AI automatically generates your intervention reports from voice dictation. Single entry, accessible diagrams, captured know-how.
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Preventive, condition-based, predictive maintenance: what are the differences, and which to choose?
Corrective, preventive, condition-based and predictive: the full picture of maintenance strategies, their limits, and how to choose.
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The 5 mistakes that kill an industrial AI project
A large share of industrial AI projects fall short of their goal. The 5 mistakes that kill them, and how to avoid them, on method and on the ground.
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Symptom or root cause: why your technicians often treat the wrong problem
Find out why treating the symptom without identifying the root cause is costly for your maintenance. Complete guide and concrete cases.
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Relational graph of an electrical diagram: the map that links the whole machine
A relational graph turns your electrical, pneumatic and hydraulic diagrams into a navigable map of components, for safer diagnosis.
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Digital twin without sensors: functional versus predictive maintenance
A functional digital twin is built without sensors, from your diagrams. What sets it apart from sensor-based predictive maintenance, point by point.
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How does AI model your industrial equipment from electrical diagrams?
AI modelling of industrial equipment turns your electrical diagrams into a functional digital twin, for a structured diagnosis.
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Three world visions of AI: innovation, control and trust
United States, China, Europe: three visions of AI shaping regulation and industrial adoption. A briefing for decision-makers.
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Trust and control: the real levers of ROI in industrial AI
Trust, control, knowledge capture: the three real levers of ROI for an industrial maintenance AI, beyond the promises of algorithms.
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Functional digital twin: modelling your equipment for maintenance, without sensors
The functional digital twin models your equipment as a navigable graph (components, diagrams, failures) for guided diagnosis, with no sensors and no 3D.
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What if your machines developed an immune system against failures?
How a factory develops an immune system against failures: capturing every resolved incident to diagnose faster and anticipate.
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Field technicians, the real AI experts
Your field technicians are the real AI experts. How to capture their tacit knowledge and build a collective intelligence.
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AI-guided diagnosis in maintenance: the complete guide
Understanding AI-guided diagnosis in industrial maintenance: principles, how it differs from attached procedures, FMEA, MTTR. Complete 2026 guide.
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AI Act May 2026: impact on industrial maintenance
AI Act agreement of 7 May 2026: industrial AI leaves the dual regime but stays under the Machinery Regulation. What changes, what remains mandatory.
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ChatGPT vs orchestrated AI in industrial maintenance
ChatGPT in industrial maintenance: useful for Q&A, limited for diagnosis. Comparison with an orchestrated multi-agent AI built for the plant.
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EU AI Act and industrial maintenance: 2026-2028 compliance guide
The 7 May 2026 Omnibus agreement, the 2026-2028 timeline and its link with the Machinery Regulation. Decoded for industrial maintenance, without alarmism.
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Loss of know-how in industrial maintenance
The shortage of technical talent is accelerating as experts retire. How to structure your experts' know-how before it disappears.
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Knowledge capture in industrial maintenance: the complete guide
Your experts are retiring and taking their know-how with them. Methods to capture field know-how in maintenance before it disappears.
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Trustworthy AI for industrial maintenance: the complete guide
What is trustworthy AI for critical operations in industry? The 6 pillars, the AI Act, data quality and the concrete criteria to look for.
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Trustworthy AI is not perfect AI
Trustworthy AI is not perfect AI. Discover why transparency and human supervision are the keys to adoption in maintenance.
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No trustworthy AI in industrial maintenance without reliable data
Without reliable data, no trustworthy AI in industrial maintenance. Customer case: diagnosis cut from 3 hours to 15 minutes with Mimorian.
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The six pillars of trustworthy AI
Robustness, transparency, data governance: discover the 6 pillars of trustworthy industrial AI according to the European Commission and the AI Act.
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Make the most of the field know-how MES forget
MES run production but overlook field knowledge. See how to capture technicians' expertise with Mimorian.
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Industrialising collective memory: when the field reclaims its rightful place
Field knowledge is lost with every departure. See how to industrialise collective memory in maintenance with industrial intelligence.
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Recurring failures: the invisible cost of uncaptured know-how
70% of industrial failures recur for want of knowledge capture. Discover how to structure field feedback with Mimorian.
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A century of industrial AI: from symbolic AI to deep learning
From Dartmouth 1956 to the Transformers: retrace a century of AI at speed and understand why it is transforming industrial maintenance today.
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