06 — Manufacturing & Maintenance

Preventive & Predictive Maintenance

Move from reactive firefighting to maintenance that anticipates failure. We define what to inspect, how often, and which conditions genuinely predict a breakdown — then embed it in the workflows your technicians already use.

Preventive where intervals work, predictive where condition data earns its cost. The distinction matters, and getting it wrong is expensive in both directions.

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Your challenges

What's holding you back right now?

  • Unplanned downtime dominates the calendar and prevention keeps getting postponed.
  • Preventive intervals were set years ago and have never been validated against failure data.
  • Condition monitoring generates data that nobody converts into a work order.
  • Over-maintenance consumes technician hours on assets that were never going to fail.
  • MTBF and MTTR are reported but not actually managed.
When it matters

You'll benefit when you are…

  • Shifting from reactive to planned maintenance on critical assets
  • Deciding where condition monitoring justifies its investment
  • Validating or rebuilding preventive maintenance intervals from failure history
  • Introducing vibration, thermal or current-signature diagnostics
  • Connecting maintenance triggers to EAM work order generation
  • Reducing over-maintenance without increasing breakdown risk
Outcomes

What we deliver

Failure-mode driven plans

Maintenance tasks that map to how each asset class actually fails, not to a generic template.

Validated intervals

Inspection and replacement frequencies derived from your history rather than inherited assumptions.

Predictive where it pays

Condition monitoring deployed on the assets where the economics genuinely work.

Fewer breakdowns

A measurable shift in the reactive-to-planned ratio, tracked through MTBF and MTTR.

Diagnostics people use

Standardised inputs and mobile-friendly workflows that reduce error and get adopted.

Spend maintenance hours where they prevent failure, not where they are habitual.

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What makes us different

Augment Cloud experts at your service

  • Failure-mode analysis tied explicitly to financial impact, not just to engineering elegance.
  • Practical experience of what condition monitoring delivers — and where it does not repay the sensor cost.
  • Diagnostics workflows built as usable applications rather than spreadsheets.
  • Root-cause methods that convert incidents into permanent fixes.

Prediction only matters if it triggers action

A condition signal that does not generate a work order is a cost, not a capability. We connect diagnostics to the EAM workflow so a detected anomaly becomes scheduled work with an owner and a deadline.

We have replaced fragmented diagnostic processes with modern applications that standardise inputs, reduce transcription errors and let technicians collaborate across devices — which is usually where the practical benefit of predictive maintenance is actually realised.

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Case studies

Prediction turned into uptime

Short-term demand forecasting for SKF

Short-term forecasting helped SKF navigate COVID-19 volatility, achieving up to 90% accuracy and enabling faster decisions.

90% forecast accuracy Show full case study →

Spare-parts inventory for ORLEN Unipetrol

Inventory value cut by 25% while safeguarding the availability of mission-critical spare parts.

−25% inventory value · 100% critical availability Show full case study →

Operations & logistics at Volkswagen Slovakia

Production and logistics analysis across one of Volkswagen Group's strategic plants.

Production · Logistics Show full case study →
All case studies →

Which failures are you still finding out about too late?

Send us your downtime history. We will show you which failure modes are predictable, which are not, and where monitoring would repay itself.

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