/case-studies / manufacturing-predictive-maintenance
DATA & PREDICTIVE AI

Predictive maintenance cuts unplanned downtime for a manufacturer

client
Industrial Manufacturer
industry
Manufacturing
services
Data Foundations · AI / ML
duration
8 months
fig.80// skillikzmodeltraininfervectorAImodel.evalrollout84%98%accuracyusage82coveragelive
// OVERVIEW

Industrial Manufacturer — a manufacturing organisation — engaged Skillikz on predictive maintenance: Sensor-driven models that predict failures before they happen. This case study sets out the business challenge, the AI-led approach we took, the technologies involved and the measurable outcomes delivered over 8 months.

// TECHNOLOGIES
PythonPyTorch / scikit-learnMLflowSparkAWS SageMaker
-35%
unplanned downtime
asset life
real-time
monitoring
ROI
in < 1 year
01 // THE CHALLENGE

Unplanned equipment failures halted production lines, and time-based maintenance was both wasteful and unreliable.

02 // OUR APPROACH

We built AI-ready data pipelines from machine sensors and predictive models that flag failures early, prioritised by impact.

Streaming pipelines from machine sensors
Predictive models for failure and remaining life
Prioritised alerts to maintenance teams
Dashboards and continuous model evaluation
03 // THE RESULTS

Unplanned downtime fell 35%, assets last longer, and maintenance shifted from reactive to predictive — paying back within the year.

We stopped firefighting breakdowns and started preventing them.

Plant Director · Industrial Manufacturer
// HOW WE'D DELIVER THIS TODAY

The AI services behind this outcome

A project like this draws on a focused set of Skillikz services — from first assessment to a working pilot and a clear path to scale.

// MORE WORK
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