Manufacturing · 12 months

AI-powered process automation for a manufacturer

A full AI programme across production, from bottleneck analysis to predictive maintenance and MLOps.

Increase in operational efficiency
40%Increase in operational efficiency
Reduction in production costs
30%Reduction in production costs
Predictive maintenance accuracy
95%Predictive maintenance accuracy

The problem

Production processes had grown by accretion, with bottlenecks nobody had measured and maintenance scheduled by calendar rather than condition. Equipment failures were expensive and largely unpredicted.

What I did

Mapped the processes against actual throughput data, built AI tooling for the constraints that were genuinely binding, and put predictive maintenance in place where downtime justified it — then handed over the MLOps to keep it running.

How it ran

  1. 01

    Assessment and planning

    Analysed current manufacturing processes, identified bottlenecks, and developed the transformation roadmap.

  2. 02

    Custom tooling

    Built specialised AI tools for process optimisation and predictive maintenance.

  3. 03

    Predictive analytics

    Deployed prediction models for equipment maintenance and quality control.

  4. 04

    Process automation

    Automated key manufacturing decisions where the data supported doing so.

  5. 05

    MLOps and handover

    Established MLOps infrastructure and the continuous optimisation process the team now runs.

Something similar on your plate?

Thirty minutes, no deck. I will tell you whether it is worth doing at all.