Industries
Manufacturing
From shop-floor automation to inspection rounds that end in work orders. The data usually exists — on the machines, in a format nobody has needed to read until now. Most of the value is in connecting it to a decision someone actually makes.
What I do here
- Industry 4.0 programmes
- Connecting shop-floor data to planning, sequenced so each stage pays for the next instead of arriving as one large bet.
- Supply chain optimisation
- Forecasting and scheduling improvements aimed at the constraint that is actually binding, not the one that is easiest to model.
- Predictive maintenance
- Failure prediction where the cost of downtime justifies it — and an honest answer where it does not.
What changes
- Unplanned downtime reduced on the lines where it costs most
- Planning based on current data rather than last month’s
- Quality issues caught earlier in the process
- Operators who trust the system enough to use it
Work in this industry
6 written up — all case studies →- Engineering services
An engineering firm managing utilisation instead of flow
18 interventionsEach traced to a desired effect, not a wish list
- Manufacturing
AI-powered process automation for a manufacturer
40%Increase in operational efficiency
- Manufacturing
Three AI pilots aimed at one line of the balance sheet
3 pilotsRanked by balance-sheet impact, not by technical interest
- Aviation
One device, three roles, one signature
3 rolesAgent on a phone, crew on a tablet, office on a desktop
- Industrial operations
A command centre demo that ends in a work order
One token swapThe whole application recoloured to the official brand palette
- Food manufacturing
Clipboard rounds into work orders
2 audiencesThe technician in the plant and the executive in the office, in one prototype
Other industries
Working in manufacturing?
Thirty minutes, no deck. Tell me what is stuck and I will tell you whether it is the kind of problem I can move.