Predictive maintenance
Models on vibration, temperature and current data that forecast failures days ahead and raise work orders.
Manufacturing & Supply Chain
AI for factories, plants and supply chains.
We connect machines, systems and people so plants run with fewer surprises: predicting failures before they happen, catching defects on the line and keeping inventory lean.
OEE0%
Sensors live0
Downtime--
The challenge
What is holding manufacturers back.
01
Unplanned downtime
A single failed motor or pump can stop a line, and most maintenance is still calendar-based or reactive.
02
Quality escapes
Manual inspection misses defects on fast lines, leading to scrap, rework and customer returns.
03
Working capital tied up in stock
Safety stock is set by habit, not by data, across thousands of parts and SKUs.
04
OT and IT do not talk
Sensor data sits in historians and PLCs, separate from ERP and planning systems.
Where AI pays off
Use cases we build and run.
Each one is scoped to a measurable business outcome and shipped to production, not left as a proof of concept.
Vision quality inspection
Camera-based defect detection that runs on the edge at line speed.
Inventory optimisation
Multi-echelon safety stock and reorder policies that free up cash without risking service.
Demand and S&OP forecasting
Forecasts that blend orders, distributor data and market signals for planning cycles.
Maintenance and SOP copilot
Technicians ask questions of manuals, SOPs and past work orders on a tablet at the machine.
Supplier risk monitoring
Agents that watch lead times, quality and news to flag supply risks early.
Our approach
How we deliver in Manufacturing & Supply Chain.
The same disciplined loop we use everywhere, adapted to the data, risks and rhythms of your industry.
Built responsibly
- IEC 62443 aligned OT network segmentation
- Plant safety and change-management procedures
- ISO 9001 quality documentation
- Data residency for plant data
Typical stack
01
Weeks 1 to 2
Plant-floor discovery
We walk the line with operators and maintenance before touching data.
- Loss tree analysis to find where OEE is leaking
- OT and IT data audit: sensors, historians, MES and ERP
- Use cases ranked by downtime, scrap and working-capital impact
02
Weeks 2 to 3
Edge-to-cloud design
Architecture that respects plant networks and safety rules.
- Data pipeline from PLCs and historians to a lakehouse
- Edge versus cloud inference decisions per use case
- Success metrics agreed with plant leadership
03
Weeks 3 to 8
Pilot on one line
We prove value on a single line or asset class first.
- Sensor retrofits where data is missing
- Models in shadow mode alongside current practice
- Alerts integrated with CMMS and shift handovers
04
Ongoing
Replicate across plants
A proven template is rolled out plant by plant.
- Reusable data models and deployment templates
- Operator and maintenance team training
- Performance tracking against the original baseline
Case studies
Results from the field.
Case study 01
Auto components manufacturer
Predicting failures before they stop the line
The challenge
Critical CNC spindles and hydraulic units failed without warning, causing costly unplanned downtime several times a month.
What we did
We added vibration and current sensors, streamed data to a lakehouse and deployed failure-prediction models that raise work orders in SAP PM.
Results
27%less unplanned downtime
9 daysaverage warning before failure
6 ptsOEE improvement
Case study 02
Packaging manufacturer
Vision inspection at line speed
The challenge
Print and seal defects slipped past manual inspection, triggering customer complaints and returns.
What we did
Edge cameras with a custom defect model now inspect every unit, reject bad ones automatically and log images for root-cause analysis.
Results
97%defect catch rate
40%reduction in customer complaints
Case study 03
Industrial distributor
Freeing cash from inventory
The challenge
Safety stock for 30,000 SKUs across warehouses was set manually and rarely revisited.
What we did
We built demand forecasts and a multi-echelon inventory optimiser that recommends reorder points weekly inside the ERP.
Results
18%reduction in inventory value
98%service level maintained
Case studies illustrate the kind of engagements we run. Details are generalised and figures are indicative, not guarantees of results.
Other industries
Explore more of our work.
Financial Services
Credit risk, fraud detection, KYC automation and AI-assisted operations.
Retail & D2C
Demand forecasting, personalisation, pricing and catalogue intelligence.
Healthcare & Life Sciences
Clinical documentation, claims processing and patient-journey analytics.
SaaS & Technology
AI features inside your product, usage analytics and support automation.
HR & Talent
Screening, matching, workforce planning and people analytics at scale.
Let's talk about Manufacturing & Supply Chain.
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