Manufacturing
Industry 4.0 that survives contact with the shop floor.
MES, IoT, and supply chain systems for manufacturers — built with the operators who will use them, on equipment that was installed long before anyone said 'digital twin'.
45+
Plants connected
23%
OEE improvement
60%
Less unplanned stop time
12 wks
To first live line
The pressure
What manufacturing teams are actually dealing with
Equipment older than the protocols
A line with machines from four decades and six vendors, half of which expose nothing more modern than a serial port.
Planning divorced from the shop floor
An ERP schedule built on standard times that the line has not achieved since the last product change.
Quality issues found too late
Defects detected at final inspection rather than at the station that caused them, by which point a shift of output is affected.
Dashboards nobody on the floor uses
Analytics built for head office that tell a supervisor what happened last week rather than what to do in the next ten minutes.
What we build
What we build for manufacturers
Systems that earn their place on the floor by making the next decision easier, not by producing a better monthly report.
Manufacturing execution
Work order execution, traceability, and dispatch that reflects how the line actually runs, including the workarounds.
- Genealogy tracking
- Electronic work instructions
- Shop-floor scheduling
Industrial IoT & connectivity
Edge gateways bridging OPC UA, Modbus, and proprietary protocols into a modern data layer without touching the control system.
- Protocol translation
- Edge buffering
- OT/IT segmentation
OEE & performance
Availability, performance, and quality measured automatically from machine signals rather than from a clipboard at the end of a shift.
- Automatic downtime capture
- Loss-reason coding
- Live line dashboards
Predictive maintenance
Condition monitoring and failure prediction on the assets where unplanned downtime is genuinely expensive, not on all of them.
- Vibration and thermal analysis
- Failure-mode models
- CMMS integration
Supply chain visibility
Inbound, inventory, and outbound tracking connected to production so a materials shortage surfaces before the line stops.
- Supplier integration
- Inventory accuracy
- Shortage forecasting
Quality management
In-process inspection, SPC, and non-conformance workflows that catch drift at the station rather than at final inspection.
- Statistical process control
- In-line inspection
- CAPA workflow
Compliance
The regulatory surface we build against
Controls are designed into the architecture and evidenced in the pipeline, so an audit is a report rather than a project.
Outcomes
What these engagements produced
Representative results from engagements in this sector.
- 01
Automotive supplier
OEE up 23 points across three lines
Automatic downtime capture replaced manual logging, revealing that a third of recorded 'changeover' time was actually material starvation.
- 02
Food producer
Full batch traceability in under a minute
Genealogy tracking across raw materials, process, and packaging reduced a recall trace from two days of paperwork to a single query.
- 03
Heavy equipment
Unplanned stoppages down 60%
Condition monitoring on eleven critical assets, with alerts routed into the existing maintenance system rather than a new dashboard.
How we deliver here
Three things that make manufacturing delivery different
The shop floor
The plant does not stop for your deployment
Change windows are scarce, short and scheduled around production, not around sprints. Anything that touches the line is designed to fail safe, deploy inside the window, and roll back without a maintenance stop.
- Change windows agreed with production planning up front
- Fail-safe defaults so a software fault does not stop the line
- Local buffering when the network or cloud is unavailable
- Rollback rehearsed against a mirrored line first
Ground truth
Automatic capture beats the clipboard every time
Manual downtime logging is where OEE numbers go to become fiction. Instrumenting the line usually reveals that a large share of recorded changeover time was something else entirely — most often material starvation.
- Downtime captured from the machine, not from a form
- Reason codes reconciled against operator input
- Baseline held long enough to cover a full product mix
- Findings validated with the line supervisor before reporting
IT and OT
A boundary that is designed, not assumed
Connecting operational technology to enterprise IT is where most manufacturing security incidents originate. We treat the boundary as a first-class design artefact with an explicit data direction and an assured remote-access path.
- Segmentation aligned to IEC 62443 zones and conduits
- Data flows outward by default, control paths tightly scoped
- Vendor remote access brokered and recorded
- Legacy protocols terminated at a gateway, not routed
Where to start
Four starting points, ranked by how fast they pay
We almost always recommend the top row. Instrumenting one line proves the numbers before anyone commits to a plant-wide programme.
| Starting point | Time to evidence | Disruption | What it proves |
|---|---|---|---|
| Instrument one lineRecommended | 8–12 weeks | Minimal — read-only telemetry | Whether your OEE numbers reflect reality |
| Digitise changeover | One quarter | Low — process change on one line | Whether the constraint is method or material |
| MES rollout | Two to four quarters | Medium — operator retraining | Repeatability of the pattern across lines |
| Plant-wide IIoT | A year or more | High — network and OT change | Only worth starting once the above are proven |
Instrument one line
Recommended- Time to evidence
- 8–12 weeks
- Disruption
- Minimal — read-only telemetry
- What it proves
- Whether your OEE numbers reflect reality
Digitise changeover
- Time to evidence
- One quarter
- Disruption
- Low — process change on one line
- What it proves
- Whether the constraint is method or material
MES rollout
- Time to evidence
- Two to four quarters
- Disruption
- Medium — operator retraining
- What it proves
- Repeatability of the pattern across lines
Plant-wide IIoT
- Time to evidence
- A year or more
- Disruption
- High — network and OT change
- What it proves
- Only worth starting once the above are proven
Questions
What manufacturing clients ask first
Almost always about downtime risk and about the OT boundary.
Operations and safety
What happens if your software fails mid-shift?
The line keeps running. Anything we deploy near production fails safe and degrades to local operation — telemetry buffers locally and back-fills when connectivity returns, and no control decision depends on a cloud round trip. We test this by disconnecting it deliberately before go-live.
Will you need to touch our PLCs?
Usually not. Most of the value comes from reading existing signals rather than changing control logic, and we default to read-only integration at a gateway. Where a control change is genuinely required it goes through your own OT change process with your automation vendor involved, not around them.
Our OEE already looks fine. Why instrument it?
That is often the most interesting case. Where downtime is logged manually, the recorded reason and the actual cause frequently diverge — on one engagement a third of recorded changeover time turned out to be material starvation. If instrumentation confirms your numbers, that is a useful and cheap thing to know.
Working in a different sector?
The engineering practice is the same across every vertical we serve. Browse the full list, or tell us what you are dealing with.
All industriesStart on one line, not the whole plant.
A twelve-week engagement that instruments a single line end to end and produces measurable results before anything scales.