Operations technology
Operations technology is the equipment side: controllers, SCADA, sensors, and plant systems that were never meant to talk to the software the office runs on. We connect the two, so what happens on the floor reaches the people who need it without anyone retyping it off a screen.
What the work looks like
Most of it is plumbing: taking a number that already exists somewhere on the floor and getting it to a place where somebody can act on it.
Industrial automation
Automating the steps still done by hand between machines — the clipboard, the whiteboard, the person walking a number from one screen to another.
SCADA systems
Supervisory control and data acquisition: screens showing what the line is doing now, alarms when it stops, and history you can go back through when somebody asks why.
Process analysis
We instrument a line before changing it, then look at where the time actually goes. It is rarely where people expect, which is the reason for measuring first.
IoT integration
Sensors and equipment connected to a network and reporting somewhere useful — run hours, temperature, vibration, counts.
OT security
Plant networks were built to trust everything on them, and most still do. We segment them from the office network, control remote vendor access, and inventory what is actually connected.
Maintenance systems
Run hours and condition data feeding the maintenance schedule, so parts get changed on evidence rather than on a calendar.
What connecting the floor to the office gets you
Four outcomes, in the order they usually arrive. The first one pays for most of the work.
Less manual re-entry
Any number typed in twice will eventually disagree with itself. Removing that duplication is usually the fastest win available on a plant floor.
Answers during the shift
Downtime, scrap and throughput visible while the shift is still running, rather than in a report that arrives next week when nobody can act on it.
Fewer surprise stoppages
Condition data and alerting mean you find out a bearing is running hot before it takes the line down — in most cases, and not for faults that arrive without warning.
OT and IT that agree
The plant system and the ERP holding the same numbers, so nobody has to decide which one to believe in a Monday meeting.
Where this work usually lands
Same idea in three settings: capture the thing where it happens, and stop it being retyped later.
Manufacturing
Production counts, downtime reasons and quality checks captured where they happen, then fed to the systems that plan the next run.
- Production monitoring
- Quality assurance
- Equipment optimization
- Supply chain integration
Construction
Site progress, equipment location and inspection records reaching the office the same day rather than at the end of the week.
- Project planning
- Site monitoring
- Equipment tracking
- Safety compliance
E-Commerce
Warehouse scanning, stock levels and order status kept in step between the floor, the storefront and the finance system.
- Inventory control
- Order processing
- Logistics automation
- Customer analytics
How we install it without stopping you
Nothing changes on a running line until we can show what that line is doing now.
- 01
Survey
What is installed, what it can already output, and what is still on paper. This part is mostly walking around and asking the people who run it.
- 02
Design
What connects to what, where the data lands, and what happens when a link drops. We design for the network being down, because at some point it will be.
- 03
Install
Staged, usually inside planned downtime. Production keeps running while we cut over one piece at a time.
- 04
Tune
Alarm thresholds are always wrong at first. We revisit them once you have lived with the system, otherwise people learn to ignore the alarms.
Technology focus areas
Industrial IoT
Equipment and sensors connected and reporting — including older kit that predates all of this and only speaks one protocol.
Edge computing
Processing at the machine, so a control decision never waits on a link to a data centre.
Digital twins
A model of the asset fed by its live data. Mostly useful for testing a change before you make it on the real thing.
AI/ML integration
Pattern detection on run data — predicting a failure, spotting quality drift. Worth doing once you have enough history to train on, and not before.
Tell us what the floor can't see
A line that stops for reasons nobody records, a report somebody assembles by hand every Monday, or a plant network nobody has mapped in years.