FACTORY AUTOMATION
Most plants already have sensors, controllers, and some software. What is usually missing is the layer that makes them work as one system. Gate-IoT integrates field devices, controllers, edge gateways, and the IoT platform above them — and takes responsibility for the joins between them.
Every industrial IoT system we build has the same shape. The brands change from project to project; the structure does not.
Sensors, meters, and instruments that produce the measurement in the first place, chosen for range, signal type, and the environment they sit in.
PLCs, HMIs, drives, and remote I/O — either the ones you already run, or new ones matched to the existing automation.
Gateways that collect, buffer, and translate. This is where a plant full of incompatible protocols becomes one consistent data stream.
Dashboards, alarms, history, and reporting, running on established IoT platforms rather than a closed product of ours.
The stack is the means. These are the things a plant actually asks for.
Consumption measured per machine, per line, and per utility, with history and trends — so a reduction target has something to act on.
Real-time process views for operators and supervisors, reachable from the control room, the office, or a phone.
Availability, performance, and quality measured from the machines themselves. See machine management →
Where equipment, tooling, and moving assets are, how they are being used, and when each is next due for maintenance.
Wastewater, emissions, and air quality measured continuously, with the report trail regulators ask for. See environment →
Shift and daily reports generated from recorded data instead of assembled by hand, scheduled by email or exported to Excel and BI tools.
Screens are built around how your plant runs — the examples above show the views we most often set up first.
Plant data is sensitive and plant networks are not always online. The deployment model is a decision, not a default.
Everything stays inside the plant network. Nothing leaves the site, and the system keeps running when the internet does not.
Reachable from anywhere, with no server for your team to maintain. Suits multi-site operations and remote management.
Control and history stay local; dashboards and reporting are published outward. The common answer in practice.
The stack is the same; what changes is what is measured and which regulations apply.
“Dark factory” gets used as though it were something you can buy. It is not — it is what a plant looks like after several other things are already working. Here is the honest ladder.
Most plants we visit sit at level 2: data is being collected, but nobody is yet acting on causes. That is a good place to be — and the shortest route to level 3.
The developments worth planning for now, and what each actually requires.
Rather than point-to-point links between every system, machines publish once to a common bus and any system subscribes. It is the decision that determines how painful the next ten integrations are.
Inspection and counting done on the line at line speed, with the model running on the edge device rather than in a data centre. Already practical for surface defects, presence checks, and reading labels.
A model of the line kept fed by real data, so a scheduling or layout change can be tested before it is imposed on production. Worth it once your history is trustworthy.
Autonomous mobile robots and cobots need to know what the line is doing to be useful. That coordination is an integration problem, and it is the same data layer again.
Unmanned shifts, usually starting with one machine or one cell overnight rather than a whole plant. It needs reliable machines, automatic material handling, remote diagnosis, and alarms someone will actually answer at 3am.
Energy data collected for cost reasons increasingly has a second job: proving consumption per unit to customers and regulators. Measure it per machine now and the reporting is already done.
Do not start with the dark factory. Start with one line, measured properly, until the numbers are trusted enough to argue with. Almost every plant that tried to jump straight to autonomy ended up with an expensive system nobody believed.
The order that works: connect and measure, then find the causes, then predict, then automate the response. Each stage pays for the next one.
Machines reliable enough to run unattended, and the maintenance discipline to keep them that way. Material in and out without a person. Every fault detectable remotely, not by someone noticing. And an escalation path that works at night.
If any one of those is missing, an unmanned shift produces scrap instead of parts. We would rather tell you that before the quotation than after.
Start where the return is clearest, prove it, then extend across the plant.
What equipment exists, what it can already report, what network is available, and which problem is costing you most right now.
A device list, a platform choice, and a deployment model — usually with more than one option and the trade-offs written down.
Devices fitted and configured, screens built around your process, and your team trained on the system they will actually use.
Once the first area is proven, the same platform takes on more lines, more measurements, and more sites.
Describe the equipment and the problem you want solved first. We will come back with a device list, a platform recommendation, and what it costs to start with one area.
VN