An Industry 4.0 approach to a factory that is intelligent, digital, and networked
Industrial IoT (IIoT) is the industrial system that forms when a new generation of information and communications technology meets the industrial economy. When equipment is connected, the plant can sense its data, move it, and analyze it as it arrives. Decisions and control then rest on that picture. Resource use improves, and the factory can take on made-to-order work, services around the product, platform-based design, and production that is run by software.
The points that stall a move from a conventional plant to smart manufacturing
Lines still depend on people at the station. Output is slow, consistency is poor, and the line cannot keep up with modern production
There is no useful watch on the machines. Faults show up late, repairs are expensive, and production stops
Production data is not collected as the work happens, so managers cannot see the floor and decisions have little to stand on
Inspection methods are behind the work. A product cannot be traced through the process, and quality does not stay steady
Three industrial IoT uses that raise what a manufacturer can compete on
A digital rebuild of the line, for flexible manufacturing and production run by software
A smart manufacturing system puts industrial sensors, RFID tags, industrial cameras, and other IoT devices on the machines, so the process is captured in data. It ties in MES, ERP, and the other systems around them, and the result is a digital shop and a smart factory. AI tunes the plan, the quality checks, and how equipment is scheduled. The plant can take custom orders, run flexible manufacturing, and follow lean manufacturing, with higher output and more consistent quality.
Production data is handled next to the line, so control can answer in milliseconds
A digital copy of the line, used to simulate a change and to forecast what it will do
Quality can be followed from start to finish, so a defect can be pinned to a step
Many variants in small batches, with a fast change from one product to the next
Live watch on industrial equipment, and maintenance that can be done from a distance
Temperature, vibration, pressure, and current sensors sit on the machines that matter, and they report how the equipment is running. The system supports remote monitoring, fault diagnosis, and performance analysis. With the machine's history and an expert library, it can spot an abnormal pattern, warn before a failure, and support remote troubleshooting. Downtime and maintenance cost both come down.
The equipment is watched around the clock, and an exception raises an alarm at once
A mobile app for the status of a machine and for clearing an alarm, wherever the engineer is
AR guidance and an expert online, so a problem on the floor can be closed without waiting for a visit
Trends in how the equipment performs, and suggestions for running it more efficiently
AI that forecasts a fault and shapes the maintenance around it
Predictive maintenance uses machine learning and deep learning on how the equipment runs, and it builds a health model and a fault model. It can say what kind of fault is coming and roughly when, then propose a maintenance plan. As the models keep learning, the forecast gets sharper. The plant moves from fixing what has already broken to maintaining before it does, and both the maintenance bill and the lost production fall.
Deep learning that recognizes a fault pattern, with accuracy above 90%
A maintenance plan generated for the plant, balancing how available a machine needs to be against what the work costs
Demand is forecast closely enough to set stock where it is needed, and the inventory cost comes down
The fault type is matched to the person who can fix it, so the job is done by the right skill
The results an industrial IoT program is built to produce
Our specialists will plan the industrial IoT work and carry it through