Factories have lots of connected systems, but that doesn't guarantee they have useful insights.
In a world of PLCs, MES, ERP, RFID, UWB, BLE, sensors, vehicles, robots and other machinery, each can provide valuable data but that data is often siloed in different applications.
This is where AIoT could be interesting.
Combining physical devices connected to systems that can analyze and find patterns in the data they generate can yield some interesting insights.
The Simple AIoT Architecture
Physical Assets -> Sensors -> Connectivity -> Data -> AI/ML -> Insights -> Action
Where the physical assets in a factory can be machines, vehicles, tools, inventory and other relevant equipment
Sensors and identifiers can pick up all kind of information like:
Machine condition
Temperature and vibration
Location of different assets
Movement of vehicles
Production events
Inventory movements
Equipment utilization
And with connectivity this information can be brought together in the cloud or other infrastructure where algorithms can analyze the data, find patterns, anomalies, correlations or other information that might not have been obvious when just looking at the data coming out of one system.
The Challenge with Data Sources
The challenge with many of these systems is that the information is distributed and not readily available across applications.
A production delay may relate to a number of factors, a machine, a vehicle, inventory and a production event, and information from any of these domains would have to be combined to detect patterns.
An AIoT architecture could combine different sources of information like:
Machine information + vehicle location + production events + inventory status
and provide some interesting insights that may not have been apparent when looking at any of the systems individually.
This could be done without disrupting any of the existing technologies in a factory, as such an AIoT architecture could work with existing MES, ERP, PLC, SCADA, RFID, BLE, UWB, or other IoT systems.
The Practical Use Cases
Some examples could be:
Predictive maintenance
Using sensors connected to machines, which are often already present in many facilities, to help identify patterns that can help indicate possible maintenance needs beyond what any individual system may provide.
Vehicle and asset traceability
Using RFID, UWB, BLE or GPS sensors to track vehicles or other assets and bring the information together in one place, providing a more complete view of their movement and utilization than any individual system.
Production analytics
Leverage information about the production process and equipment to uncover patterns and insights about production events and bottlenecks.
Intralogistics
Use information derived from the location of inventory and vehicles to understand material flows in the factory.
The Important Part: Context
Getting data is often the easy part, deriving value from it is often much harder.
The temperature of a machine may be interesting, but knowing how that relates to the overall production process, the stage it is in, the type of equipment and other factors is even more valuable.
Which is the reason why AIoT is a great combination, IoT provides the data but AI provides the contextual analysis needed to get the most out of it.
Taking it to the Next Level
AIoT is really just an approach, a method of using existing IoT infrastructure and connecting it to AI/ML systems to derive new insights.
The challenge for manufacturers is to get the information they need to actually improve operations.
In many cases that means choosing an area of focus such as predictive maintenance, vehicle traceability or another domain and then identifying the relevant technologies and data sources to support it.
Anyone looking into this space should consider visiting OEMNex AI to learn more about AIoT in the context of automotive manufacturing.
It is not about connecting everything but rather about creating a feedback loop of Observe -> Connect -> Analyze -> Understand -> Act to get real value from the process.
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