Industrial facilities generate a lot of data. From sensors, machinery, devices, and other sources. Every piece of equipment can be turned into a data point. The issue is that this information has to make its way into the hands of the people who need it.
How can organizations harness the power of connected data and turn it into actionable intelligence?
Artificial Intelligence of Things, or AIoT, helps with that.
- Understanding AIoT
Internet of Things, IoT, refers to the ecosystem of connected devices, sensors, and other means of collecting data. IoT enables the collection and aggregation of information. Artificial Intelligence, AI, is the process of analyzing the data and transforming it into something useful. AIoT is a combination of these two fields.
Physical Assets
Sensors and
Identification
Connectivity and
Data Collection
AI and Data
Analysis
Insights and
Decisions
Action
The chain of command begins at the bottom with physical assets. These are usually some form of equipment or machinery. IoT adds sensors and identification tags to these items, which then relay the information to an AI network through connectivity. The AI analyzes the data and transforms it into actionable decisions and insights.
What AIoT strives to accomplish is contextual information.
A lone sensor may not have much to say about the situation it is in. A group of interconnected sensors can build a picture of the context and environment it is in and relay useful information and insights.
Situations such as logistics, where the location of an item or a vehicle is of interest, can benefit massively from AIoT. The ability to identify vehicles and track their movements, as well as the condition of the machinery, is a huge boon to any industry that makes use of vehicles or equipment.
Context is also relevant to the field of logistics. When a company wants to know how many units of equipment it has, where they are, and how they are doing, without having to look at every single one of them, AIoT can help. That is if the information is there and available to be read by an AI.
- Limitations and Potential
AIoT has a variety of applications, but it is not without its limitations.
Industry
Potential application
Manufacturing
Condition and maintenance of equipment and production
Logistics
Location tracking of vehicles and returnable assets
Construction
Usage, location, and maintenance of equipment and materials
Energy and utilities
Condition and monitoring of equipment, environmental data
As can be seen in the table above, AIoT has a number of potential applications in various industries. It is useful in areas of equipment management, production, and location tracking of various kinds.
The problem is that AIoT is not always a silver bullet. While it might seem like a simple matter of hooking up sensors to equipment and letting an AI do the rest, it is not that simple. Context is still key to making AI work, and not all data can be used in every scenario. It is also an issue of integration and compatibility. Finally, an organization has to have a framework in place to handle the information provided by AI.
Human oversight is of particular importance when it comes to AI. This data has to be put into some meaningful context, and that context has to be useful to the people who will be using it.
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