1.Automotive manufacturing operations produce information from equipment, sensors, processes and related systems. Understanding how best to capitalize on the value of data within this complex environment can be challenging. The potential value lies in making sure this information is useful to people who need to manage the operations in day-to-day operations.
This is an opportunity to consider the value of Artificial Intelligence of Things (AIoT).
Connecting AIoT concepts to automotive manufacturing can benefit operations teams by bringing a broader, more integrated awareness to factory floor activities.
- What exactly is AIoT in a manufacturing environment?
A standard “IoT” implementation focuses on connecting equipment and gathering information from sensors.
AIoT takes this concept further by including processes to analyze this information.
One useful way to think about the architecture is:
Equipment & Sensors –> Collecting data –> Connectivity –> Collecting and processing data in an organized manner –> Analytics –> Insights to support Operational Decision-Making
These elements have specific purposes which contribute to the overall value.
Equally important are the differences between each element. Let’s examine them individually.
- Equipment and sensors
They provide information about operational processes. Selecting what information to gather depends on the objectives. In most cases, this is information that will eventually contribute towards operational decision-making.
AIoT system success starts with this component. It may be tempting to select every relevant data point, but prioritize based on what is useful for solving the specific problem.
- Data collection and connectivity
Connecting various systems and data sources within the operational environment becomes even more crucial.
This applies particularly when working with legacy systems and equipment from various suppliers to create a more uniform data source. The goal is not to collect data for the sake of data collection. It is to make relevant, useful information accessible in a timely manner.
Processing this information may include structuring it for easier digestion. It may also involve managing these data points in relation to other information.
- Analytics
Analyzing operational data to detect trends, patterns and correlations supports the entire manufacturing environment.
Depending on specific needs, it can apply to a variety of insights, from equipment monitoring and production insights to process analysis and maintenance. The specific application depends on the requirements of the manufacturer. It is relevant for identifying how best to apply data to support decision-making.
- Operational insights
Having created this complex system – it is time to think of the end purpose.
A sophisticated technical implementation has little value unless it provides accessible, meaningful insights that people can apply. Teams that manage and maintain manufacturing operations are the ultimate beneficiaries of an AIoT implementation.
This highlights the importance of relevance. Unless people can take meaningful actions with the generated information, the entire endeavor loses its purpose.
3.Why should automotive manufacturers care?
Automotive manufacturing requires a complex set of manufacturing processes and related systems. Having additional information about these activities supports better decision-making.
An AIoT-driven approach makes it possible to take that information and apply it within a relevant context.
For example, rather than looking at equipment statistics and production data points in isolation, teams can combine the information to get a broader understanding of what is happening on the shop floor. This, in turn, supports decision-making by answering questions such as:
• What is happening overall in the manufacturing environment?
• Are there any changes in equipment conditions?
• What are the patterns that are emerging?
• What information should manufacturers be looking for?
• How can they apply historical and current information better understand what is happening?
This list is by no means exhaustive. The specific applications depend on the unique requirements of each automotive manufacturing facility.
- AIoT is not just about sensors – it is much more than that
One common misconception about AIoT is that it is only about connecting more devices. Connectivity is a useful start, but it only forms a small part of a much bigger equation. In many ways, it is a process-driven initiative rather than a hardware-focused one.
Key considerations include data collection, systems integration, processing, analytics, existing environments and relevance to overall operations. There is no doubt that hardware connectivity plays an important role. At the same time, teams must think beyond the sensors.
- Starting small: A practical perspective on AIoT in manufacturing
Manufacturers evaluating the potential value of an AIoT implementation do not have to think of a large-scale initiative right away.
Challenging questions that people need to ask themselves include the potential value of data in specific situations. For example, teams can think of a particular process where additional equipment insights or production information would be relevant. Based on that, determine key considerations, such as:
What data is currently accessible?
What is missing?
What systems provide the relevant information?
How can these data sources be brought together?
What kind of analytics does the initiative require?
What will the insights generated be used for?
This approach examines the practical application of an AIoT-powered initiative. It looks at the problem first – and then determines how best to use AIoT to solve it.
An example of how to make sense of this complex process while staying focused on the end goal for vehicle manufacturing operations is available at OEM Nex AI.
- Summary
Artificial Intelligence of Things (AIoT) provides valuable insight into connecting data and building a more integrated view of automotive manufacturing operations.
The real value lies in focusing on the end goal – and realizing that it is not about how much data the manufacturing environment produces. It is about what this information can mean to people who manage and maintain the operations. These insights, in turn, become a crucial enabler for any connected-factory initiative.
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