Across production lines and facilities, automotive manufacturing involves coordination of resources, from components and inventory to machinery and quality control. With multiple operations occurring simultaneously, the involvement of isolated data islands and varied platforms can cause challenges within operations visibility, inventory management, and production traceability.
Artificial Intelligence of Things (AIoT) can help manufacturers address these challenges by connecting operational data points, and presenting them in actionable ways
This article considers AIoT applications in automotive manufacturing, and how operational insights might be applied in practice.
- Connecting Data From the Factory Floor
Within automotive manufacturing, data might be generated by production equipment, inventory systems, tracking tools, or enterprise platforms.
However, merely collecting this information is only one part of the solution. For manufacturers, there is a need to combine and present this information in a way that reflects the realities of operations.
A connected manufacturing environment could enable this process, by helping to structure data points according to priorities on the factory floor. Several insights become possible as a result:
Considerations:
• How data from various platforms might be aggregated
• How assets, components or materials might consistently identified or tagged
• How events could be time-stamped or linked to one another
• How this information might reach relevant teams or systems for analysis and action
- Improving Asset and Inventory Visibility
Across automotive manufacturing facilities, and especially on a shared production line, it becomes necessary to know the location, availability, or allocation of inventory items and equipment. These considerations extend to materials or components within a facility, and especially between storage spaces or work stations. Connected tracking and inventory solutions could therefore play a role in manufacturing operations. This could allow team leads or managers to know the status of a given material, and better plan their operations. The extent to which such insights might be useful, and how these applications might be implemented will depend on data availability and integrity.
- Supporting Component Traceability
Throughout automotive manufacturing, component traceability is one key concern that drives data acquisition and logistics strategy. By nature of the industry’s complexity, these considerations need to occur across multiple dimensions and scales.
A systematic approach to component movement and inventory control can enable higher levels of traceability, as production events can be tracked throughout the supply chain. With relevant data points connected, traceability records concerning a given component could be reviewed, to identify any production or inventory inconsistencies.
The extent to which such functions could be applied would depend on available data and the degree to which these insights might be applicable.
- Turning Operational Data Into Actionable Insights
The AIoT space combines several technologies to deliver value to manufacturers, but its practical value is most evident when it comes to analyzing operational data. In short,
before deploying advanced analytics capabilities, manufacturers must build a consistent set of information regarding their processes, equipment, materials, or inventory. This might begin with a defined goal, such as improving inventory management. From there, relevant data points might be identified and validated, and new insights built around a specific manufacturing purpose. As an illustrating example, it might be easier to begin with inventory visibility or material management, and eventually apply similar principles at a larger scale.
- A Practical Implementation Approach
As with most AIoT applications in manufacturing, there is value in approaching connected systems capabilities from a specific and well-defined application. In the broadest sense, a manufacturer interested in AIoT applications might begin with the following considerations:
A specific pain point: This might range from difficulties with inventory management to challenges with production tracing or other concerns
An inventory of available data: From here, manufacturers can begin to consider what existing systems might contribute to the problem
Integration considerations: This might involve existing equipment limitations, access restrictions, or even security concerns
A limited scope pilot: With an ideal candidate process, manufacturers can begin to deploy limited scope insights or analytics
Performance quantification: With new visibility or capabilities, the manufacturer can analyze the extent to which such changes have improved operations
Larger scale application: Depending on performance and impact, these solutions might be deployed in an extended scope or integrated into new processes
Don't Overlook Interoperability and Security
In an operational setting, the benefits of an interconnected system must be weighed against several engineering and security considerations. In particular, different platforms or production equipment may generate data in different ways, preventing straightforward aggregation and analysis. Consistent tagging or labeling of materials, components or inventory items will also be critical in ensuring that data points reflect tangible production events.
From a security standpoint, an organization must recognize the potential risks and ensure appropriate restrictions or isolation within its IT infrastructure. These considerations will affect the practicality of an AIoT initiative, including its ability to deliver meaningful insights and advantages on the factory floor.
Conclusion:
AIoT has the potential to improve automotive manufacturing, particularly in terms of production visibility and component traceability. Most applications would benefit from a clearly defined goal, realistic expectations, and a practical implementation window. In considering such applications, manufacturers may want to begin with a clearly defined pain point that is supported by available data, and ensure realistic integration expectations.
To review an automotive manufacturer’s perspective on connected operations and AIoT applications, visit "OEM Nex AI".
What do you consider the biggest technical obstacle to connected automotive manufacturing: legacy-system integration, data quality, component traceability, or real-time operational visibility?
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