With automotive manufacturing becoming increasingly connected, manufacturers need to leverage Industrial Data to provide meaningful insights to support operations.
By using Artificial Intelligence and Industrial Internet of Things (IIoT) manufacturers can utilise connected devices and systems to gain greater insight into their industrial data.
In this article we'll look at 4 use cases and some challenges.
- Connect Industrial Devices
Modern automotive manufacturing facilities can include a variety of devices such as Programmable Logic Controllers (PLCs), Sensors, Robotics and various manufacturing software.
These will not always be compatible. When implementing an integration you will need to consider the protocols, data structures, network and security.
Depending on your environment you can connect different compatible devices together using technologies like OPC UA and MQTT.
You want to gain access to the relevant data without impacting critical manufacturing processes.
- Predictive Maintenance using AI
Unplanned downtime can occur due to equipment failure.
Sensors can be used to collect data from your equipment such as vibrations, temperature, pressure and machine status. This can then be analysed to detect any anomalies.
If you have enough reliable historical data then Machine Learning Models could be used to predict the likelihood of certain failures.
This isn't just adding an AI Model. You will need to consider factors such as sensor quality, false positives, model efficiency, maintenance processes and validation.
- Improve Asset Tracking Using RTLS
Tracking tools, parts and vehicles can be an issue in a large manufacturing facility if you don't have accurate location data.
RTLS Technologies can be used to track certain assets and provide location data to relevant applications.
Depending on your accuracy requirements and manufacturing environment you could use Ultra Wideband (UWB), Bluetooth Low Energy (BLE), RFID or other location technologies.
Some great use cases include:
Tool tracking
Work in progress vehicle tracking
Better visibility of materials
Support inventory
Detect delays in internal logistics
You will need to decide what accuracy, range, cost of infrastructure and environment you need for your solution.
- Connect Industrial Data
Industrial data is often stored across multiple systems including:
PLC – Control of machines and industrial processes.
SCADA – Supervisory control.
MES – Helps manage and monitor manufacturing.
ERP – Business processes such as inventory and planning.
By connecting these systems together you can get a better insight into your manufacturing processes.
However there can be issues if the integration is not well planned. If your data isn't mapped well, time stamps aren't consistent, there are network issues or cyber security issues it won't provide as much benefit.
Start with identifying what data you need to achieve your target.
Practical Implementation
Manufacturers can reduce complexity by implementing in stages.
- Define Problem
Identify a problem that you want to measure. For example unplanned downtime or difficulty finding assets.
- Review Data
What data do you have, what quality is it, how up to date and how accessible?
- Choose Technology
Consider what sensors, connectivity, analytics and integration you need.
- Pilot
Implement within a small section of manufacturing to test.
- Measure
Review relevant metrics. For Example downtime, time taken to find assets, data availability or manufacturing delays.
- Scale
Once you have improved your implementation you can scale.
Final Thoughts
AI and Industrial IoT can help automotive manufacturers improve visibility and equipment monitoring. However to be effective you need reliable data, good integration, good security and clear business objectives.
If you're looking to implement AIoT in your automotive manufacturing processes OEMNex AI (https://oemnexai.com/ ) is a great place to start learning about Industrial AI.
The most important thing to consider is how much data you can actually use to help people solve problems in your manufacturing process.
Discussion: What do you think is the biggest challenge in Smart Manufacturing? Integration of old systems, having reliable data or implementing AI models?
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