Contemporary car manufacturing facilities generate data from their machinery, sensors, video surveillance, production software, connected equipment, and other sources.
But the crucial question is not in how much data these sources produce. Instead, it is about how the manufacturers can extract value from this data stream.
This is where Artificial Intelligence (AI) and the Internet of Things (IoT) can be utilized.
By connecting smart devices and analyzing the data they produce with AI, the manufacturers can transform it into meaningful insights.
Thus, the most interesting applications of using AI in automotive manufacturing are related to IoT.
From Connected Devices to AI-Powered Insights
IoT is used to connect devices and systems, enabling them to communicate with each other, typically through some form of gateway or middleware.
In many cases, these are sensors that collect information about the physical conditions they are located at, whether it is a machine, a room, or another environment.
This data stream can be processed with analytical tools or AI algorithms to extract information about the environment and find patterns that are useful for insights generation.
This pattern recognition can take place in a simple form, for instance, by visual inspection.
For example, the machine tool data captured by these devices can be used to determine average values of normal functionality and alert the maintainers if something seems abnormal.
A more formalized but similar approach is implemented in the computer vision field, which is fueled by the abundance of image data from different manufacturers’ equipment.
The data collected can be processed by AI algorithms to analyze and identify features, objects, or patterns that could be utilized to improve some aspects of a given operational unit.
To summarize, the critical application of AI in manufacturing is linked to smart devices and systems connected through IoT, forming an ecosystem of data.
The main points about these advantages are as follows:
• Predictive maintenance – Fluctuations in vibration, temperature, and other metrics can be detected by the changes in equipment performance data to find patterns potentially indicative of future malfunctions. This makes it possible to detect deviations from normal functioning and alerts the maintenance team in case something goes wrong.
• Quality inspection – Computer vision algorithms can be applied to analyze images and detect patterns that usually signify some irregularity.
• Production overview – Connected equipment can give a better understanding of what is going on in the manufacturing process.
• Production planning – Similar to the previous point, historical and current data on equipment performance can be utilized to see what patterns affect the production process and how to plan the manufacturing.
However, the actual use of such data for business requires human analysis and operational understanding of the equipment and manufacturing process.
The Larger Perspective
IoT connects the physical and digital worlds.
On the other hand, AI is good at extracting insights from patterns in data.
Therefore, it is not surprising that the two are sometimes used together, with IoT generating data and feeding it to AI models that can learn from it and find patterns.
Ultimately, it creates a powerful triad of “things,” data, and analytics, which can transform the automotive manufacturing industry.
However, right now, most of these opportunities are still unachieved.
The deployment of such projects is capital-intensive, technically demanding, requires proper data preparation and processing, and a clearly defined purpose.
After all, without proper insights that can answer specific questions, there is not much a manufacturer can do with AI and IoT.
A working example of such technology is represented by OEMNex AI in the automotive manufacturing industry.
In many ways, the idea behind this technology is inspired by the described above possibilities of applying AI in manufacturing.
For instance, analyzing the data of production equipment as a whole can be useful for predictive maintenance, ensuring nothing goes faulty.
However, the implementation of such projects usually requires time, resources, and prioritization.
Hence, companies that want to use AI in their operations do not try to utilize every possible advantage of connected devices.
Instead, it is usually better to prioritize one particular use case that is the most pressing and come up with an AI solution for it.
Such operational approaches are usually well-formed in specific processes like predictive maintenance. Below are the steps that may be taken in this instance:
1. Determine the problem: This use case is relevant to most manufacturing facilities since equipment malfunctions can be costly. Nevertheless, some problems are more acute than others, and it is worth prioritizing them.
**2. Determine available data: **In many cases, some of the necessary data may not be available, or it is redundant and thus should be discarded.
3. Identify the action items: At the same time, the data that needs to be collected for processing should be defined. This step often involves the identification of the necessary but currently unavailable information.
4. Build a pipeline: Next, an integrated system that ensures the collection of this data stream should be constructed.
5. Find patterns: The next stage involves finding patterns and utilizing them to build an analytical model, possibly with the help of AI.
6. Take action: A response mechanism should be designed to react to certain findings, such as anomalies in equipment performance.
7. Evaluate results: Finally, the actual impact of the solution should be checked, preferably against a selected metric, and the results analyzed.
This way, the implementation of an AIoT project can begin with a well defined goal in a specific operational context.
As mentioned at the beginning of this article, most of these opportunities are yet to be capitalized on by automotive manufacturers.
As a result, it remains to be seen which of these three use cases would produce the most substantial advantages for the manufacturers predictive maintenance, quality inspection, or production planning.
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