Meaningful AI use cases in the manufacturing industry

Expert Views

Andrej Nikonov

Technology Leader Explainable AI

It is obvious that artificial intelligence (AI) is ubiquitous in today’s world. Although the technology itself is not brand new, ChatGPT has significantly changed our perception of it. The language model has made it clear to many people how advanced the technology has become. It shows that AI is capable of performing tasks at the highest level that we wouldn’t have expected it to be capable of. This leads us to consider how companies, especially in the manufacturing industry, can benefit from this development.

At the outset, it is worth mentioning that ChatGPT is a deep language model whose answers are based on statistical models and are therefore not always accurate. Especially in narrowly defined task areas, the benefits are invaluable. For example, there are many applications in the manufacturing industry, especially in knowledge management and efficiency optimization. Furthermore, ChatGPT’s capabilities can also be applied to image-related applications, making it useful in design, marketing, and advertising.


The following aspects should be taken into account during the deployment


With the EU AI Act, Europe is already working on guidelines for artificial intelligence in order to do justice to technological progress. Standards and certifications are also being developed in Germany to make AI use safe.

When introducing AI to manufacturing, companies should decide which solutions can be purchased as standard and where custom applications are required. Consortia are developing industry-specific platforms that can serve as a foundation. Existing platforms from major cloud providers can also be used. AI developments range from innovative, research-oriented projects to smaller, standardized solutions.

It is essential to ensure that the team is suitably composed. At best, it should consist of experts for the industry, the application technology, and for machine learning, as well as end users. A comprehensive database is essential to avoid bias. Companies should be judicious in their data selection. In the end, the focus is always on the need to address actual problems and identify real vulnerabilities. A guiding question for this is to consider what capabilities the system can actually unleash.


Cloud-based or local AI applications?


In order to operate AI applications, a company’s IT infrastructure must be adapted to process large volumes of data.

The decision between cloud-based and locally installed solutions must be made. Cloud solutions are scalable, but can raise data privacy issues. Therefore, it is important to consider where the data is stored and whether third-party providers may have access to sensitive information. One option might be to host the data in an on-premises data center. Although computing capacity may be more limited here, this method offers a higher standard of data protection.


The path to practice


Examples of the use of AI in manufacturing are diverse and growing. AI capabilities can address both technological and business challenges and find varying applications depending on the industry. In the area of productivity, AI applications can be used in combination with business processes and automation techniques.

Specific applications of AI in the industry include chatbots for customer service, assembly assistance systems, crawling tools in procurement, quality monitoring, diagnostic systems, intelligent selection processes in warehouse logistics, and optimization of operations and product design through AI.

Before we use sophisticated algorithms to control operational processes, an essential step is required: data collection. Often, this step requires building new infrastructures, as data is frequently generated in industry and replaced shortly thereafter. Therefore, it is essential to implement robust data management systems to ensure consistent collection and sustained use of this data.


Automation: quality control (QC) through AI


To get a more detailed insight, let’s take a look at a proven case: automation in the QA sector using visual AI analysis.

Here, the time-consuming and cost-intensive manual inspection can be replaced by automated systems. For implementation, data from defect-free as well as from defective products must first be entered to create a frame of reference.

To implement a suitable system, the software must first understand what a flawless product, component, or foreign object is like. For this purpose, image data of ideal conditions as well as of defective products or typical foreign bodies are fed in. This establishes a criteria framework that determines what is accepted and what is not. If the system identifies a defect or foreign body, it is immediately rejected. The criteria can be adjusted and optimized as needed.


Targeted use of AI for maximum profitable results


In summary, AI in industry offers a wide range of optimization opportunities. With clearly defined goals, the right resources, and a flexible approach, companies can fully exploit the potential of AI.