AI in industry: what companies need to look out for

What does AI in industry mean?

In contrast to general AI, AI in industry focuses on specific application areas such as quality control, predictive maintenance, process optimisation and robotics. AI in industry refers to the use of algorithms and machine learning to automate complex tasks, identify patterns and make predictions. This involves analysing and processing large volumes of data from machines and systems.

But why should companies rely on AI in industry?

One example is that malfunctions in a machine can be detected in real time by analysing them. By using AI in industry, you can carry out predictive maintenance, ensure product quality (predictive quality) and utilise resources more efficiently (predictive energy).

What are the advantages of AI in industry?

This is why companies should use AI software:

• Increased productivity and OEE
• Transformation of the maintenance strategy towards condition-based maintenance
• Reduced costs, e.g. for energy and production
• Improved occupational safety
• Competitive advantage through differentiation from the competition
• Improved quality
• Less unplanned downtime

The advantages of AI in production are manifold. For example, AI solutions such as predictive maintenance can be used to identify malfunctions before they lead to expensive failures. If problems occur, maintenance measures can be taken immediately. It avoids unplanned failures, reduces maintenance costs and increases the company's productivity. In addition, the data also contributes to occupational safety since material fatigue can be detected to some extent, thus preventing hazardous situations.

Another major advantage of AI in industry is the improvement in product quality. By using AI, companies can detect and correct defects and errors at an early stage, thus reducing rejects and rework. This leads to greater customer satisfaction and a positive company image.

That is why predictive capability is another important aspect of AI. By analysing large amounts of data, AI algorithms can identify trends and patterns that other experts would normally miss. This allows companies to make informed decisions and better manage future challenges.

Using AI in industry: a step-by-step guide

The use of AI in industry requires careful planning and preparation. It is important to select the right AI technology that matches the specific requirements of the company. Aspects such as scalability, flexibility and data compatibility should be taken into account. Thanks to the large number of AI projects we have completed in industry, we can provide valuable insights and show what needs to be considered when using AI in industry.

1. Deciding to make or buy:
Before an AI solution can be used, companies must decide whether they want to develop an AI solution internally or purchase it externally. During the decision-making process, they must take their specific requirements, resources and budgets into account. How much do we want to spend on a solution? Do we have enough specialists with expertise in AI, data science, IT and production, etc.?

Finding the answer is not always easy. In our make or buy guide to AI solutions, you can find out more about this; it provides a check list to determine when an in-house or purchasing an external solution is the better option.

2. Data acquisition:
The relevant data sources in production, e.g. sensors, machine logs and quality control data, must be identified. Data connectivity must be guaranteed.

3. Data processing and analysis:
In order to use AI effectively, it is important to have sufficient high-quality data. These need to be cleaned up, structured and prepared so they can be processed by the AI algorithms. The aim is to identify patterns and correlations in the data.

4. Integration:
The AI solution has to be integrated into existing production systems. Comprehensive tests are carried out to ensure that everything works smoothly and that the data can be processed in real time.

5. Training:
The AI models are trained with all the collected data. It is important to regularly review and adapt the training process to improve the accuracy and performance of the AI models. This ensures that the best results are always achieved.

This step-by-step guide gives you a solid foundation for implementing AI in your production processes. AI offers enormous potential for optimising your production and increasing your competitiveness.

Examples: using AI in industry

To illustrate how AI can be used in industry, we will now look at some specific, real-life examples. These companies have decided to purchase an AI solution and have reaped the benefits.

• A company in the automotive industry has used Festo AX Industrial Intelligence to improve machine availability and quality assurance in body-in-white production. By using AI algorithms, it has been possible to identify problem, such as compressed air loss, in real time and take immediate action. This has significantly reduced rejects and rework, resulting in considerable cost savings. Unplanned downtimes were reduced by 25%, the Mean-Time-To-Repair (MTTR) by 20%.

• Another company in the semiconductor industry has used Festo AX Industrial Intelligence to maximise the quality of its wafers. Thanks to the AI analysis, typical sources of error in the sawing processes (e.g. rough surfaces) were detected in advance. This quickly led to the launch of quality assurance measures. The company saved €100,000 per year as a result of reduced waste per line.

Summary and outlook

Using artificial intelligence in industry can unlock enormous potential. It offers companies a wide range of benefits, such as increased efficiency, cost reduction and a better basis for decision-making.

The future of AI in industry is promising. As AI technologies continue to develop and awareness of their potential grows, the use of AI in industry will continue to increase. Companies should address the topic early on and take advantage of the opportunities that AI offers so they can develop innovative solutions and remain competitive. There are already standardised AI solutions on the market for various applications, such as predictive maintenance of cylinders. These can be connected, making the introduction of AI in industry easy and convenient.

The real-life examples show how companies are already benefiting from AI in production. You must give careful thought to whether you will buy an AI solution of develop your own. Buying an AI solution from experts offers advantages such as quick implementation, proven algorithms and a user-friendly interface. It is important to carefully weigh up the make-or-buy decision and consider the specific requirements and resources of the company.