Choosing between building your own AI solution or buying a proven one can define your success in predictive maintenance. But without a clear strategy, companies risk high costs, long development times, and missed opportunities.
In this guide you will discover:
✅ How to make informed Make-or-buy decisions in an industrial environment
✅ The specific decision criteria for software and AI applications
✅ If standardised AI Apps are a solution for OEMs and End Users
Download now and take the first step toward smarter, AI-powered maintenance decisions
In contrast to general AI, AI in manufacturing focuses on specific application areas such as quality control, predictive maintenance, process optimization and robotics. AI in manufacturing refers to the use of algorithms and machine learning to automate complex tasks, identify patterns, and make predictions. This involves analyzing and processing large volumes of data from machines and systems.
But why should companies leverage AI in manufacturing?
One example is that anomalies in a machine can be detected in real time by analyzing their data. By using AI in the industry, you can enable predictive maintenance, ensure product quality (predictive quality), and utilize resources more efficiently (predictive energy).
Machine learning (ML) is the technological backbone of AI in manufacturing. Unlike rule-based systems, ML models learn from historical and real-time production data and continuously improve their predictions. This makes them particularly valuable in dynamic environments where processes and conditions change frequently.
Typical applications include predictive maintenance, where algorithms detect wear patterns in machines before failures occur, and quality control, where computer vision systems identify defects more quickly and reliably than manual inspections. ML is also used for process optimization – for example, adjusting parameters in real time to reduce energy consumption or stabilize production quality. Another promising field is supply chain optimization, where ML analyzes demand fluctuations, delivery times, and material availability to create more resilient and cost-efficient processes.
The strength of machine learning lies in its scalability: once trained, models can be applied across multiple plants or production lines, enabling companies to standardize best practices globally. At the same time, the algorithms adapt to local conditions and continue to learn with every new dataset.
For companies, this means not only fewer downtimes and lower costs, but also higher flexibility and competitiveness. The integration of ML into production systems is therefore a decisive step towards truly data-driven manufacturing.
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.
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.