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Using artificial intelligence correctly

Optimizing entrepreneurial action with artificial intelligence

Exploiting the potential of artificial intelligence in a targeted manner
19.12.2023
Artificial Intelligence
Digital Transformation
Data Management

In the previous article, we highlighted the fundamental added value of software and applications based on IIoT data. Building on this, this final blog article in our IIoT Maturity series focuses on integrating and using artificial intelligence in the existing software landscape. This includes various algorithms, machine learning models, and data mining strategies.

Use of artificial intelligence for machine data evaluation

In general, artificial intelligence can take machine data analysis to a new level. Algorithms from various areas of artificial intelligence and data mining make it possible to analyze complex patterns and anomalies in the data. This can lead to more precise performance analyses and better predictive maintenance. On the one hand, improved content analysis enables the company to offer more precise customer-oriented services. On the other hand, customized products also benefit. The individual points are discussed in more detail below.


Predictive maintenance and optimized operating processes based on AI

Knowledge of possible breakdowns is essential for production operations. Predictive maintenance solutions are being developed for this purpose, which in turn rely on data analysis and algorithms to monitor the machines' condition continuously. Real-time data and historical patterns are used to predict at an early stage when potential causes of failure will occur and when maintenance is generally required. As a result of this information, maintenance work can be planned and carried out in a targeted manner. This minimizes unplanned and optimizes planned downtimes. As a result, costs are entirely reduced.

 

In addition, all the operational processes in a company's production also benefit. The use of artificial intelligence helps to optimize processes in real-time. By analyzing large amounts of data, bottlenecks, and inefficient processes can be identified and improved in a more targeted manner. The AI applications can react to certain data-driven situations and suggest adjustments or, if necessary, carry them out automatically and independently. Accordingly, the operational goal is to ensure smooth production. Consequently, this is accompanied by an increase in the productivity of operational processes through their optimization.

 

Therefore, using artificial intelligence algorithms in predictive maintenance and optimizing operating processes is diverse and must always be explicitly considered in context. Companies are in a new position to reduce costs and improve operational machine management and product quality.

 

Customer-oriented services

The use of artificial intelligence enables companies to understand the needs of their customers better, develop individual offers, and create a personalized customer experience. On the one hand, this means that key products can be better adapted to customer expectations. On the other hand, the data-based services that are based around these products can be tailored to the individual customer. With an ever-increasing data basis, other business models are also conceivable in the future, which could lead to new products or service models.
In terms of the customer experience, analyzing large amounts of customer data helps identify customer behavior and preferences. AI applications can automatically generate recommendations and suggestions, process customer inquiries as quickly as possible, and enable service processes such as real-time service support. Overall, data-driven optimization with AI increases customer satisfaction and loyalty, which delivers sustainable added value for the company.

 

In conclusion, AI can result in the continuous improvement of products and services. Innovations and new functions can be derived and developed through this data's constant aggregation and evaluation process. Integration into the IIoT structure considerably expands the possibilities and significantly adds customer value. This project requires the successful implementation of the previous phases of the IIoT maturity model but simultaneously opens the door to an even more profound data-driven transformation. This will make companies in the manufacturing industry more efficient, more resilient, and also more competitive. Therefore, the use of AI technologies is an essential step into the future of the manufacturing industry.

Written by

Fuhrmann, Johannes
Johannes Fuhrmann
Head of Strategic Business Development
ArvatoSystems_MA_Konstantin Klein
Konstantin Klein
Business Development Manager at TTTech Industrial Automation AG