Using artificial intelligence correctly
Optimizing entrepreneurial action with artificial intelligence
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.
A total of six blog articles will guide you through the digital transformation of the manufacturing industry. We use an IIoT maturity model to explain how production companies can increase their digital maturity. You will find links to the blog articles that build on each other at the bottom of each page.
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.
The authors
Johannes Fuhrmann is Head of Strategic Business Development at Arvato Systems. In this role, he is responsible for the portfolio and product development for the manufacturing industry. His focus is mainly on the topics of digital twin, digital administration shell and the development of shared data ecosystems in the industry. Prior to his career at Arvato Systems, Johannes Fuhrmann held various relevant positions within the manufacturing industry. For example, he worked as a Senior Consultant for Industry 4.0 at Deloitte Consulting and as Head of Digital Operations and Business Development at VELUX. Johannes Fuhrmann completed his studies at the University of Warwick with a Master of Science (MSc.) in Information Systems Management. He also holds a Bachelor of Arts (B.A.) in Technical Business Administration from the Hamburg University of Applied Sciences (HAW Hamburg).
Konstantin Klein is Sales Growth Manager at Arvato Systems and responsible for customers in the manufacturing industry. In his position, his focus is on the digital transformation of companies, particularly in the areas of process optimization, digital twin, and cyber-physical systems. Before joining Arvato Systems, Konstantin Klein gained extensive experience in discrete automation as Product Manager Industrial IoT Network Solutions at B&R Industrial Automation (Member of the ABB Group) and as Business Development Manager at TTTech Industrial. After completing his schooling and training as an electrician, he completed a Master's degree with a Master of Science (M.Sc.) in electrical engineering and information technology with a focus on automation technology at the Leipzig University of Applied Sciences.
Written by
