.png)
As part of the SPARETECH Summit 2024, leading experts exchanged views on the current status and future potential of AI in industry and maintenance. The panel discussion offered insights from:
- Dr. Thomas Heller (Smart Maintenance Community Managing Director | Fraunhofer IML)
- Bernd Zenk (Senior Specialist & Team Coordinator Global Maintenance | Schaeffler)
- Stefan Heinkel (Project Engineer CU Maintenance Global | ElringKlinger)
- Manuel Lehmann (Group Leader Production Planning | Bosch)
- Lukas Biedermann (Moderator | SPARETECH)
The experts discussed the opportunities and challenges of integrating AI into maintenance processes. They emphasized the essential role of curated data, the initial hurdles to adopting AI in organizations, and the strategic steps needed to make it successful.
The following is the most important in brief.
Where do industry and maintenance stand today when it comes to AI?
The world is turning faster and faster and with this acceleration comes the urgent need for companies to react quickly. Machines need to be back up and running quickly after a breakdown, and the implementation of AI technologies offers promising solutions to meet these needs. AI is used for tasks such as classification, anomaly detection, and criticality assessment, which are essential to maintaining smooth industrial operations.
Currently, the application of AI in maintenance varies significantly between companies. Those who have well-established maintenance processes and efficient spare parts management see AI as the next logical step in their digital transformation journey. These forward-thinking companies are already investing in digital solutions, and AI is a powerful tool to further enhance their capabilities.
"I clearly see it as an opportunity where we are really still at the beginning. We are starting to work with it and have to make sure that we start in the right place."
Manuel Lehmann
What is the practical problem?
A critical point that hinders the effective implementation of AI in maintenance is the state of spare parts data. Clean and well-structured starting data for spare parts offers an optimal basis for further digitization efforts. This data transparency is crucial for conducting valuable analyses and evaluations and is indispensable for the use of AI technologies.
Several challenges complicate the adoption of AI technology:
- Initial effort: The process of digitizing spare parts, especially for new systems, requires a significant initial effort.
- Data protection and copyrights: Managing data and securing the necessary rights for AI training data is another layer of complexity.
- Lack of in-house expertise and resources: Not all companies have dedicated teams and experts who can focus on the use and development of AI or train the AI tools for their specific needs.
What should happen next?
While there are challenges, there are also opportunities for growth and innovation in the field of AI for spare parts management. A strategic approach is needed to ensure that AI developments are effectively integrated into day-to-day operations, especially at the management level. Key steps include:
- Develop an openness to new ideas: Cultivating a culture that embraces innovation and new technological solutions is crucial.
- Start small: Instead of trying to overhaul entire systems at once, start AI projects with small budgets and manageable subprojects. This allows for testing, learning, and incremental improvement.
- Clear communication: It is important to clearly communicate the importance and value of maintenance and efficient processes. This helps to gain the support and understanding of all stakeholders involved.
"We should not only focus on technical solutions but also effectively demonstrate our value [of maintenance] within the organization. We can achieve a lot if we take this discussion as a motivation to collaborate across departments, focus on holistic KPIs, and effectively market our strengths."
Dr. Lukas Biedermann
