Innovating Responsibly: An Interview with Rahul Garg on AI and digital threads in industrial machinery

By Kathryn Gerardino-Elagio

At Realize LIVE in Las Vegas, Nevada, International Metalworking News for Asia (IMNA) had the opportunity to interview Rahul Garg, Vice President of the Industrial Machinery Industry at Siemens Digital Industries Software. During the conversation, Rahul shared valuable insights into the transformative role of AI in predictive maintenance, the ethical strategies Siemens employs for AI deployment, and the ways digital threads are enhancing collaboration and efficiency in industrial machinery. As industry leaders gather to discuss the latest technological advancements, Rahul's expertise provides a compelling look at how Siemens is driving innovation and ensuring responsible AI integration in the industrial sector.

IMNA: How do digital threads enhance the collaboration and efficiency of product, life cycle management and industrial machinery.

Rahul Garg: Digital threads significantly enhance the collaboration and efficiency of product lifecycle management and industrial machinery. Here's how:

In the realm of industrial machinery, customers typically manufacture complex systems like components, gears, motors, pumps, and printing machines. These systems often require diverse skills, such as mechanical engineering, electrical engineering, automation engineering, and manufacturing engineering. Each expert focuses on their specialised area, and digital threads play a crucial role in connecting these experts and their work seamlessly.

For instance, consider the process of designing a machine with a motor. A mechanical engineer needs to determine how to place the motor, considering its physical dimensions, weight, and heat generation. Meanwhile, an electrical engineer focuses on powering the motor, ensuring the electrical supply is adequate and doesn’t interfere with other components. An automation engineer programs the motor for specific operations, such as starting, stopping, and adjusting speed based on sensors.

Digital threads facilitate the sharing of information among these experts. If the mechanical engineer makes a change to the motor’s placement, this information is easily communicated to the electrical and automation engineers. Similarly, if a sourcing buyer finds a new, cost-effective motor supplier, all relevant team members are promptly informed to evaluate the impact on electrical requirements, weight, and other factors.

Furthermore, digital threads extend their benefits to the sales process, especially in industrial machinery where custom orders are common. They connect salespeople with designers, mechanical engineers, and manufacturing teams, ensuring that customer requests and requirements are accurately communicated and implemented. This integration improves the efficiency of the engineering work required during the sales process.

Overall, digital threads facilitate a smoother digital transformation within a company. By connecting various departments and ensuring seamless information flow, they enhance the adoption of digitalisation capabilities, providing a clear roadmap for companies to mature their digital processes and improve overall efficiency.

IMNA: Could you discuss the role of AI in a predictive maintenance and its impact on the machinery of industry?

Rahul Garg: AI plays a crucial role in predictive maintenance and significantly impacts industrial machinery. AI can be leveraged in two key areas: the front-end engineering and design process, and the operational phase of machinery.

Front-End Engineering and Design: AI assists in the engineering design process by acting as a co-pilot. Through our partnership with Microsoft, we've introduced an AI co-pilot that aids in front-end programming, designing, and engineering. This co-pilot can generate preliminary designs, automate code creation, and offer new ideas that engineers can refine further. The term "co-pilot" highlights that AI is not taking over the work but assisting engineers, enhancing their efficiency and creativity.

Operations and Maintenance: In the operational phase, AI enables real-time monitoring and predictive maintenance. By using AI, machinery manufacturers can monitor equipment performance daily. AI helps in identifying potential issues before they become critical, answering questions like:

• How did my machine perform yesterday?
• How many units did it produce?
• Were there any problems, and if so, when and where did they occur?
• If a robot went down, how and why did it happen?
• Do I have similar robots that might face the same issue?
• Who is the supplier, and how can I contact them?

These capabilities allow for human-like interaction with the system, making it easier to diagnose and address problems promptly. Consequently, this improves overall machinery efficiency, reduces downtime, and enhances the effectiveness of maintenance operations.

In summary, AI enhances both the design and operational phases of industrial machinery, facilitating predictive maintenance, reducing downtime, and increasing overall efficiency.

IMNA: So, another question for AI, what strategies does Siemens employ to ensure that the ethical use of AI in industrial opportunities?

Rahul Garg: That's an excellent question. Ensuring the ethical use of AI in industrial opportunities is a priority for Siemens. Here are the strategies we employ:

AI as a Support Tool: We see AI as an enhancement tool, designed to support and augment human capabilities rather than replace them. By making processes faster and more efficient, AI helps improve overall usage without fundamentally changing how tasks are performed.

Ethical Guidelines and Frameworks: Siemens adheres to strict ethical guidelines and frameworks for AI development and deployment. These guidelines ensure that AI is used responsibly and transparently, minimising risks and maximising benefits.

Continuous Monitoring and Adaptation: AI is a rapidly evolving field, and we are committed to continuously monitoring its impact and adapting our strategies accordingly. We ensure that AI technologies are developed and used in ways that are consistent with our ethical standards.

Workforce Considerations: We are mindful of the impact of AI on the workforce. Our aim is to enhance workers' skills and provide tools that help them perform their jobs more effectively. We focus on retraining and upskilling employees to ensure they can work alongside AI technologies.

Collaborative Approach: Siemens collaborates with partners, including tech giants like Microsoft, to ensure that AI technologies are developed responsibly. This collaboration helps us stay at the forefront of ethical AI deployment and leverage the best practices in the industry.

Transparency and Communication: We maintain transparency in our AI initiatives, openly communicating the purpose, capabilities, and limitations of AI systems. This helps build trust with our customers and stakeholders.

Siemens is dedicated to the ethical use of AI by seeing it as a supportive tool, adhering to ethical guidelines, continuously monitoring, and adapting strategies, considering workforce impacts, collaborating with partners, and maintaining transparency.