Machinery-as-a-Service The Future of Industrial Use

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Siemens AG

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How companies procure and operate machines is changing rapidly. What keeps coming up in this context is Machinery-as-a-Service (MaaS). The MaaS model promises more flexibility, lower investment costs, and data-driven efficiency.

In an increasingly digitized world, the way companies procure and operate machines is also changing. A model gaining importance in this context is Machinery-as-a-Service (MaaS). It has the potential to sustainably transform the industrial landscape. MaaS means that machines are not purchased but used as a service. The provider remains the owner of the machine and makes it available to customers for a usage-based fee. The usage fees can be calculated based on operating hours, the number of units produced, or other performance metrics. This makes the use of machines more flexible and scalable. The foundation of MaaS lies in the increasing connectivity of industrial facilities through the Internet of Things (IoT). Sensors continuously collect operational data, which is processed and analyzed via cloud platforms. This is complemented by AI and machine learning, which identify patterns, create forecasts, and reveal optimization potential. This enables precise control, transparent billing, and intelligent maintenance.

Predictive Maintenance

A key component of Machinery-as-a-Service is the so-called Predictive Maintenance. It utilizes modern technologies such as sensors, IoT, big data, and AI to monitor the condition of machines in real time and proactively plan maintenance measures. Unplanned downtimes can be avoided, the lifespan of machines extended, and operating costs reduced. Sensors continuously collect data such as temperature, vibration, pressure, power consumption, or runtime. This data is transmitted to the cloud and analyzed there. Using machine learning algorithms, patterns are identified that indicate upcoming issues, such as wear, material fatigue, or anomalies in operational behavior. For instance, if a production machine exhibits unusual vibrations, the system can recommend maintenance at an early stage before a breakdown occurs. Additional advantages of Predictive Maintenance include:

• Maximum availability: Machines are less frequently out of operation, which increases productivity.
• Efficient resource utilization: Maintenance is carried out as needed, not at fixed intervals.
• Cost reduction: Fewer unplanned repairs, lower spare part consumption, reduced downtime.
• Planning reliability: Maintenance measures can be scheduled during low-production periods.
• Sustainability: Targeted maintenance extends the lifespan of components, conserving resources.

Strategic Importance

In modern MaaS models, Predictive Maintenance is often directly integrated into the platform. Customers gain insights into the condition of their machines, maintenance recommendations, and even automated service bookings through dashboards. Providers, on the other hand, use the data to further develop their machines, optimize spare parts supply, and enhance their service quality. Strategic advantages include:

• Competitive advantage: Companies with high equipment availability and low maintenance costs are more efficient and flexible.
• Customer retention: Providers who prevent failures instead of merely reacting build trust and long-term partnerships.
• Data-driven business models: The collected operational data can be used for new services, benchmarks, or AI-supported optimizations.

MaaS in Use

Typical application areas of MaaS depend on the industry. While in manufacturing, machines like CNC machines, industrial robots, or packaging systems are offered as a service, in logistics, it could be conveyor technology or autonomous vehicles. However, in other areas, machines such as cranes, excavators, or filling and packaging machines with high hygiene relevance can also be offered in a MaaS model. Advantages of MaaS include:

• Financial benefits: Predictable expenses and usage-based billing.
• Technological up-to-dateness: Regular maintenance and updates from the provider.
• Data-based optimization: Greater efficiency and availability through real operational data.
• Scalability: Flexible addition or reduction depending on production needs.
• Long-term business relationships: Recurring revenues and stronger customer loyalty.
• Service orientation: Providers become comprehensive solution providers.

Challenges of MaaS

• Data security: The exchange of sensitive operational data requires high security standards.
• Contract design: Clear regulations on liability, availability, and service levels are essential.
• Cultural change: The shift from ownership to usage for a fee requires a change in mindset in many companies.
• Integration into existing systems: MaaS must be seamlessly integrated into ERP, MES, and other IT systems.

Future of Industrial Digitalization

The combination of MaaS and Predictive Maintenance exemplifies the next stage of industrial digitization. In the future, machines could autonomously request maintenance, order spare parts, or optimize themselves. The integration with Digital Twins—virtual replicas of machines—will also open up new opportunities, such as for simulations, training, or remote maintenance. Digital platforms like Siemens Xcelerator can facilitate the transition to digitization by supporting MaaS models, for example. MaaS is more than just a new business model. In combination with Predictive Maintenance, it creates a powerful, transparent, and sustainable system that benefits both customers and providers. MaaS represents a future where machines are not just tools but intelligent, connected service providers—flexible, efficient, and data-driven.

*Andreas Dettmer is Head of Business Development Central Europe for Siemens Senseye Predictive Maintenance.

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