Digital twin

Realize predictive maintenance, lay the foundation for future-proof Everything-as-a-Service (XaaS) business models and increase the sustainability of your business


Value retention through maintenance


XaaS business models


Circular economy

What is a digital twin?

The digital twin is the digital image of a real object, for example a machine. This image is enriched with all available information − master data and live data of the real object − over its entire life cycle so that it corresponds to the real object in terms of its properties and behavior. The digital twin connects the real world and the digital world in the sense of industry 4.0. On the basis of the virtual image, valuable insights can be gained about the machine and various competitive advantages can be achieved.




  • Establishment of an end-to-end maintenance strategy (from planning to execution)

  • Shifting from reactive to predictive maintenance (predictive maintenance)

  • Significant increase in machine reliability

  • Reduction of machine failures, downtime and associated costs

  • Creation of failure forecasts

  • Realization of new business and payment models (Everything-as-a-Service, pay-per-use)

  • Strengthening sustainability according to the circular economy approach

  • Implementation of optimizations in design, construction and operation

The digital twin enables a data-based view of the entire life cycle of a machine or plant. This results in the advantage that the machine or plant can be optimized holistically with regard to planning, product design, construction, maintenance and operation. In addition, the digital twin also offers the opportunity for predictive maintenance, XaaS business models and a strengthening of sustainability.

Use cases for the digital twin

Companies operate plants in order to generate added value. For this to succeed, they are interested in high reliability and continuous availability of the plants as well as low downtimes and the highest possible capacity utilization. These aspects are also relevant for the manufacturer, as they represent important sales arguments to customers.

Predictive maintenance helps to ensure the maximum service life of machines and plants and to safeguard their performance in the long term. The digital twin forms the basis for predictive maintenance, as it can be used to identify signs of wear and any problems before failure occurs. Early intervention and replacement of the affected components minimize unplanned downtime in production.

Predicitve Maintenance | Digitaler Zwilling | IBsolution

The digital twin and the Internet of Things create the conditions for innovative Everything-as-a-Service business models. Here, machines and systems are not offered as a product but as a service for a certain period of time − including maintenance, repair, etc. The user has the advantage that he no longer has to buy the machine but can rent or subscribe to its use. This means that there are no high upfront costs for the purchase.

This is also accompanied by new billing and payment models such as pay-per-use. Only the operating costs based on consumption are billed. This means that users only pay for what they actually use. The manufacturer benefits twice over: on the one hand, it gains predictable and renewable sources of income, and on the other, it gains valuable insights into customer usage patterns. This in turn creates opportunities to increase sales through targeted cross-selling and upselling.

As-a-Service-Geschäftsmodelle | Digitaler Zwilling | IBsolution

In terms of the circular economy, the digital twin also plays a crucial role. The circular economy pursues the goal of using existing materials, products and machines for as long as possible, thereby extending their life cycle. The highest value retention is achieved by regular and timely maintenance of machines, as this allows them to be used particularly efficiently and for a long period of time..

Nachhaltigkeit | Digitaler Zwilling | IBsolution

Cross-company collaboration

The digital twin not only networks machines, but also offers the opportunity for closer collaboration between manufacturers, customers and service providers. If all parties involved access a uniform system in plant management and share the various data with each other, this has a positive effect for manufacturers, customers and service providers alike.


  • Closer ties with customers

  • Exchange across all assets

  • Basis for digital business models

  • Digital twin of their own plants

  • Information on instructions, maintenance plans, etc. directly from the manufacturer

  • Simplified spare parts management

  • Predictive maintenance for own operations
Service provider:
  • Central know-how

  • Optimized information flow

  • Predictive maintenance for the customer

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Dominic Lösch

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The matching software from SAP


For the implementation of the digital twin, SAP has different software solutions that develop their respective strengths in interaction. SAP Intelligent Asset Management (IAM) is a cloud application for industry 4.0. that is made up of the components SAP Business Network for Asset Management (former SAP AIN) and SAP Asset Performance Management (APM). SAP APM was created by merging the formerly standalone SAP Asset Strategy and Performance Management (ASPM) and SAP Predictive Asset Insights (PAI) solutions. The SAP IAM applications are to be understood as a supplement and enhancement for existing asset and maintenance applications and do not claim to replace the existing solutions.

SAP Business Network for Asset Management (former SAP AIN)

  • Focus on network and data exchange

  • Cross-company approach

  • Synchronization with SAP S/4HANA is possible

  • However, SAP Business Network for Asset Management can also be used independently of SAP S/4HANA

  • Breaking up of data silos

  • Basis for a customer portal

  • Connection to SAP C4C solutions (SAP Service Cloud, SAP Commerce Cloud) is possible

SAP Asset Performance Management (APM)

  • Extension of SAP Enterprise Asset Management (EAM) in SAP S/4HANA

  • Closed-loop integration with SAP S/4HANA

  • End-to-end maintenance processes (from strategy to execution) in interaction with SAP S/4HANA EAM

  • Increased reliability of assets

  • Predictive maintenance instead of reactive maintenance

  • Risk assessment

  • Asset health optimization

  • Development of maintenance strategies tailored to requirements

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Would you like to learn more about how you can use the digital twin to optimize maintenance, realize XaaS business models, and operate more sustainably?

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