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This blog is the second part of a blog series covering digital twins. In the first part, I have described digital twins and explained how the concept creates business value for connected assets and connected products. Now, let’s look at the implementation approach.

Digital twin implementation concept

The actual implementation of a digital twin depends on the intended business outcome and sophistication of business logic. For most connected products and connected assets scenarios described above, the following capabilities and integration scenarios are required:

  • Twin-to-device integration The physical object needs to be securely connected and managed. Onboarding established a relationship to an instance. This may happen before installation (e.g. during configuration or production) or after installation (in a two-phased approach with certificates being pre-installed earlier). Streams or batches of live data often require protocol conversion, semantic mapping and transformation before being ingested into a big data store infrastructure. This allows to query object state and historic information captured as time series
  • Twin-to-twin integration As an optional component, an integration to a digital twin managed by a service provider (e.g. a telematics vendor) or by a supplier (e.g. by an automation equipment vendor) may be needed if the physical object is not managed by the provider of the digital twin.
  • Twin-to-system-of-record integration Integration with business information and engineering systems provides essential context along the lifecycle of the physical object
    • PLM for engineering bill of material, components and spare parts, software versioning (for embedded systems)
    • CAD/CAM/CAE for 2D and 3D models, layouts, assembly information
    • Manufacturing systems for product traceability, serialization, manufacturing bill of material
    • ERP for product variants, financial information (e.g. depreciation), equipment and spare parts inventory
    • ERP/CRM and supplier networks for service contracts, business partners and roles, SLAs
  • Twin-to-system-of-intelligence integration Most digital twins are not consumed directly by end users, but interact with systems of intelligence through events and notifications while exposing condition monitoring and historic information; rule handling, data science algorithms, and machine learning create insight from streams of live data (e.g. anomaly detection, issue segmentation, health scores) and provide predictions on future state (e.g. remaining lifetime, time of arrival forecasts)

The digital twin implementation will largely be managed from the cloud to facilitate the network-centric engagement models described above. However, most scenarios will distribute actual data and algorithms between an edge or gateway implementation (located on or near the physical object) and the cloud in a distributed architecture. Learning and model development are primary functions in the cloud, however, not all data is relevant to be transmitted. In many cases, only change information and events will be sent into the cloud as a stream while data locally and temporally persisted can be replicated to resolve underlying issues and to evolve algorithms.

SAP Leonardo portfolio for digital twin

With SAP Leonardo, SAP has launched a complete portfolio to enable the vision of digital twin in a live and networked business ecosystem. The main SAP capabilities of a hybrid solution architecture with components at the edge, in the cloud, and in relevant business and engineering systems are shown here:

SAP Leonardo IoT platform and IoT Edge

Extending SAP Cloud Platform into a comprehensive IoT platform, SAP Leonardo IoT platform services provide digital twin modeling (thing modeler), device management, connectivity, messaging and data ingestion, time series and event storage and archiving, and APIs as the foundation of the digital twin implementation; a corresponding SAP Leonardo IoT Edge component delivers IoT gateway capabilities and local connectivity as well as edge persistence, rules, and streaming analytics.

The SAP Leonardo IoT platform is composed of SAP Cloud Platform Internet of Things and SAP IoT Application Enablement. Edge functionality is delivered via the IoT Gateway component of SAP Cloud Platform Internet of Things and SAP Edge Services for additional persistence and streaming analytics at the edge (see also here).

A great starting point is also the IoT community. The IoT platform will be a focus area at SAP TechEd. Follow this agenda to get started.

SAP Predictive Maintenance and Services (PdMS)

Built on top of the SAP Leonardo IoT platform, SAP PdMS uses sophisticated predictive models to detect anomalies, calculate asset-specific health scores and remaining lifetimes, predict failures, and provide a decision support basis for maintenance schedulers.

An extension is SAP Leonardo Machine Learning. Again, TechEd is a great learning opportunity.

SAP Digital Twin for structural dynamics (planned, based on former FEDEM)

Dynamic structural analysis for physics-based modeling of digital twin provides a sophisticated digital twin. See this video from SAPPHIRE 2017.

SAP Asset Intelligence Network (AIN)

As a cornerstone of the asset network, SAP AIN serves as the shared asset repository and collaboration platform for all business partners during the lifecycle of assets; deeply integrated with the digital twin exposed through SAP Leonardo Foundation services, AIN not only keeps all network participants updated from a single source, but also allows a fine-granular authorization management for collaboration scenarios around the digital twin.

SAP Vehicle Insights

SAP Vehicle Insights is a cloud-based solution to realize a variety of scenarios and new business models for any type of moving assets and connected cars. It empowers fleet decision makers by correlating car fleet telematics data with geo data and business information on a large scale. SAP Vehicle Insights allows to monitor live vehicle conditions and failures to support data-driven remote diagnostic scenarios, and it enables more precise usage-based services and pay-as-you-drive contracting.

SAP Connected Goods

SAP Connected Goods is a cloud-based solution, designed to maximize the value of mass market devices (such as coolers, vending machines, or power tools) through remote monitoring, management and central control. SAP Connected Goods helps enterprises to reduce operational costs, increase revenue, and improve customer satisfaction. At the same time, it allows enterprises to avoid lost sales and missed revenue opportunities.

SAP Hybris Service Cloud

SAP Hybris Service Cloud manages the entire service engagement. Issues created by notifications from digital twins are efficiently resolved while keeping all participants informed across multiple channels.

SAP Asset Manager and SAP Work Manager

The SAP Asset Manager and SAP Work Manager mobile apps provides all information to service technicians and repair teams to efficiently install, inspect, maintain, and repair assets in the field.

SAP 3D Visual Enterprise

SAP 3D Visual Enterprise Solutions combine visual product, plant and process information with textual business content to provide an a combination of traditional business information with fully interactive visuals.

Conclusions

Implementing a connected assets or product strategy with a digital twin as the foundation for live business processes and innovative asset- and product-centric business models is a core requirement in every digital transformation agenda for industrial goods manufacturers and asset operators. The starting point for such a journey certainly varies as the immediate business value may be highest in after-sales service expansion for manufacturers while operators may focus on predictive maintenance or asset risk and performance management. SAP Leonardo provides a comprehensive portfolio of software solutions to create tangible business outcome by implementing digital twins.

Do you want to learn more on how to implement digital twins? Attend SAP TechEd and join our sessions on SAP Leonardo and the Internet of Things.

 

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