As cutting-edge companies seek ways to improve internal processes and gain competitive advantage, they’re increasingly examining the business implications of digital twins. The digital twin is a virtual representation of physical assets, processes and systems that mimics changes as they occur in an actual physical system. The most common applications today are found in industrial settings, in which companies like General Electrics use digital twins of jet engine components to predict maintenance schedules and life expectancy. Doing so enables the company to manage engines to deliver the highest possible profit and performance, while also ensuring passenger safety.

The key value proposition to digital twins is in their ability to combine real-time data, physical dependency models and intelligence from different platforms to simulate, predict and improve assets and E2E processes. According to a recent Deloitte report the global market for Digital Twin technologies is expected to grow to $16 billion by 2023, while technologies used for digital twins – IoT and machine learning, for example – are predicted to almost double by 2020.

Further underpinning the importance of the technology, vendors like IBM, Oracle and SAP have launched product portfolios around data twins. Such vendor support, coupled with increasing numbers of implementation scenarios, is driving down the cost of digital twins, while also increasing its applicability, as organizations recognize it diversity and appeal. As such, digital twins now are used for applications in retail, healthcare and the public sector to model a variety of processes within companies, including overall product lifecycle management, network planning and design, and the deployment and management of remote resources.

Making the Most of a Digital Twin

As organizations consider ways in which digital twins can be integrated, it’s imperative to include as many data points as possible. Digital twin development should include asset lifecycle data from product designs, production, sales and marketing, supply chain, operations, services and finance. Additionally, data twins should incorporate real-time information between manufacturers, service partners, customers, OEM partners, regulators and other ecosystems. The successful digital twin will gather information from both cloud and on-premise assets, including cloud IoT integration, enterprise resource planning (ERP) business suites, data management systems, collaboration asset platforms and digital innovation portfolios.

When the digital twin incorporates data from these diverse data points, it creates a myriad of potential benefits, including:

  • The ability to test changes to processes before they’re implemented
  • Data-driven decision making
  • Collaboration with both internal and external ecosystems
  • Development of new business models and improvements for existing business models
  • Improvements to customer experience

Test-driving Potential Changes

Digital twins enable organizations to test scenarios, changes or updates to business processes before they’re implemented. This “try before you buy” capability can pinpoint areas within an organization that would benefit from automation. As such, enterprises gain the ability to walk through multiple scenarios prior to making a decision that could impact business processes, operations and employees. Such modeling also affords companies the ability to innovate and evolve without disrupting day-to-day operations, while also reducing the complexity and inefficiencies of existing models.

Data-driven Decision Making

Digital twins promote data-driven decisions by building a comprehensive, virtual representation of a company’s processes. With the right data points, digital twins provide a business-level view that can be used to measure and analyze operations across an entire enterprise. This end-to-end visibility enables organizations to understand strengths and weaknesses of its associated processes. With such knowledge, organizations can simulate alternative approaches and restructure entire processes based on hard data, as opposed to making assumptions based on generalized expectations.

Innovations Throughout Ecosystems

Organizations that incorporate data from numerous ecosystems will find that it ignites innovation, as ecosystems are armed with the necessary data to build next-generation products or manufacturing processes. Likewise, the digital twin can spur innovation from secondary ecosystems, incorporating external assets and technologies from different vendors. Information from an organization’s digital twin can be made available to its ecosystem of partners, enabling them to innovate and improve the design and performance of their products or services. This creates secondary and tertiary systems of data around which companies can base decisions.

Product Improvement and Creation

Digital twin models enable product designers to prototype new ideas quickly and inexpensively. Product improvement is achieved by simulating what-if scenarios involving system interactions, product testing and customer experience. Predictive analytics from digital twins creates a better understanding of how an enterprise can meet the needs of its customers through new product and service development, as well as providing insight for ways to improve service after sales. By studying the digital twin under actual working conditions, companies can see a product in action, enabling engineers to make more informed choices during the design process and use digital twins to make their simulations more accurate.

In addition to future innovation and product development for designers and engineers, digital twins build a stronger relationship between engineering and operations teams. Operations teams use the data to optimize performance, service and maintenance over the lifetime of a product, enabling organizations to avoid costly downtime, repairs, replacements and future performance issues.

Create customer value for life

The overarching result of data twins is that organizations can create customer value and satisfaction throughout the entire lifecycle of its products and services. Customer experience is improved through collaboration, as well as the creation of customized and individual product and service offerings. Industry stakeholders expect lifetime value, especially for capital-intensive products. Digital twins can be used to evaluate and deploy new operating methods that weren’t necessarily a part of the original value proposition, thereby providing new services to customers over an asset’s lifetime.