IoT presents many advantages to organizations seeking competitive differentiation. Two unique advantages pivot around the availability of new types of sensing devices that can be added to most types of industrial gear, and the ability to collect near-real-time data from equipment with edge analytics.

The IoT is also enabling the concept of the digital twin, in which a digital replica of physical assets, processes and systems is created. The digital twin concept enables organizations to better understand, predict and optimize the performance of its installed assets.

A digital twin helps manufacturers avoid costly product quality issues by generating “what if” scenarios using stochastic simulations, thus reducing time-to-market and improving throughput. Using a digital twin, years of equipment usage can be simulated in a fraction of the time. The advantages of embracing the concept of the digital twin are multifold; however, organizations must first resolve a few questions before jumping headlong into the fusing of the physical and digital worlds.

If you are exploring a digital twin concept, consider 5 leading practices to get you started:

1. Assess Process & Technology Maturity.

A digital twin relies on the availability of complete information for fault analysis or prognosis to deliver precise predictive foresights. Non-availability of information from any of the data sources—such as field measurements, quality inspection reports, customer feedback, etc.—detracts from digital twin accuracy.

A well-defined data process ensures that data is generated and stored at the source. When coupled with the technology, the stored data can be shared across organizational boundaries. An assessment survey is devised with the key parameters of process, technology, governance and people to understand the maturity and readiness of the organization.

The real benefits of the digital twin concept become evident only when departmental data is integrated. This typically means sourcing quality data from business planning systems (ERP, PLM, SCM) and manufacturing operations management systems (MES, LIMS, CMMS). In our framework, lower technology maturity means an organization is struggling with data integration and data sourcing challenges. They also suffer from a lack of documentation and non-standardized processes because data isn’t regularly shared but is localized. These organizations can’t consolidate the information necessary to create a picture of all possible operational failures and will be unable to determine the best strategies to tackle critical situations or to leverage data for competitive advantage.

2. Design with the Right Building Blocks.

The concept of a digital twin, as first defined by Dr. Michael Grieves in 2003, consists of:

· Concrete products in the physical space.

· Virtual products in the digital space.

· Connected data that tie the physical and digital together.

Think about this concept as an evolving digital profile of the physical asset that captures its past and current behavior to provide clues about its future behavior. The digital twin concept is built on large amounts of cumulative and real-time operational data measurements across multiple physical world dimensions. These measurements can help create an ever-evolving digital profile of the asset that may provide vital inputs on system or business performance leading to actionable value.

3. Manage Design Data among Supply Chain Partners.

To realize the true value of digital twin requires a comprehensive approach to collect, manage and manipulate the product’s digital data. Close integration among partners and suppliers is essential to ensure that the digital twin accurately maintains digital and physical configurations. So as the physical product evolves, managing the design data for creating a digital twin among partners and suppliers becomes an ever-growing challenge.

4. Choose an Optimal Level for Detailing the Digital Twin.

One major implementation challenge is gauging the optimal level of detail that is needed. If the digital twin is basic and simple, then it might not yield the expected value. If a broader approach is taken, however, then the ensuring complexity could derail the project. It is imperative to start with a basic, simple model of a digital twin and continuously add the necessary inputs and analytics as needs emerge.

5. Implementing a Digital Twin.

Organization and technological maturity are not enough to guarantee digital twin success. If the model is not flexible enough, is incorrectly built, or serves only a single purpose, then it will become obsolete over time and severely undermine the investment in building it. To avoid such mistakes and build a truly dynamic digital twin that can deliver the promised value, don’t forget these critical practices:

· Gain participation across the product value chain.

· Create and modify standard & healthy practices for business models.

· Collect data from multiple sources.

· Ensure long-access lifecycles.

· Measure Success to demonstrate proof of value.

The digital twin concept is unlike other technologies; a twin can be built for an individual asset, an organization or an entire enterprise. Depending on the level of the twin implemented, the corresponding impacting measures (utilization, cost reduction, user satisfaction, etc.) need to be analyzed and measured for both pre- and post-implementation stages to generate a business case to drive new value.

Moving Forward

Given digital’s rapid acceleration, organizations need to move quickly to achieve early mover advantage. To a large extent, this move favors organizations that aren’t risk averse.

However, technologies that create significant business impact — such as those that compose a digital twin — must be understood completely by all participants in an industrial value chain from the get-go. Otherwise they risk implementing something that they are unable to technically support or end up with an inaccurate model that offers limited economic value.

Learn more: Download whitepaper
Connect: vivek.diwanji@cognizant.com


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