How Digital Twins Enhance Effectiveness & Efficiency Across the Value Chain
To create data-driven product design strategies, industrial companies need to reassess their operational maturity and technology readiness to compete in a data-driven world where the virtual and physical seamlessly fuse.
The Internet of Things (IoT) presents many advantages to organizations seeking competitive differentiation. Two unique advantages pivot around the availability of new types of sensing devices (e.g., wireless sensors) that can be added to most types of industrial gear, and the ability to collect near-real-time data from equipment for analysis and prognosis (known as 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. Let’s take a deep dive into our three-point framework that industrial organizations can use to pursue the digital twin concept.
But First…A Digital Twin Primer
A digital twin is a virtual representation of a physical asset that is virtually indistinguishable from its physical counterpart. It includes design and engineering details that describe its geometry, materials, components and behavior, or performance.
Moreover, a digital twin integrates all of the organization’s digital information on a specific asset or piece of equipment with operating data streaming from the product while in use. At a conceptual level, all the equipment within a factory floor can be aggregated into a digital twin or a digital factory. When combined with analytics, the concept of the digital twin delivers insights that can unlock hidden value for the organization. It can provide engineers with information on potential operational failures of IoT-connected products, for instance, and thus help prevent unplanned downtime, improve product performance, etc.
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 (see Figure 1).
Assessing 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.
Digital Twin Building Blocks
The concept of a digital twin, as first defined by Dr. Michael Grieves in 2003, consists of three main parts:
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 actions in the physical world.
Managing 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.
Choosing 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.
Therefore, it is imperative start with a basic, simple model of a digital twin and continuously add the necessary inputs and analytics as needs emerge.
Best Practices for 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 the it will become obsolete over time and thus severely undermine the investment in building it.
To avoid such mistakes and build a truly dynamic digital twin that can deliver the promised value, we highlight a few of the best practices defined by Gartner (see Figure 2):
Participation across the product value chain. With the extensive knowledge gained on critical and practical challenges, participation of stakeholders across the supply chain is essential. Inclusion of inputs from across the supply chain will ensure a better and efficient design of the digital model.
Standard & healthy practices for creating & modifying the models. Forming standardized design practices helps organizations to connect and communicate design ideas across the globe.
Data collected from multiple sources. While a digital model can replicate how various components fit together, organizations need to gather the data from many sources — sometimes both internal and external — to perform simulations or carry out the necessary analytics to gain business value from a digital twin.
Ensure long-access lifecycles. Digital twin models currently built on this proprietary design software format run the risk of becoming unreadable in the later part of their service life. To overcome such risks, digital twin owners and IT architects need to insist that proper terms are set and agreed upon with proprietary design software vendors to ensure data compatibility is maintained, backward and forward, for relevant categories of software.
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.
The configuration of digital twins is determined by the type of input data, number of data sources and the defined metrics. The configuration determines the value an organization can extract from the digital twin. Therefore, a twin with a higher configuration can yield better predictions than can a twin with a lower configuration. Organizations need to agree and decide on the relative percentage of improvements that can be achieved based on the level of twin implemented.
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.