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Executive Overview
A digital twin is a dynamic virtual representation of a physical entity using real-world data. The business value comes from decision support to improve outcomes. Performance digital twins use real-time data and analytics to optimize the effectiveness of the resources deployed for operations and maintenance. The digital thread, in turn, involves a communication framework to connect data in siloed systems. This enables an integrated view of the asset’s data throughout its lifecycle across the traditionally siloed functional perspectives. Digital thread provides the needed infrastructure for developing and deploying a program for multiple digital twins.
With their relatively narrow scope, digital twins at the equipment and unit levels allow for more rapid advancement in maturity. Many have moved beyond problem detection into the diagnostic and predictive levels. Predictive maintenance (PdM) is the most common application.
Digital twins of production lines or entire plants currently focus on 3D models. These enable users to navigate virtually to an asset and obtain information for that asset and, in this manner, empower a broader portion of the workforce for faster and higher quality response to issues. Expect 3D twins to advance to higher maturity levels.
Key findings from ARC’s research include:
- User adoption of a performance digital twin needs low false positives and good ease-of-use. Factors to achieve this include data quality, model quality, intuitive user interface, a quality indicator, and management of change to mirror the physical asset over time.
- Successful digital twin programs have a business case template, workflow for repeatable digital twin development, and consistent graphical user interface (GUI) for users to easily engage with multiple twins.
This report builds on the August 2020 ARC Strategy Reports, "How to get Executive Support for Digital Twins." That report examines the business case for a digital twin in the operate and maintain portion of an asset’s lifecycle.
Digital Twin Scope
The term “digital twin” appears in many vendors’ marketing programs, with a wide variety of interpretations. The first step for a meaningful review and recommendation requires defining the term: A digital twin is a dynamic virtual representation of a physical entity using real-world data. The representation can take the form of a mathematical algorithm, machine learning, or 3D model.
Each digital twin has these key aspects:
- The physical asset
- Virtual representation
- Data transfer between the two
- Integration into related business processes
Currently, most associated business value comes from decision support to improve outcomes, such as alerts for predictive maintenance going to the maintenance planner. In the future, we’re likely to see the business value evolve beyond decision support, to autonomous operations. For example, automatically releasing a maintenance work order with diagnostic information. However, this will require high reliability, i.e., near-zero false positives or negatives.
Not included in this report are digital twins for designing a discrete product or for operator training. Both are viable applications, but outside this report’s scope.
Digital Twins for Asset Management
Digital twins for asset management fall into two fundamental categories:
Project digital twins use 3D and 4D[1] models for simulation to avoid errors in the design and build of equipment, plant or infrastructure and to improve the construction schedule.
Performance digital twins use real-time data and analytics to optimize the operational effectiveness of the resources deployed for operations and maintenance.
A performance digital twin involves collecting real-time operational data, applying analytics to evaluate current conditions, and sending alerts when something deteriorates. Digital twins also provide the basis for digital transformation in asset-intensive industries.
Augmenting the Process Control System
The process control system – usually PLC- or DCS-based – typically focuses on regulating the mechanization and automation for one or multiple connected machines or units. Here, the digital twin is best used to augment functions in the process control system. Hence, most performance digital twins identified by ARC involve equipment maintenance – particularly equipment critical to production operations. Predominately, performance digital twins are applied to a specific piece of equipment or process unit in production to prevent unplanned downtime.
Plant Historian
The digital twin software usually interoperates with historian applications for data acquisition. Rather than impinge on the control system, digital twins pull data from the plant historian. This allows the control system to focus on controlling the process without interrupts to service messaging and data transfer needs. Also, users can deploy and make iterative improvements to a twin without modifying the control system.
Business Process Automation
When something starts to go bad, the digital twin creates an alert. This alert needs to be communicated to someone who can initiate action that prevents or mitigates the problem. Manual methods for communicating alerts are unreliable and typically lead to lost alerts, resulting in equipment failures. The digital twin software should interoperate with business systems for business process automation, so the alert is not lost.
For PdM, alerts involve notifying the maintenance planner who plans maintenance work orders using the enterprise asset management (EAM) system. All EAM systems have an application programming interface (API) for alerts to automatically generate a work order for a maintenance planner to review and schedule. This type of business process automation helps assure that responsibility for resolving problems is assigned prior to failure.
Digital Thread
In most industrial enterprises today, many functions still store their respective data in “siloed” systems that are not available to other functions. In this manner, a department often obtains and controls the information it needs for success.
Balkanized Data
“Balkanized” data is often found in mutually untrusting and sometimes hostile groups or functions within an organization. This dysfunction commonly involves the design/build process for assets among the engineering, procurement, and construction functions. It also occurs during the operate and maintain phase of an asset’s lifecycle for reliability engineering, maintenance, control engineering, and others. Digital threads break through these barriers to data by connecting silos to provide access to information.
Asset Lifecycle Information Management (ALIM)
Asset information changes many times during an asset’s lifecycle, from asset creation through operations and maintenance and to end-of-life. Data about the asset are handed over numerous times, from engineering, procurement, and construction (EPC) firms; supply chain partners; owner-operators; and internally within each of these organizations. Valuable data can be lost, misinterpreted, or keyed-in incorrectly. Tag and equipment data are difficult to manage because they are often in differing formats, exist in various applications and systems, and transmitted by different means. Managing asset information is difficult among balkanized organizations with independent workflows and KPIs.
The digital thread involves a communication framework to connect the silos and provide data flow. This enables an integrated view of the asset’s information, documentation, and data throughout its lifecycle across the traditionally siloed functional perspectives. A digital thread extends across design and build, through handover, toward operate and maintain, and into the next upgrade cycle. Engineers typically spend 30 percent of their time looking for information. A digital thread provides a means to significantly reduce this waste.
Table of Contents
- Executive Overview
- Digital Twin Scope
- Digital Thread
- Performance Digital Twin Lifecycle
- Sustaining the Digital Twin
- Management of Change Includes the Twin
- Digital Twin Maturity Model
- Recommendations
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