Six keys to success when implementing and operating digital twins
Digital twins are one of the hottest topics in digital transformation. As a cross-company concept, they promise many benefits, including faster prototyping and transparent collaboration between design, testing and prototyping, to improved asset availability and OEE in manufacturing, and lower operational cost of equipment in the field.
The predicted growth rates and market volumes for digital twins across all industries are promising. According to Markets and Markets, the global overall market volume for digital twin is expected to grow from $3.1 billion in 2020 to $48.2 billion in 2026. Forrester supports the growth trend, stating that 76% of the manufacturing companies they surveyed listed digital twins among their business and strategic priorities for 2021.
Nevertheless, strong awareness of the importance of digital twins in the future does not necessarily translate into investments by these companies. In the same Forrester survey, only 40% of the interviewees stated that they are either currently implementing digital twins or intend to do so within the next 12 months. This raises the question: Why are companies reluctant to implement digital twins?
The problem with digital twin implementations: A lack of structure and rules
Although the importance of digital twins for digital transformation is undeniable, companies are confronted with challenges concerning implementation and operation. No standards or common integration framework exist. The integration of an increasing number of digital twins also adds to complexity, and different business units must coordinate on shared data models. The additional challenges include:
- Nonexistent or insufficient governance and management during implementation and operation
- Unclear accountability during implementation and operation due to undefined roles
- Lack of involvementof key stakeholders in the digital twin initiative and decision making
- Inconsistent and incomplete digital twin related management decisions
- Overly large digital twin implementation scope (big bang instead of incremental approach)
- Lack of integration with business applications
- IT competency gaps (such as security, device management or communication)
- Business values not explicitly defined and pursued
As a consequence, lifecycle steps are not performed. In other words, digital twins are not adapted to reflect updates to the assets or processes they represent — leading to unsatisfactory business value and results. In addition, stakeholders lose confidence in the digital twin and the related decisions made, which can lead to resistance to using digital twins. This endangers the ability to meet the business goals set.
The keys to success: Clear strategy, governance and focused implementation
The implementation of digital twins throughout a company needs to be understood as a journey. Therefore, the implementation should follow an incremental approach with an initial focus on realizing digital twins for low hanging fruit, as well as critical assets and processes. Nevertheless, companies should have a digital twin strategy from the beginning, which takes a big picture view and defines the roadmap for the implementation of the diverse digital twins.
The implementation of digital twins throughout a company needs to be understood as a journey.
Companies must also define what business value digital twins will deliver and pursue these goals continuously and consequently. Our top recommendations for successful digital twin implementations and operations are:
- Clearly define all roles in the digital twin program (executive sponsor, program manager and owner) to secure the required budget and resources, ensure improvements and updates as well as integrate stakeholders
- Create a digital twin charter that outlines business and innovation goals, as well as the business value to be achieved
- Implement clear digital twin governance by defining participants in decision making, communication principles, IT governance, reporting and rules concerning updates and data management
- Close IT competency gaps, such as for security, IoT and/or device management
- Plan for end-to-end system integration — the integration of digital twins with existing business applications
- Execute digital twin updates whenever necessary — namely, when assets and/or processes change
Since data is at the heart of digital twins, companies should focus on modeling the data they will need and ignoring unnecessary data. In addition, it is critical to look for new data over time to support the digital twin, which will ensure that the intended business value can be achieved. Since the digital twin objects will change over time, the digital twins must be adapted to these changes to deliver the intended results.