Introducing digital twin in your enterprise management? But how will you model a successful integration?
The technology of digital twin brings a lot of excitement amongst the corporate leaders and the management teams considering how useful it can be to identify pain points in real time which will make way for prompt action. But the technology itself is in its initial phase of development and leaders are skeptical about how exactly it can be useful for their organization or what benefits it may bring. Can it be scaled according to the rising needs of the business? Or how much should they invest in the technology? And many other tempting questions still are in need of concrete answers. This makes it important for leaders to have a strategy in place on how to introduce digital twin in their application portfolio. Following points give a strategic view on how business leaders can keep themselves involved in the implementation process of digital twin right from the start.
Initiation of ideas:
Leaders in the organization must select and develop different use cases which
will benefit from digital twin. These benefits will be different for different
domains in the organization. The use case should bring a valuable insight from
the digital twin which otherwise wouldn’t have been possible. Managers should
invest time with their team members and understand what data they want to see or
use which will be useful for decision making. Answers should be developed for
questions such as-
What kind of data will give asset information which will eventually help to
lower the costs?
What tangible values the organization is generating with the use of digital
twin?
What amount to flexibility digital twin will bring in planning which was not
possible earlier?
Generate holistic View:
After developing the ideas teams should communicate on how the digital twin is
helping them understand data which is out of their expertise. A holistic view
will create a bigger picture which will help understand how data from different
units complements each other to generate new insights. Instead of delving deep
into a specific area, leaders should focus first on creating a digital twin
which will cover all the functional areas of the business. This will make sure
that digital twin has all the possible data it can have at one place. Once this
is achieved AI and machine learning can be put to use to develop new insights
and find hidden pattern.
Agile mindset:
Early implementation and introducing improvements iteratively is the right way
to go. This is critical as it will make sure that management leaders are
constantly in touch with introduction of digital twin in the organization. Using
agile mindset teams will be able to see value from early on in the
implementation which will also make sure that unnecessary requirements will not
be prioritized.
Functional integration:
Using agile mindset next step will be to develop policies and techniques on how
other functional units can be integrated into the digital twin which were
working in isolation earlier. A framework must be developed so that the
onboarding process is quick and easy to understand for others. This will make
way to develop communication links between applications which were not present
before. Application will talk to each other and exchange data over cloud and
even convert data into a specific format if required. This infrastructure
development will pave the way for faster integration.
Vertical and horizontal scaling:
Next, efforts should be given to vertical and horizontal scaling. Initially
priority should be given to the most closely related processes to the ideas
which were developed in the step 1. By this time implementation teams must have
developed a roadmap for lean integration. Leaders should also keep in mind what
kind of data they will need in the future for organizational growth which
ultimately give them a competitive advantage.
Measure KPI:
Leaders should keep an eye from the initial phase itself on how and what value
is being delivered by digital twin to the organization. Early monitoring will
prevent the project to go off track with priority given to most prominent
features. Teams should test data in various ways as different combinations will
give different view which can uncover hidden patterns. This process should
include people from ground staff to upper management to verify the KPIs and to
determine maximum value is being delivered or not.