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.
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.
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.
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.