While digital twins are often regarded as a concept that’s not quite ready for prime time, the truth is that a number of industries are already employing them because the problems they solve and benefits they deliver are just too great to ignore.
In manufacturing, for example, digital twins – digital representations of processes, services, or physical objects that can be updated with real-time data – are used regularly to improve production and upgrade business processes. The automotive and aviation industries, meanwhile, are using digital twins to enhance complex mechanical projects.
As a result of digital twins’ growing popularity, many analysts are predicting the digital twin market will grow exponentially over the next five years, from its current level of $6.9 billion annually to $73.5 billion in 2027. A significant reason for that projected growth is that digital twining has demonstrated an uncanny ability to overcome many of the challenges that traditionally plague automation programs, creating bottlenecks, stifling scale, and eating away at ROI. The most significant of these challenges – and ones digital twins may provide a solution to – include:
Inaccurate documentation and a lack of audit trails – While automations evolve, the process design documents on which they were designed do not. Because these documents remain static (and often get lost), companies are left without an audit trail or version history for their automated processes, leaving them unable to visualize and understand how their automations have changed over time. Digital twins solve for these issues by providing both accurate documentation for the organization’s entire automation estate and detailed audit trails that provide insight into the evolution of all automations currently in use.
Heavy maintenance effort – Errors and outages traditionally have hampered automation efforts. When they occur, organizations have little choice but to dedicate highly skilled technical resources to investigate the automated process, understand what function it performs, identify where the problem exists, and determine corrective action. Digital twins radically accelerate this process because they serve as a canvas for the automation that can be quickly reviewed to earmark where the error is and, in doing so, mitigate downtime and lost business value.
Reactive change management – For the most part, change management remains a highly reactive function for the same reasons that make maintenance so challenging. Corrective action is only taken when automations fail. Digital twins flip that equation, turning change management into a proactive pursuit. When automation teams are alerted of an impending change, such as a new regulation or a version update to an application that impacts the dependent UI, it can be addressed immediately via the repository where the digital twin resides. This, in turn, enables any required corrective actions to be taken quickly and easily.
Limited performance visibility – While most RPA execution platforms have a control room where operational data for the automations can be tracked and monitored, they typically come up short in other areas of automation analytics. Digital twins, which reside in a separate repository, open the door to those other analytics, providing insight into the complexity of the process, redundancies in the automation estate, and which automations should be retired in order to reduce costs and increase returns.
Complexities in understanding and managing automation estates across multiple RPA tools – In a recent Blueprint Software survey, 40 percent of participating organizations reported using multiple RPA tools in their automation initiatives, while 74 percent of those on a multi-vendor strategy indicated they had no plans to consolidate their automations under one roof. While using a multi-platform strategy may be advisable in certain situations, it does present severe challenges when it comes to standardization, governance, and visibility. At its most basic, spreading automations across multiple tools means there is no single source of truth. Maintaining a digital twin for all automations in a centralized location, however, enables organizations to monitor, govern, and keep a pulse on their entire automation estate, regardless of the RPA platform on which they are designed, deployed, and orchestrated.
Beyond the ability to address these major challenges, digital twins enable organizations to easily visualize which automations can be optimized to further increase performance and returns. And because 30 percent of automation estates, on average, are redundant, digital twins put organizations in a better position to determine which automations aren’t delivering value and which should be retired to reduce operating costs incurred due to maintenance and licensing.
Similarly, digital twins of all automations residing in a central repository also enable the application and enforcement of strong standards, collaboration, and general governance practices.
Finally, digital twins accelerate and simplify RPA platform migrations because it’s easy to perform feasibility assessments to evaluate the effort required to switch to a destination platform of choice. This is particularly important given the high volume of organizations looking to migrate from their legacy RPA platforms to next-generation intelligent automation solutions.
While the advantages of using digital twins likely vary from one company to the next, there can be little doubt they provide a tangible source of determining what is delivering value and what isn’t, and that alone is enough to help most organizations become more efficient and increase their returns.
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