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Automation has delivered significant benefits for organizations that have taken the necessary steps to adopt it, but let’s face facts: The journey to successful automation typically isn’t an easy one. Despite best-laid plans, barriers and pain points continue to plague automation programs, creating bottlenecks, stifling scale and throttling better returns.
Given the challenges of automation — for instance, heavy maintenance burdens can eat away at ROI, and a lack of visibility on automation estates can result in redundancies and inflated costs — it is little wonder that digital twinning and its innate ability to successfully address issues is beginning to resonate.
Tracing its origins to NASA’s space program in the 1960s, digital twinning in automation can best be defined as a digital copy of an automated process that resides in a separate repository to the robotics process automation (RPA) platform where the actual automation is developed, deployed and orchestrated.
Advantages of digital twins
The primary advantage of the digital twin is that it evolves as automation evolves. As a result, if any changes are applied to the automation in the RPA platform, those same changes are reflected in the twin, ideally in real-time or at least near real-time.
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Operational metrics (including runs, last time run, number of issues, utilizations, and success rates) are also accessible and displayed where the twin resides so that it can be monitored and continuously improved.
Beyond changes and operational metrics, a digital twin in automation enables an organization to compile accurate documentation and detailed audit trails for the entire automation estate and maintain it in a single, centralized repository. Doing so not only addresses the problem of misplaced or lost process design documents, but also solves one of the major pain points of automating: An inability to visualize and understand how automations have changed over time.
Maintaining digital twins for all automations in a central location — regardless of the RPA platform in which they are designed, deployed and orchestrated — vastly improves automation standardization, governance and visibility. Particularly for companies that employ a multi-platform automation strategy, a single repository allows for greater visibility into the complexity of all processes, as well as the systems and applications with which they interact.
This not only vastly improves oversight of the entire automation estate, but allows for faster recognition of potential issues and redundancies and identification of automations that can be retired to lower costs and increase returns.
Less maintenance required
Digital twinning also reduces the need for maintenance. By serving as a canvas for automation, a digital twin can be quickly reviewed to identify where an error has occurred and how it can be corrected, saving both time and money.
It also flips the change management process. Rather than waiting for an automation to fail before taking corrective action, digital twins enable proactive steps to be taken as soon as a potential malfunction is detected or in advance of a regulatory change or application update.
Finally, digital twins enable accelerated and simplified RPA platform migrations because the feasibility assessments to evaluate the effort needed to switch destination platforms can be more easily performed. Because a digital version of up-to-date automation exists, exporting automation with a mapping engine that requires only minor modifications dramatically reduces the effort needed and removes manual recoding.
Forging new partnerships
This will prove to be particularly important in the year ahead as more organizations look to migrate from their legacy RPA platforms to next-generation intelligent automation solutions.
Migrations will be further complicated as new partnerships are being formed among well-established solution providers using information from Internet of Things (IoT). At least three digital twin-focused industry standards groups have already emerged to assist in guiding the technology forward.
While there can be little doubt that digital twins in automation provide a palpable source for understanding what is delivering value (and what isn’t), implementing digital twins is likely to get more complicated as parameters, design principles and even basic assumptions change.
Some digital twins still rely on older simulations and monitoring, while others have built-in AI solutions that rely on evolving data to keep parameters up-to-date.
All of this suggests that while their benefits will likely vary from one organization to the next, digital twins undoubtedly will see even wider use in the future. With compound annual growth rates generally projected to approach 40% annually, some analysts are already predicting 2023 as a banner year for the digital twin.
Spurred by new developments — including the ability to proactively search for and harvest data — expect the digital twin market to grow from its current level of $6.9 billion to more than $73.5 billion by 2027. More organizations will recognize the persistent problems digital twins can address and the benefits, from increased efficiency to greater ROI that they offer.
Dan Shimmerman is president and CEO at Blueprint Software Systems
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