Reinvent business processes with digital twins

A digital twin is a virtual model – or replica – of an actual process, service, or product that allows the builder to simulate, test, and various scenarios in a zero-risk virtual environment. Originally developed in manufacturing environments to help engineers test and refine physical assets such as engines and turbines, using AI-powered process exploration tools, organizations can now create digital twins of business processes.

Also known as “process simulations,” digital twins not only model business processes, but allow the user to test new procedures, inputs, and other variables to see their impact on process performance. Digitally mature organizations that implement process mining and digital twin technology at scale end up creating what is known as a digital twin of an organization (DTO). DTOs are virtual models of the entire enterprise – an organization’s processes, physical assets, interactions, and data storage structures – that can be manipulated to test the impact of changes in a domain. across the entire ecosystem.

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Simply put, the four steps to creating a digital twin are mine, visualize, analyze, and simulate.

To elaborate, the first step in building digital twins is process mining, a set of methods that extract operational data (date, time, user, activity, object, etc.) from the event logs of computer systems and transform them into visual representations of business processes.

Although, technically speaking, process mining can be done manually, it is extremely time consuming and, given the level of precision required, difficult. As such, organizations are adopting process mining software solutions, suites of technologies that, at the very least, automate data extraction and analysis. Increasingly however, process mining and virtual twins have become so synonymous that many tools facilitate the entire process of creating digital twins from start to finish.

Not only do these automated tools acquire, cleanse, and validate data, but they can also automate process discovery, business process identification.

The second step? Visualization of the process. As we often say, pictures speak louder than words. While process mining alone can certainly provide key insights, by creating a visual representation of process flow, process visualization can provide you with a deeper understanding of process performance.

Using process data extracted from process data mining, organizations can model, map, and visualize complex business processes. Additionally, by using automated analysis methods (target-actual comparison, root cause analysis, etc.), organizations can identify areas of improvement and validate new ways to optimize and digitize them.

The last step is the simulation. Using data collected through process mining, users can test process changes and adaptations in a risk-free virtual environment before implementing them in the real world. Mature organizations that have built DTOs can even see how a small change to a single process can impact the entire ecosystem.

The benefits of using process mining to create digital twins of business processes are wide-ranging and very significant. In fact, given the potential of process mining tools to radically transform operational performance, organizations are rushing to adopt them. As such, the digital twin market is expected to reach over $36 billion by 2025 at a CAGR of 38%.

Key benefits include:

  • Automated process discovery. Using process mining tools, organizations can speed up the identification of new processes to automate
  • By centralizing process intelligence in a central dashboard, process mining enables cross-functional collaboration and communication
  • Promotes experimentation while reducing risk (both financial and regulatory) by allowing users to test process changes in a secure virtual environment before implementing them in the real world

For example, Credem Bank used process mining to create digital twins of its back-office (BO) process and as a result was able to automate 90% of BO processes using RPA. After automation, the bank managed to reduce BO costs by up to 85%.

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