The four stages of digital twins in business processing – stage #3: simulating
Blog: Capgemini CTO Blog
In the first two articles in this series, we looked at business mining and at modeling. Let’s assume, therefore, that we’ve reached a point where we’ve mapped our current business, all our ideas are in place, and we’ve modeled potential improvements to our finance and administration processes. All of which means we’re now ready to run simulations.
Testing the effect
What we’re simulating, of course, is the “to-be” model we’ve defined. In other words, what we expect will be our new, improved approach to a process vs. our “as-is” what we currently do today. Will it work – and will it deliver the benefits we anticipate?
Let’s say it’s an invoice processing routine. We can input the number of invoices we might expect in a given timeframe, and also the metrics that might apply, such as the length of time it typically takes to handle one such invoice. This, in turn, will be conditioned by whether the process is deemed to be constrained (i.e., by factors such as the necessity for working-hours human input) or unconstrained (i.e., automatable) resources.
At Capgemini, we use the BusinessOptix platform, a tool that leverages the Monte Carlo simulation model to simulate the path of transactions while they’re taking place. It simulates and monitors the performance of the proposed improvements – but of course, it also simulates and monitors the status quo. We ensure the metrics for these two process models – for the “as-is” and for the “to-be” – are consistent with one another. This means that, as processes are put through their paces, we can switch between them at will, watch the gauges rise and fall, and see which current bottlenecks disappear, which ones reduce (and by what degree), and which ones might need further attention.
As we’re all often told, information is power, and the findings from these simulations enable us to establish a business case for next steps in the real-world implementation. We can see what will make the biggest and fastest difference. We can also establish likely costs, and calculate potential savings over current methods. We can use all this knowledge to decide what we’ll do first, what should come next, and what, based on information received, is perhaps not worth doing at all.
Simulating in action – a national tax and revenue body
Here’s an example of what simulation can do. This European national tax and revenue organization was running a number of different journal processes across different departments. All of them were manual, there was no proper documentation, the interdepartmental mismatches were numerous, and the metrics were insufficient.
Modeling and simulation in BusinessOptix enabled us to map current processes and pain-points, and also to propose future standardization. Simulation routines showed the potential reduction in preparation and processing time for the journals, and also demonstrated how automation would reduce bottlenecks.
As a result, we were able to propose improvements in both the process and technology areas. Metrics for best practice were established, including areas of potential process standardization and automation. We also suggested new process flows that could be introduced immediately.
Measurable benefits we were able to demonstrate included potential reductions in: journal preparation, from 11 minutes to 5 minutes: and also in journal processing time, from 57 minutes to 45 minutes for non-rule-based processing, and from 57 minutes to as little as 24 minutes for rule-based processing.
In summary, it’s this the simulation stage that gives digital twins their real business value, because it marks a risk-free way for organizations to assess their options.
In the next stage, we’ll consider the scope digital twins provide to continue to make improvements. We’ll also look at how digital twins can help organizations transition to – what we call – the Frictionless Enterprise.
Elle Sanchez Cardenas creates target operating models for finance and accounting with an automation first focus to improve transaction cycles, reduce manual effort, and increase capacity within teams. She also designs end to end transformations from process and policy enhancements to touchless processing.