It would be tempting to think that in those three topics, we’ve covered all the bases. After all, once you have a model that can simulate both how things are, and how you want them to be, you’ll have reached the point at which you know what will work, where you want to go, and how to get there – right?
Well, yes, indeed. But that doesn’t mean the job is done. As with many things in life, it will always be work in progress.
From better – to better still
For instance, let’s say we’ve used a digital twin to model a new approach to invoice processing. The model has been developed and implemented in the live system, and it’s reduced a major bottleneck in the previous process – but not to the extent we expected. We still have our digital twin, though, so we can revisit the simulation cycle, and try out various “what-if” tweaks to see what might help. We can do this, of course, without disruption to the live system, which is still delivering better results for us – results that we may now be able to improve.
Here’s another instance. In this case, the implementation is indeed delivering the expected results – but that doesn’t mean things are the best they can be. For one thing, it’s likely that our new model was designed on 80/20 principles, to address the biggest process bottlenecks. Which means there are still other, lesser points of friction that can be addressed. Revisiting the business mining stage will help identify them, and modeling and simulation can help resolve them.
Even if the new process model has addressed everything, completely and perfectly, there are still other issues to tackle. First, there is the human factor. People are likely to interact with live systems in different and possibly quirky ways. What starts out as a personal shortcut can become an inconsistency, which can in turn become a new bottleneck that needs fixing.
Second, things can change. The perfect new model may have been designed to address circumstances that no longer apply. It needs to be adapted. Similarly, changes that weren’t made previously because they were too expensive may now, as a result of developments, be not only more cost-justifiable, but actually desirable.
In short, the digital twin process should be seen not as linear, but as a cycle. There is always room for improvement.
Improving in action – a global logistics organization
This logistics business is a household name. It was running credit-to-cash operations across three global regions, partly in-house, and partly on an outsourced basis. Each region had a different process models, making it difficult to introduce a single approach to automation, and thereby leading to overall inefficiency.
The digital twin brought together best practices from all the regions, and by consulting widely and using the BusinessOptix platform, we were able to develop a common framework for roles, controls and systems to be used consistently in the new global process model.
Transition to the new platform has been smooth – but the work continues. We have continued to gauge real day-to-day implementation of the process, to ensure it remains commensurate with global and regional needs. We’ve also continued to monitor industry-wide best practice, so we can recommend improvement opportunities.
In the fifth and final article in this series, we’ll look at the role a Transformation and Innovation Office can play in the implementation of digital twins for business processes. 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.
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