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Why it is Essential to Simulate Process Changes after Process Mining
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Why it is Essential to Simulate Process Changes after Process Mining

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Many companies have used process mining to discover and analyse their processes and are generating improvement ideas based on their process mining insights. However, there are often better implementation choices than the improvement suggestions of process mining.

In a previous blog, we explored the 5 levels of process simulation and why a business must use a standalone process simulation tool to further optimise their processes. This blog will illustrate how Silico can add significant value to your process mining insights and help you avoid common pitfalls when solely relying on process mining for your transformation projects.

What are the pitfalls of mining-based process changes?

Process mining cannot determine the process-wide or company-wide impact of decisions.

An improvement to a single element or step identified through process mining may not scale up to the entire process. Process mining cannot indicate how the transformation will impact other areas of the process. This can result in one stage of a process benefiting whilst the problem is pushed to another, with negligible benefits for the process or company as a whole. This can raise more questions than answers as further mining is now required in this changed process.

Process mining focuses on historical data, making it nonoptimal for future decisions.

Businesses need to optimise their operations for their future, rather than historical circumstances. For example, an analysis of improvements should consider what may happen in situations such as large growth or the need to reduce FTE numbers. Process mining and its analysis of historical data provide little benefit to understanding the pressure processes may be under in future situations. This can put a business under huge stress because they are not aware of the issues added to a process, which may have been within an acceptable tolerance beforehand, but now has growing backlogs. Moreover, issues in the future may arise at different parts of the process than they have in the past, resulting in mis-prioritising improvements. All of these factors result in less revenue, profit, customer satisfaction, and a harmed reputation.

Recent events mean historical data is as unreliable as ever.

Using only historical data can give an insight into past performance, but is not suitable for potential unforeseeable circumstances which may arise in the future. A recent major example of this would be the COVID-19 pandemic and how it affected supply chains. For many businesses, historical data would not have provided any benefit to forecasts, business performance and capacity requirements for example. 

What about processes which are not tracked via systems?

Process mining can only provide insight into processes which are tracked via a system, meaning processes may not be covered fully. This presents a real problem for businesses solely relying on process mining, as many steps may go unaccounted for and presents a ‘blind spot’ in the improvement process. Whilst Process mining has its value, businesses may struggle to answer ‘What's next?’ using mining alone. This is where Silico simulation can add significant value.

Analyse your Process in Forward-Looking Scenarios

Simulation allows you to see your processes from a forward-thinking perspective, analysing how they perform in various scenarios whilst accounting for complex dynamics such as shifting backlogs. You can read more about this in our blog, discussing ways to add value to your process maps.

You can see how your business will perform in different circumstances whilst highlighting the areas under the most pressure. This gives decision-makers the evidence they need to assure transformation teams are focusing on the right areas to prevent future issues and deliver the most value, without causing unintended consequences.

For example, let's simulate what happens to an Order-to-Cash process once the business has had to reduce FTEs by 10% in May 2023. In this example, the process has been divided into sub-processes, ‘Review Orders’, ‘Deliver Orders’ and ‘Reconcile Orders’.

The model tells us profit will take a more than £30m hit if capacity is reduced and nothing is done to resolve the fact that the team is now unable to process enough orders, and so a significant amount of revenue is being delayed.

Quantify the Impact of Transformation Initiatives on the Whole Process

Simulating your transformation initiatives allows you to quantify the outcome before it happens. Whilst process mining can identify undesired activities and roughly estimate the benefits of removing them, it cannot look into the future. Silico simulation allows you to interchange and combine variables and structural changes to see how the process will be affected by this.

For example, process mining has identified an undesired step in the ‘Order to Delivery’ process called ‘Remove Delivery Blocks’ which occurs on 11% of orders. Let's simulate removing this and see if that helps our issue. 

We can see that the solution has generated about 400k more profit compared to the previous visual but nothing substantial. This is where process mining alone may not be optimal. The initiative presented to us has identified a route in the process which is inefficient but has not considered the quantifiable benefit of reducing case volume through this inefficient path; it has highlighted the effects of a problem, but not the problem itself. Simulation shows us that when we insert a scenario, such as 10% less FTE, to the process, it yields a minimal ROI.

Silico Focuses your Transformation Team on the Most Harmful Problems

Silico simulation can show us how many orders are stuck in a process at any point - this gives your transformation and improvement teams an idea of where the problem is occurring and which parts of the process to consider improving.  

In the ‘Reduced Headcount’ scenario (red) the bottleneck is in the ‘Review Orders’ sub-process, whereas process mining identified the issue in the sub-process ‘Deliver Orders’ (bar 2). Simulation ensures that the transformation team is focusing on improving the right part of the process, rather than wasting time and resources on improvement ideas that may not have a significant impact on the business.

Redesign your Process with Silico

Silico allows you to experiment with different solutions to see how best to resolve issues in the process. Let's review the ‘Confirm Order’ stage of the ‘Review Orders’ process.

The stock tells us that a backlog is increasing here, and something needs to be done to resolve this. Using Silico’s new feature ‘Variant Panels’, we can now experiment with different solutions to see how this affects the backlog. Let's introduce automation and review the outcome.

By automating just 65% of orders coming into this activity, we can see the backlog has dropped significantly and the issue will be resolved.

Silico Lets you Experiment with your Process Before Making Changes in the Real World

We can now revert back to our dashboard and review the impact on our financials.

Even with a 10% decrease in FTE across the process, the automation has removed the backlog and even increased profits by 727k compared to the base case.

Using only process mining, you would need to implement the 10% FTE decrease and review new data before making a decision. In this example, it would have cost the business £77k a day before a change is implemented, not taking into account the reduced customer satisfaction from long lead times and potential order cancellations.

Get the Most out of your Process Mining Insights with Silico

Process mining alone may cause companies to make reactive, rather than proactive, decisions. This, in turn, means you have to identify the opportunity and then track it against performance, ultimately introducing risk to your initiatives.

Using Silico, your business processes will be future-proof and ready for any real-life scenario which may arise. It will allow you to focus on the root cause and not the effects of a problem, saving you time in the decision-making stage. This ensures that transformation teams are working in the correct areas of a process and making real changes that will benefit the whole process rather than one step.

To learn more about how Silico can be used within your organisation, contact