Process modelling, process mining, two sides of the coin

When it comes to process transformation, you need both a strategy and a clear understanding of where you are today. That is why process modelling, and process mining are critical to your success.

To remain successful in a competitive, globalized world, every organization needs to undergo constant transformation in the way they do business. Change and innovation are a part of the new normal for businesses across all industries. These practices can range from small – like making shifts to meet new market demands or industry regulations – to implementing larger customer-focused business model innovations that can disrupt the underlying industry or even create an entirely new market.

To transform your organization, you essentially need two components:

#1 Strategy and plan for the desired new way of working

#2 Visibility into your current modus operandi

In the case of digital transformation, the first is process modelling, which sets the bar you want to achieve, and second tells you where you are and what you need to change using process mining to achieve the first.

It is like two sides of the same coin; you need to actually pay with both.

Process mining allows you to take all the process data within your company and mines it for insight on potential improvement, focusing on finding better, more efficient pathways in operations. The goal is to take a deep-dive into as-is processes and discover touchless process paths that require minimal human intervention and increase the speed, accuracy, and quality of whatever your business does.

So what can process mining reveal about a business’s current state of operations?

Let us take a process from order to delivery as an example. Customers today demand a highly flexible and digitized customer experience that allows them to change their order up to the last possible moment. When customers cannot do this because of inefficient processes, businesses risk losing customers to a competitor.

Here’s an example: I recently ordered some clothes from a large fashion house and wanted to change the size of one of the items after I placed the order. The process model used by the designer could not support my request to change the size of one piece of clothing and my request caused issues with the shipping and delivery of the entire order. It was a nightmare experience for me as a customer, and from a sustainability standpoint, it created a lot of waste.

In this case, process mining could help this business identify those touchless points in the process that can be improved to deliver an improved customer experience and reduce waste. Maybe the data analysis will reveal that a tweak in the point-of-sale software can support a customer’s ability to amend an order within a certain timeframe without causing delays.

Or maybe it will identify opportunities to increase efficiencies in the shipping, deliver, and return steps of the process which will lead to increased customer satisfaction. These are only assumptions, however.

A complete process mining analysis can only reveal the realities of a business’s as-is state.

Order changes are increasingly a necessity not only in the stock-to-order business, but also in make-to-order. Take automotive manufacturers, who must allow customers to change their vehicle configuration until extremely late in the production process, like changing the paint colour by the time it reaches the paint shop.

Customers also demand full visibility into their orders. This applies not only to automotive orders – which can take many months to more than a year in times of global value chain disruption – but to industries with shorter make-to-order timelines like the live tracking of food deliveries.

Once you have acquired the data and insights into how your business operates, the next step is critical to achieving the innovation you want to see throughout your business model: process modelling.

Process mining and process modelling are often thought of as two unrelated practices used for digital transformation. They in fact are very closely related and are two sides of the same coin.

You cannot successfully accomplish a process modelling exercise without the data and insights derived from process mining. Having said, several customers treat process mining and process modelling as siloed entities within their transformation journeys. And if there is anything we know better, it is that removing silos and encouraging free flow of information is what drives innovation, change, improved efficiencies – all things that ultimately make a truly connected enterprise.

If you want to change the way you do business, or even your entire business model, to achieve the innovation you want, there are many stakeholders involved. Therefore, you need a platform that manages the transformation process and delivers the right information to the right people at the right time.

Take the process from order to delivery as an example: If you only change the underlying IT systems without educating everyone involved in the process on those changes, the transformation will cause friction or even worse.

While an increasing number of companies have already implemented process modelling and mining, the majority still do so in isolation, resulting in lower effectiveness of transformation outcomes or even failed approaches. This is similar to companies working in isolated functions rather than process-driven organizations.

For true transformation heroes, it will therefore become best practice to establish an integrated transformation platform to gain fact-based, data-driven process insights as well as planning and rollout capabilities to turn plans into actions and ultimately measurable results.

Key takeaways

• It is like two sides of the same coin you need to actually pay with both.
• Data analysis will reveal that a tweak in the point-of-sale software can support a customer’s ability to amend an order.
• Maybe it will identify opportunities to increase efficiencies in the shipping, deliver, return steps of the process.
• A complete process mining analysis can only reveal the realities of a business’s as-is state.
• Customers also demand full visibility into their orders.
• Automotive orders can take many months to more than a year in times of global value chain disruption.
• Once you have acquired insights into how your business operates, the next step is critical to achieving innovation.