Business-process optimization has always been a labor- and time-intensive activity. The traditional method for uncovering the root causes of process problems—value stream mapping—involves a team, a room, and a stack of Post-it notes. Mapping the loops and alternative pathways involved in a process such as order-to-cash, claims processing, or source-to-pay can reveal plenty of hidden waste. And bringing different stakeholders together in a room can be an excellent way to create an end-to-end perspective on processes that may involve multiple stakeholders, sites, and business functions.
Traditional process mapping has an important weakness, however, in addition to the many hours of work it requires. It often relies on estimates for how long individual process steps take, or how often variances occur. As a result, these maps inadvertently represent the biases and misunderstandings of their creators. That can lead companies to miss important issues, or to focus too much attention on problems that occur infrequently and cost the business little.
Enter the new digitally-enabled world of intelligent process analytics. Every piece of information that flows through a business today generates its own digital trail, creating a plethora of data revealing where the information went and when. Now, a new generation of smart analytical tools allows companies to use that data to see what is really going on in their back-office operations.
These “process mining” tools can rapidly analyze thousands of transactions to reveal the underlying process flows. More powerfully still, they allow managers to slice business process data in multiple ways. They can show exactly which types of invoices are most likely to require manual rework, for example, or they can automatically generate key performance indicators, segmenting them by task type, customer, or operations team. Or they can monitor the effectiveness of new digital workflows, identifying the categories of task that still escape from digital process flows and demand manual intervention.
These tools can also help with the design of process changes or automation efforts. They allow companies to validate the effectiveness of those changes as they roll out. And because they can deliver useful results quickly and easily, process mining systems are especially useful in agile development environments, where robotic process automation and other new digital approaches are introduced in a rapid, iterative way.
Process mining solves several major challenges. It brings speed, analytical power, and fact-based rigor to the problem of uncovering the sources of waste, inefficiency, and lost value in business operations. But, on their own, these tools can’t do anything about fixing those problems. That’s where the hard work starts.
The smartest of analytical tools can only deliver value if they are used in the context of a wider transformation effort. Companies still need teams of people with the skills, influence and motivation to design effective processes and select appropriate performance indicators, to turn analytical insights into concrete plans of action, and then to test, roll out and sustain those processes. And the cross-functional and interconnected nature of business processes means they will also need to carefully coordinate multiple improvements to avoid introducing new problems and unintended consequences elsewhere.
We are excited about the potential for emerging process mining technologies. We have already seen companies use them to great effect in a number of different sectors. But we also know that a diagnosis is not a cure. Without the right approach, expertise, and energy to transform insights into lasting change, companies risk gaining scant return on their investment in these tools.
The authors wish to thank Klaus Kunkel, Rohit Panikkar, and Samir Singh for their contributions to this blog post.