The Role of Process Mining in Business Process Improvement (BPI) Methodology

How can organizations effectively measure the “as-is” process? How can they define and execute changes within the process and measure the impact of these changes along the way? This is where process mining fits into Business Process Improvement.

Let us first define BPI and consider the most common methodologies, and then circle back to further define the role of process mining within the larger puzzle or Business Process Improvement.

What is Business Process Improvement (BPI)?

BPI is self-defining, yet complex in its simplicity. Business Process Improvement is a strategic approach, or methodology to identifying an organization’s ‘as-is’ processes and pinpointing specific actions in that process that can be altered, removed or replaced to achieve a pre-defined goal. That goal might be to increase customer satisfaction, reduce operational costs or improve specified KPIs. While BPI is concerned with the entire improvement journey, it does not frame itself as a drawn out change management transformation but instead aims to bring drastic, immediate improvements addressed at the root causes of inefficiencies and process flaws that lead to higher costs, slower growth and missed opportunities.

Popular Business Process Improvement Methodologies

There is no silver bullet methodology to BPI, and the popularity of one approach over another comes in waves — TQM being the hero of the ‘70s, while Six Sigma is the approach for modern day BPI, with all Fortune 500 companies employing it within their management structure. A number of proven frameworks do exist and it’s a matter of defining organizational needs, resources and preferences to find to the methodology that best suits the time and place of a process transformation.

Total Quality Management (TQM): the mother of all BPI methodologies; the spark which lit the fuse of continuous process improvement in the conscious minds of management. Often surpassed for the next generation methodology, Six Sigma, of which it spawned.

Six Sigma: a sharp focus on finding and removing the causes and defects in a process, with the goal of virtually error-free performance. Strong structure of champions and sponsors require intimate management involvement, while simultaneously creating a true team-level culture of continuous improvement. Six Sigma employs the DMAIC 5 step approach: Define, Measure, Analyze, Improve, Control.

Lean Management: a customer-centric approach to BPI concerned with eliminating waste, closely linked to the Just-In-Time methodology of logistics management. The lean approach identifies and eliminates non-value add activities in a process and flatness organizational hierarchy bringing everyone closer to the process.



Rapid Improvement: suited ideally for fast-paced events such as improving a service-based business process, rather than projects that require complete process re-engineering or significant data analysis. RAPID: Review, Assess, Plan, Implement, Determine success

This list of BPI methodologies is not exhaustive, and each deserves a white paper unto themselves. However, they represent the core of existing approaches to BPI.

What is Process Mining?

Process mining is an automated, systematic approach to defining the ‘as-is’ phase of BPI to identify waste, bottlenecks, anomalies, and opportunities in an existing workflow or process. Process mining relies on verifiable data within IT systems and replaces manual process mapping, which is inherently riddled with human error and time waste. Additionally, process mining is used throughout the entire BPI lifecycle as a fast, accurate way to repeat the process analysis in order to sustain and monitor process change.

Placing Process Mining within BPI

Process mining can be viewed as a piece to the larger puzzle of BPI. Regardless of the process, you are looking to improve; the improvement procedure follows a similar path: identify need for change, deeply understand the process you are looking to improve, redesign said process, implement change, reflect and benchmark. A critical step early on is gaining a deep understanding of how the process actually works in real life. Guidelines and documentation may account for the ‘should-be,’ but process mining is concerned with the ‘as-is.’ It is here, in this understanding phase, that process mining is first applied.

Process mining takes the pragmatic position that even well documented and controlled processes usually don’t behave as planned and need to be objectively verified to be improved.

Traditionally, before the development of process mining software — and sometimes, even with this technology available, organizations relied on manual process mapping to paint a picture, or a map, of how a process worked. This involves lengthy interviews, subjective views, and acts as a significant time drain as consultants are required to sift through available data. When process mapping becomes process mining, subjectivity, human-error and time waste are virtually eliminated. Process mining takes thousands or hundreds of thousands of time-stamped system data events and transforms them into a story that shows the reality of any given process. By using data based on truth, not subjectivity, process mining builds confidence and certainty into the early phases of BPI.

Learn about process discovery with Minit

Process mining objectively views anomalies, quickly identifies process bottlenecks and their root causes, and uncovers steps, or missing parts, of a process that employees are either unaware of or fail to highlight.

Process mining appears again and again throughout a BPI initiative as changes are implemented, and continuous process monitoring and evaluation is required. Once process mining is established it can easily be “switched-on” to re-analyze the updated process and create a clear benchmark of measurement at any stage necessary.

To learn more about process mining software and how technology can help drive your organization’s Business Process Improvement initiative, get in touch with our team.