What is Process Mining and how does it help drive business process improvement?

Introduction:

In today's complex business landscape, organizations often struggle to understand the inner workings of their daily processes. Failure to monitor and evaluate process data can hinder an organization's ability to optimize customer outcomes, ensure compliance, and reduce operational costs. To overcome these challenges, organizations must adopt a holistic approach to process optimization that prioritizes ongoing monitoring and evaluation of process data.

By doing so, they can better understand their internal operations, identify areas for improvement, and proactively address any issues that may arise. This approach not only drives operational efficiencies but also bolsters the organization's ability to deliver exceptional customer experiences, reduce costs, and enhance employee well-being.

To address fragmented processes across multiple business units, organizations have traditionally relied on time-consuming and subjective methods, such as SME interviews and ‘time and motion’ studies. These approaches may not always provide accurate insights and can be labor-intensive. To address these challenges, organizations should adopt an automated, data-driven approach to process optimization that leverages process mining, task mining, advanced analytics, and machine learning algorithms.

By using these tools, organizations can gain a deeper understanding of their internal operations, identify areas for improvement more quickly and accurately, and enable a more efficient way to serve their customers, reduce costs, and improve employee satisfaction. In a world where organizations must continually adapt and evolve, a data-driven approach to process optimization can help ensure sustainable success and a competitive advantage in their respective industries.

But what is Process Mining?

Process mining tools integrate multiple business processes to provide a comprehensive end-to-end view of an organization's operations in a visually appealing format. These visualizations enable the identification of process variations by volume and facilitate the exploration of specific process flow scenarios. This allows process analysts to easily identify delays and bottlenecks within the process.

What is Task Mining?

Task mining and process mining are complementary tools that can provide a comprehensive view of an organization’s processes. While process mining uses system data to visualize the entire process flow, task mining captures individual user data to visualize the specific steps individuals take to complete a process. This makes task mining a valuable tool for building an automation pipeline, particularly for organizations with citizen developer programs.

By utilizing task mining, teams can capture user data and create awareness of how work gets done within a team, which can then be automated within the governance framework of the organization. Together, task mining and process mining can identify opportunities to improve various processes such as customer service, finance, HR, or supply chain.

How does Process Mining work?

In any process, employees follow a sequence of steps, and each time they make a change, it creates a digital record in IT systems like ERP, leaving behind a trace in the system. Process Mining tools leverage these digital traces to generate a virtual and visual representation of the process flow, which becomes more accurate and reliable as more data is collected over time.

1. Selecting a business process to measure.?To initiate a successful process mining program, selecting a business process to measure is crucial. The first step is to identify the process to be studied and the KPIs associated with it. Additionally, all the applications used in the process should be identified, and task mining software tools may need to be installed onto user machines to track user actions accurately. To ensure that all actions are tracked to the right software application, labelling applications of interest is recommended

2. Collect and connect process data.?Process Mining is predominantly dependent on the log files generated by various applications. Typically, Enterprise Resource Planning (ERP) systems have pre-built log files that serve as the foundation of this data. Once the log files are collected, AI algorithms work on processing the data to construct a comprehensive process flow. Similarly, task mining breaks down user activity into process steps. AI further segregates these steps into process actions and creates a link across the entire process. However, accurately interpreting the process is crucial, and analysts may need to intervene to classify each step correctly.

3. Check the story emerging from the process.??Upon reviewing the process flows generated by process mining, it is essential to drill down into each business process scenario and map them to SME experience. Data analysis will begin to reveal a story, but interpretation is critical to ensure that the findings are accurate. For instance, in the case of accounts payable processing, high-value invoices may require additional scrutiny due to legal and regulatory requirements, which could result in a longer processing time. This delay does not necessarily indicate a process bottleneck. Therefore, it is crucial to bring SMEs with business process knowledge and process mining skill sets together to accurately determine the current state of a process flow.

4. Identify the right process opportunities to improve upon.?Process mining tools enable organizations to track the effectiveness of improvement initiatives and assess how outcomes and KPIs are impacted over time. They provide a reliable way to establish a baseline, measure performance, and drive process improvements, while also supporting efforts to drive intelligent automation. With process specialists now able to measure processes based on actual data, they can identify bottlenecks and drill down into root causes of delays, non-conformance, or experience issues. Once these issues are identified, appropriate strategies can be implemented to address them. Additionally, impactful visualizations can be used to map process pain points and monitor improvements over time.

Pros of process mining

There are five key benefits of leveraging process mining and task mining technologies in organizations:

1. Process owners can obtain a virtual process blueprint with key performance indicators (KPIs) based on actual data with minimal manual effort.
2. Process leaders can continuously track and quantify outcomes resulting from process improvements.
3. Task mining surfaces usage and training issues, leading to improved experiences for both employees and customers.
4. Enhanced operational efficiency can be achieved through process simplification, standardization, and automation.
5. Continual improvement efforts can be accelerated, resulting in faster progress toward organizational goals.

Cons of process mining

There are also a number of potentially negative consequences to process mining that organizations should keep in mind when leveraging that technology:

1. Process mining relies on IT systems and logs to stitch together data, and legacy systems may not have such logs or may be limited in the insights they can generate.
2. The accuracy of process data is contingent on the quality of data available, and as such, it may be necessary to invest time and resources in data quality management.
3. Process analysts may need to spend significant effort to stitch together a coherent narrative of process performance based on the available data.
4. Establishing a formal organization structure, such as a Process Center of Expertise or Business Process Optimization team, is often necessary to effectively leverage process mining and drive process improvements. This investment can yield significant dividends in terms of improved operational efficiency and enhanced customer experiences.

Conclusion

Large organizations committed to continuous process improvement can benefit from process mining or process intelligence platforms. Process mining helps baseline current process performance, identify areas to improve, and track results over time, fostering a data-driven decision-making culture. Continual monitoring also surfaces ideas for ongoing intelligent automation and digitization, providing more value to customers and employees.

Automated process discovery solutions, like process mining, shorten the review cycle and accelerate time to value, making it a complementary solution for organizations with lean, continuous improvement, or operational excellence capabilities. Strong executive-level sponsorship and support are required, and the process mining journey must be considered as a continuous cycle.

Regardless of where the journey begins, process mining provides the transparency and justification to apply process re-engineering or intelligent automation solutions.

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