Daily, humans perform various tasks and activities with a scope to improve. For example, a task as simple as preparing a cereal bowl for breakfast. We must heat milk, put our favorite cereal flakes into a bowl, pour the hot milk over it, add fruits, nuts, berries, honey, and other treats, and enjoy—a straightforward meal with an easy process. If analyzed further, this routine can be optimized by reducing the number of steps by placing all the required ingredients for this preparation in one place rather than grabbing them from different spots in the kitchen. However, we probably wouldn’t invest much thought or effort into optimizing this simple routine because a few minutes saved here won’t make a big difference in our lives. Now scale this to an enterprise-wide scenario and imagine the number of routine processes making up the daily workflow. You would find dozens of optimization opportunities for those processes. Trying to identify these processes and methods to optimize them manually is like boiling the ocean. Process mining tools and techniques come to the rescue here.
What is Process Mining?
Process mining is a technique to examine and track processes. Using traditional methods, process mining was carried out in business settings using process workshops and interviews. With sophisticated tools and technologies in place, process mining uses data from company information systems to study the processes, how they are performed, and how they can be improved.
Process Mining Stages
- In a company’s IT systems capture several daily transactions as digital records. Examples of such digital records include receiving orders, approving financial transactions, processing employee leave requests, etc.
- Any Process mining tool would transform these digital records into event logs. Event logs are usually in XML-based XES formats and have three key attributes – Case ID, activity, and timestamp.
- Using event logs, process mining tools create visualizations, charts, and dashboards of the complete process or how the process is being performed.
- Once the visualizations are created, the results can be compared to established KPIs or metrics on how the process is currently performing versus how it should ideally perform.
Process Mining use cases
Process mining can be used in any area or domain where workflows of any sort are being followed. Some common industries where it finds its applications include Supply Chain, Finance, and Automation (RPA).
Process Mining in RPA
Robotic Process Automation, or RPA, automates repetitive business tasks. According to QPR software, a significant provider of process mining software, process mining can reduce RPA implementation time by almost 50% and RPA project risk by 60%. UiPath is a leading RPA provider, and according to them, “78% of people who automate say process mining is key to enabling their RPA efforts”. Some benefits offered by process mining in RPA include:
- Process mining discovers areas that need improvement and operations that can benefit from automation.
- As a result of mining, processes are optimized, which is a necessary first step before thinking of automating them.
- Process mining assesses results from existing processes and measures them against established metrics.
- Process Mining tools can help simulate workflows to predict the impact of RPA on performance.
Considerations for Process Mining Implementation
- Organizations must have their manual processes digitized so that process mining tools can extract event logs from their IT systems.
- The data to be processed by mining tools must be organized and complete. Process mining results are directly proportional to the quality of extracted data. Preparing data for process mining is the most challenging part for most organizations.
- Organizations must also factor in the budget and costs for purchasing Process mining licenses.
- There are various process mining tools available in the market, such as Celonis, QPR, UiPath, and Fluxicon. In addition to these, various open-source tools such as ProM and Apromore are also available. While choosing the tool of choice, organizations must ensure seamless integration between their IT systems and the mining solution to enable flawless data extraction and accurate analysis.
The Intelligence Automation team at IGT works with various customers to automate their processes via RPA, for which process mining is a crucial milestone, as discussed above.
Renu Ayani Bhatia is a Senior Business Consultant at IGT Solutions’ Intelligent Automation and Analytics Practice. With a consulting experience of 6 years, Renu has worked across domains such as Healthcare, Payroll, and now Travel. Her expertise lies in bridging the gap between business and technology, managing business goals and expectations to turn into reality.