How Process Mining Empowers Process Automation
Organizations have become complex constructs, and with it, their process landscape. To make effective decisions in this environment, management needs a clear understanding of how their business processes work. This, however, requires transparency which is often not a given or an illusion yet to be exposed. Indeed, it is no secret that Process Mining technology offers an intriguing solution to this dilemma. Utilizing data from IT systems underlying business processes, Process Mining reconstructs the true As-is processes and exposes weaknesses within those. Not without reason, this technology is sometimes referred to as providing X-ray vision of organizations.
Thus, Process Mining provides cutting-edge transparency to enable better and data-driven decision-making. This has positive effects on the automation journey as well. While companies embrace automation in general, they often start with processes limited to their own perception without measuring its overall impact first before committing. However, with data readily available and connected to a reconstructed process, it is now easy to make effective decisions on what to automate.
Process Management is picking up with today’s technological opportunities, specifically, Process Mining is discipline combining data science with process modelling.
Foundations for Robotic Process Automation (RPA)
RPA certainly is one of the favourites of today’s companies when it comes to automating business processes and works best on standardized, digitized, and repetitive processes. While RPA doesn’t have to be used to automate entire end-to-end process and may just serve to automate smaller yet strenuous tasks, large-scale automations will most likely need prior process analyses to find optimization potential before progressing further in the automation journey. Implementing Process Mining as a sparring partner to automation initiatives in general will help to gain the necessary transparency to effectively analyze the processes under consideration. Process Mining is therefore a complementary technology that supports automation in awakening all its potential.
Challenges of traditional process analysis
Usually, processes are analysed by recording the As-is process first (if not done previously) and then derive a To-be process before examining the gap between the two and taking concrete actions. This has traditionally been done by conducting drowning workshops and conducting interviews with one or more people involved. Other methods include work shadowing or, the more modern approach, screen recording.
One specific problem arises when using this method. People see the process through their own eyes and are, in most cases, biased towards a specific conduct of the process. Often, this will prevent the recording of all variations and, thereby, does not uncover some of the largest cost drivers within the process. While individual biases might somewhat be reduced by conducting workshops with several process participants, in reality, it still suffers from biases since some opinions weigh stronger and are more dominant than others. Hence, it would need a more data-driven approach to resolve biases. A second issue with traditional process recording is, that these methods are rather time consuming without a guarantee for success in many cases.
Capture day to day information from IT systems
In theory, all IT systems collect data that can be used for Process Mining and process reconstruction, however, most of the time, transactional systems will provide the greatest amount of data. Transactional systems are databases that record a company’s daily transactions. The three major transactional databases include CRM (customer relationship management), HRM (human resources management), and ERP (enterprise resource planning). For instance, a sales transaction would be recorded and stored as a piece of data in the CRM database like Salesforce. Or ERP systems like SAP or Oracle help plan, budget, predict, and report financial results.
Identify and monitor automation
Generally, identifying weaknesses in processes can already be considered as automation enablement since process automation might be one of the solutions to the original problem. Thus, creating process transparency to better analyse the business process is, in itself, a driver of automation, especially, when higher-than-expected costs are exposed, and with it, a lurking activity to be automated. Sometimes, however, processes’ advancements are the best course of action before automating it, as Bill Gates has observed. While Process Mining helps to identify those inefficiencies, separate tasks can still be automated in parallel to be more effective and quicker in improving operational efficiency.
Automation applied to an efficient operation will magnify the efficiency. Automation applied to an inefficient operation will magnify the inefficiency. – Bill Gates
It is important to improve business processes before automating the end-to-end process; however, essential tasks can be automated even if improvements on the process have yet to be made.
The above statement is therefore only partly true and does only consider end-to-end processes. In addition, process mining used for monitoring processes offers a way to track automation rates for individual activities. For example, some IT systems track user types and acknowledge non-human users. This information is then used in the reconstructed process to track automation maturity within the process. In more complicated cases, where non-human users cannot be observed directly in the system, it is necessary to pre-program the user based upon behavioural patterns, e.g., by observing the rate of cases handled or the speed of execution. In line with the general outlook for Process Mining, this again, provides full transparency and ‘X-ray vision’ through the business process and exposes all contemporary strengths and weaknesses. Monitoring automation results with process mining further allows to track the success of previous implementations.
It was established, that process mining identifies improvement potential, which is important to optimize processes before applying automation, and that it can monitor automation rates and assess automation success. A third way in which process mining’s transparency identifies automation opportunities arises from the original definition of RPA from our introduction. Repetitive, error-prone, and standardized activities are easily observed in the process mining environment by analysing the most common variants and consider the most standardized activities within it. Automation investments can then effectively be channelled towards automations for activities that are certain and fulfil the above criteria.
Today’s available technologies have opened new opportunities for organizations. When implemented properly, they can lead the way towards superfluid organizations in the 21st century – the current frontier of efficiency, where processes are fully transparent, understood and workers focused on productive and enjoyable work instead of conducting strenuous tasks. When it comes to process automation, process mining assists by offering transparency and monitoring and, with this, unmasks available opportunities to move your company to the new frontier of organizational efficiency.