Turn RPA into a success with Process Mining: 3 examples

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With the added value of Robotic Process Automation (RPA) becoming clearer, many organizations enter the area of RPA. The expanding focus on RPA brings questions about how to successfully select the to be automated process. To do so, Process Mining is a valuable addition. Many articles describe the nature of RPA and Process Mining, now […]

With the added value of Robotic Process Automation (RPA) becoming clearer, many organizations enter the area of RPA. The expanding focus on RPA brings questions about how to successfully select the to be automated process. To do so, Process Mining is a valuable addition. Many articles describe the nature of RPA and Process Mining, now it is time to take further steps in combining these two techniques!

The hot topic of RPA

RPA concerns the automation of business processes enabling companies to improve cost effectiveness and quality improvements. One of the first stages of a RPA-project is to select the intended business process to be automated. However, RPA is not a magic wand. The intended automated process needs to be selected carefully to benefit the most from RPA. How do you know you selected the most eligible process? Often the business process that is most eligible for RPA has activities that are repetitive, standardized and transactional. But how do you exactly know which business process this is? Although most people in an organization tend to know how the daily operation works the real deal is to discover the business process as objective as possible. This is where Process Mining comes in. Use Process Mining techniques to construct the as-is processes by using event logs, identify and select the processes that benefit the most from automation!

Advantage of Process Mining

Over the last decade business process management techniques practitioners constructed the as-is process using qualitative methods. Who hasn’t heard of the ‘brown paper sessions’? With Process Mining, available data from an organizations’ information system is used to construct an as-is process. This is also known as automated process discovery. As the process model is constructed by data it is objective and more reliable than as-is process constructed by qualitative methods. The data is extracted from IT-systems that store data and create event logs. The events range from a cash withdrawal from an ATM, a business owner applying for a permit and a sales clerk e-mailing your receipt after you bought a product in the Apple Store. After the as-is process is constructed it can be extended with perspectives that are relevant to the business case or the question that has to be answered. Hereby you can think about elements as the frequency of (repetitive) activities, significance (costs or resource use), slowdown time (as well as waiting time), process bottlenecks, waste and barriers for a smooth process.

Value of RPA and Process Mining

Now you know what RPA and Process Mining are, it is time to learn how your RPA-project can benefit from Process Mining. Be aware that you do not only have to use Process Mining as part of a RPA-project, it is also valuable before or after a RPA-project. Here are some examples:

Before
Use Process Mining before automation to optimize a business process.
Process Mining allows for discovering the business process where redundancies and process inefficiencies are exposed. This is beneficial for RPA, as for RPA to be most successful the variation in the business process should be minimized. Before RPA it is better to standardize the processes variants to create high volumes per process variation. By doing so, the RPA is used as efficiently as possible.

During
Conduct Process Mining as part of a RPA-project to provide an objective overview of business processes. The actual process is made transparent by a data-driven perspective whereby repetitive activities, bottlenecks and process loops are shown. By doing so we can identify the best processes to be (next) automated.

After
The data which is generated by automation enables continues improvement. Use the data for Process Mining techniques to analyze the process and determining the desired outcome, effectiveness of the automation and subsequent improvements.

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