Mining business processes preparing for hyperautomation, and discovering decision points

Once insight into the value chains and business processes is established, blank spots may appear. Often the documented business processes are only partially followed and pragmatically adapted. The manual adaptions need more documentation but leave traces in the data, describing each process's activities, inputs, and outputs.?

Process mining, one of the hyperautomation core technologies, can validate documented business processes by comparing them to the actual behavior captured in event logs and detecting discrepancies between the intended and actual execution of operations. Doing so highlights the areas where the documented procedures may need to be revised or updated. In addition, by analyzing the event logs, enterprises can identify bottlenecks and inefficiencies in their operations and patterns of manual behavior that may have yet to be recognized. As a result, process mining provides enterprises with valuable insights into how their business processes operate, identifying bottlenecks, inefficiencies, and areas to focus on with hyperautomation.??

Data from logs?

Process mining uses data from event logs typically captured automatically by business applications. These logs contain measurement points that provide detailed information about the execution of business processes, including the sequence of activities, the time taken for each activity, and the resources used. This model can be compared to the intended process model to identify deviations in the process flow and actions that take longer than expected, are frequently repeated, or involve many errors or exceptions: candidates for hyperautomation focus to improve resource utilization. Usually, heat maps are used to visually represent the process flow and identify the activities causing delays or inefficiencies. In addition, heat maps (like, for example, Camunda Optimize) are great for interactively analyzing processes.??

Process mining can provide valuable data about how processes at a specific moment in time were being executed

Updating process models?

One of the key benefits of process mining is that it provides an objective view of how processes were in the past or are currently being executed based on historical data. Moreover, the decision criteria based on policies and legislation would have changed the process orchestration flow, introducing new activities or making other tasks obsolete. Therefore, mining processes must be committed with the care and knowledge of these change agents. However, done well, this view on historical data can be precious for updating process documentation when documentation is outdated, inaccurate, or incomplete. Moreover, even the past individual process versions can be detected and mapped.?

Therefore, three steps need to be taken:?

• Identify documentation gasps: process mining can provide valuable information about how processes have been executed in the past up to today. For example, operations may have changed due to legal requirements or policies requiring updating the process orchestration.?
• Collaborate with process owners: they are typically the best source of information about how processes should be executed within an organization and which legal aspects and policies need consideration.?
• Update documentation: this may involve creating new or updating versions of documentation to reflect past and current processes.?

Robotic Process Automation?

Robotic Process Automation (RPA), like open source Robot Framework, can automate manual, repetitive, rules-based tasks involving a high volume of data or transactions identified with process mining as 'hot spots.' Furthermore, RPA software robots or bots can do this without changing the existing digital systems, including desktop applications, web-based applications, and databases. The bot's actions need programming, such as clicking buttons, filling out forms, or extracting data. Therefore, they should be seen as additional software that needs further management and maintenance. RPA is a quick fix for automating repetitive and time-consuming manual tasks. However, this temporary fix introduces technical debt that needs resolving later on. RPA should never be seen as the final solution; however, it can be beneficial to elicitate the conditions and business rules of manual tasks, preparing for the next step in hyperautomation: automating manual decisions.?

Digital Decisioning?

Process mining can help identify the best candidates for automated decisioning by identifying repetitive activities and manual tasks involving manual, often biased complex decision-making. In addition, enterprises can benefit from the speed and accuracy of digital decisioning technologies, including machine learning algorithms.?

Process mining helps identify the best candidates for digital decisioning in several ways:?

• Decision points: identify points within processes and tasks where operational decisions are made based on rules or complex criteria. In the current process model, making decisions in decision steps is common, which could be optimized into one single decision using hyperautomation.?
• Decision quality: analyze the quality of decisions made during the execution of tasks that benefit from reducing errors, increasing consistency and accuracy, or improving response times.?
• Resource utilization: Process mining can analyze the utilization of resources, such as time, money, and personnel, during the execution of processes and tasks, identifying areas where hyperautomation can improve resource utilization and reduce costs.?

Hyperautomation?

Process mining can help organizations leverage hyperautomation in several ways:?

• Identify automation opportunities: Process mining can help organizations identify which processes are good candidates for automation.?
• Prioritise automation opportunities: based on the potential benefits, such as reducing cycle time, improving accuracy, or reducing costs. Furthermore, quick fixes like RPA can be applied, and their final solutions, like digital decisioning, can be planned.?
• By using process mining to identify the best candidates for automated decisioning, organizations can improve the efficiency and accuracy of their decision-making processes, reduce costs, and improve their overall business operations.?

Overall, process mining can be a powerful tool for checking and upgrading process documentation by providing valuable data about how processes at a specific moment in time were being executed and by identifying areas for improvement.?

In the next blog, another key hyperautomation technology is visited: Robotic Process Automation (RPA).

?More articles published in the hyperautomation series

• Enterprise motivation to pursue hyperautomation
• Core technologies for driving hyperautomation
• How enterprises are optimizing business processes and value chains for driving hyperautomation
• Mining business processes preparing for hyperautomation and discovering decision points
• Robotic Process Automation, a step towards?Hyperautomation
• Digital Decisioning at the heart of hyperautomation, creating business value
• How applying Artificial Intelligence helps hyperautomation
• Six preparation steps to leverage Machine Learning for hyperautomation
• 8 technologies around the core in the epicenter of Hyperautomation