Process Mining and RPA: Automation Done Right
Joohee Sim | Sep 11, 2023 | 9 min read
RPA and process mining, when combined, offer a holistic approach to automation, providing visibility and efficiency for businesses in the digital era.
RPA has empowered numerous organizations to operate smarter, leaner, and more effectively through automated workflows. Research from Consegic Business Intelligence predicts that worldwide Robotic Process Automation (RPA) market is projected to grow at a CAGR of 30.4% from 2023 to 2030. The growth of the RPA market is being driven by the increasing adoption of RPA solutions across various industries, including finance, retail, manufacturing, and healthcare. Deloitte’s Global Intelligent Automation survey underscores this trend, revealing that many organizations have already embarked on their automation journey for increased productivity, cost reduction, improved accuracy, and enhanced customer experience. In fact, 74% of respondents have already initiated RPA implementations.
However, despite its promising potential, RPA faces barriers that limit its effectiveness. Process mining, as a complementary technology, can play a pivotal role in addressing these challenges. In this exploration of RPA and process mining, we’ll take a look at how combining these technologies can elevate automation to new heights.
What is RPA?
RPA, which stands for Robotic Process Automation, is a technology that uses programmed software robots, or “bots,” to automate repetitive and rule-based tasks within business processes. These bots mimic human actions by interacting with various digital systems and applications involved in a process. Whether it’s data entry, calculations, web scraping, or triggering responses, RPA takes over these routine tasks and serves as a virtual workforce, allowing human employees to focus on more strategic and value-added activities.
Three Types of RPA
According to Robotic Process Automation (RPA) – Digitization and Automation of Processes (Langman & Turi 2023), RPA can be divided into three types – attended RPA, unattended RPA, and hybrid automation. –
Attended RPA: Also known as Robotic Desktop Automation (RDA), these robots run on the user’s desktop and must be manually triggered by the user. Like Excel macro, they can execute assigned repetitive tasks on demand.
Unattended RPA: These robots usually run on a server in the background and performs actions independently without user interaction. They are triggered automatically by triggers such as the receipt of an email or the presence of a file in a folder.
Hybrid Automation: Hybrid models combine Attended and Unattended RPA. For example, Attended RPA can trigger a robot running under Unattended RPA, or a human might intervene in the middle of an Unattended RPA process to make decisions (“human-in-the-loop”).
How RPA helps businesses
RPA offers a wide range of benefits to organizations across various industries. Here are some key advantages of implementing RPA:
- Increased Efficiency: RPA significantly boosts operational efficiency by automating repetitive tasks to operate 24/7, reducing processing times and handling more workload.
- Cost Reduction: RPA lowers labor costs, minimizes errors, and leads to long-term cost savings, making it a cost-effective automation solution.
- Improved Accuracy: RPA bots perform tasks with a high degree of accuracy, reducing the likelihood of errors and rework.
- Enhanced Productivity: RPA allows human employees to focus on more creative, strategic and value-added activities, enhancing overall workforce productivity.
- Scalability: RPA implementations can be easily scaled up or down to adapt to changing business requirements, ensuring flexibility and agility.
What’s the difference between RPA and AI?
You might be wondering, what sets RPA apart from Artificial Intelligence (AI)? After all, both are used to automate tasks and processes. Both RPA and AI are components of intelligent automation (IA), which refers to the combination of modern digitization technologies that provide automation capabilities for improving business processes. Despite their similarities, RPA and AI differ in terms of complexity and cognitive capabilities.
RPA follows explicit instructions or if-then rules to automate repetitive, rule-based tasks based on structured data. It is a highly process-driven technology with limited data extraction capability.
By contrast, AI encompasses a wide array of technologies that enable machines to learn and think like humans. These intelligent, data-driven systems can do a lot more than just follow predefined rules; they can work with unstructured data to understand natural language, recognize patterns, and make decisions. AI includes subfields like Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision.
By combining RPA and AI, businesses can achieve intelligent automation that goes beyond rule-based automation to handle complex tasks without human intervention.
What are the main challenges of RPA implementation?
Implementing Robotic Process Automation (RPA) can be a game-changer for businesses, but it’s not all smooth sailing. Like any transformative technology, RPA comes with its fair share of challenges. Deloitte’s Global Intelligent Automation survey consistently found that immature and fragmented processes pose the most significant hurdle to automation. Lack of a clear vision and IT readiness were also identified as major barriers to scaling.
When processes are fragmented, it is difficult to choose the right processes to automate and to standardize processes. Not every task is suitable for automation, and selecting the wrong ones can lead to wasted resources. Overcoming these obstacles is crucial to unlocking the full potential of RPA and reaping the benefits of increased efficiency and reduced operational costs.
According to Gartner’s VP analyst Nisha Bhandare, “Despite ongoing investment in RPA, CFOs are realizing they need a broader toolkit to realize their full automation objectives…To realize higher value from their RPA investments, CFOs are turning to a suite of complementary efficiency technologies, such as process mining, which will remain a future driver of growth for RPA in the coming years.”
Should I use process mining with RPA?
Process mining can be a valuable complement to RPA, especially for companies with complex processes. It can be used in the run-up to RPA design and in post-deployment optimization. Here are some benefits of using process mining with RPA:
- Process Discovery: One of the initial challenges in RPA implementation is understanding and documenting ‘real’ processes and identifying the most suitable processes for automation. Process mining provides a comprehensive view of existing processes across various IT systems, revealing bottlenecks, repetitions, deviations, and more. The assessment of candidates for RPA may depend on several factors, including:
– High-volume, manual, repetitive tasks
– Rule-based tasks
– Number of tasks performed
– Proportion of exceptions handled
- Process Monitoring and Continuous Improvement: RPA implementations are not static; they need continuous monitoring and optimization to maintain their efficiency. Process mining offers real-time monitoring capabilities, allowing organizations to track the performance of RPA bots and assess their impact on business outcomes.
- Data-Driven Decision-Making: Process mining offers data-driven insights that can guide strategic decisions related to RPA. For example, it can help organizations decide whether to expand RPA initiatives to other areas or modify existing ones based on process variations.
PuzzleData’s ProDiscovery provides a user-friendly interface for visualizing and analyzing processes, ensuring that RPA initiatives are grounded in data-driven process intelligence. Learn more about ProDiscovery here.
In conclusion, RPA is a powerful automation tool that becomes even more effective when paired with process mining. Process mining acts as an x-ray of business processes, offering visibility into end-to-end operations, helping organizations make smarter decisions about where to concentrate their RPA efforts. Together, these complementary technologies provide a holistic approach to process automation and optimization, enabling businesses to achieve greater operational efficiency in the digital transformation era.
Langmann, C., Turi, D. (2023). Robotic Process Automation (RPA) – Digitization and Automation of Processes: Prerequisites, Functionality and Implementation Using Accounting as an Example. Germany: Springer Fachmedien Wiesbaden.