How can you maximize the return on investment (ROI) for your Robotic Process Automation (RPA) project? This is a persistent and crucial question for organizations striving to fully unlock the potential of RPA.
Despite being viewed as a game-changer for digital transformation, reports from prominent consulting firms like EY and PwC indicate that half of RPA projects fall short in achieving the anticipated ROI. Reasons for these shortcomings range from human errors in the implementation team to technology-related challenges.
With over three decades of expertise in process excellence, QPR Software has observed numerous customers facing challenges in implementing effective process automation, often falling short of their desired ROIs. In this blog post, we aim to unveil the underlying causes of the most prevalent RPA issues by addressing the following questions:
1. What is RPA?
2. What are the common pitfalls in RPA implementation?
3. What are the common approaches to RPA?
4. How to identify RPA opportunities?
5. How can you utilize Process Mining in your RPA project?
1. What is RPA?
Gartner (2019) defines Robotic Process Automation (RPA) as a productivity tool enabling users to set up scripts or “bots” to replicate specific tasks within a business or IT process. RPA combines UI interaction and descriptor technologies across various software applications to manipulate data, trigger responses, execute transactions, and transfer data between applications.
Interestingly, given its nature, this tool could be more accurately termed robotic ‘task’ automation, as it involves automating individual tasks previously done by humans. Nevertheless, these bots ultimately contribute to enhancing overall process efficiency.
While identifying suitable processes is crucial for RPA success, gaining a comprehensive understanding of how a process operates in an organization through workshops and interviews can be challenging due to human bias.
This is where Process Mining becomes valuable. It collects data from event logs in an organization’s systems, providing a holistic view of end-to-end processes, whether executed by humans or bots.
The benefits of using Intelligent Process Mining in RPA projects are twofold. Firstly, it identifies process inefficiencies, bottlenecks, and RPA opportunities. Secondly, it monitors and configures tasks performed by bots, predicts process outcomes, and suggests necessary actions to the process owner, proactively addressing potential RPA issues before they occur.
2. What are the common pitfalls in RPA implementation?
Despite being viewed as a silver bullet for numerous organizations’ digital transformation initiatives, reports from leading consulting firms like EY and PwC reveal that half of RPA projects fail to achieve the desired ROI. The reasons for these failures can range from human errors in the implementation team to technology-related issues.
In our extensive experience supporting RPA implementation projects for large organizations globally, we’ve observed that most RPA failures originate from selecting the “wrong process” to automate.
1. When your processes have too many exceptions.
Solving exception cases, which make up only 20% of your business volume, can consume 80% of your organization’s efforts. From the perspective of RPA implementation, introducing a robot into a process with numerous exceptions can jeopardize the achievement of your desired ROI. Constantly reconfiguring bots to handle diverse situations may result in significant costs, including millions of euros in downtime, maintenance, and support.
Process Mining provides visibility into the number and root causes of exceptions in your processes. It enables you to reduce the frequency of exceptions and accurately identify tasks best suited for automation, minimizing downtime and optimizing efficiency.
2. When you have processes with no business significance.
It’s logical not to invest in a process of minimal business significance. However, these seemingly less important processes may impact other crucial ones. Once again, a thorough understanding of your end-to-end process is crucial to identify the right candidate for automation.
3. When cost of error in execution is too high or the processes are too complex.
Certain tasks may be simpler without bot implementation, even if it requires human double-checking for each task. Process Mining provides a comprehensive view of all processes in your organization, making it easy to identify these tasks within your end-to-end process.
In our extensive experience supporting RPA implementation projects for large organizations globally, we’ve observed that most RPA failures originate from selecting the “wrong process” to automate.
4. When there are too many high-level cognitive tasks.
In such cases, use Intelligent Process Mining software like QPR ProcessAnalyzer, incorporating essential machine learning features like Clustering Analysis and Predictions to oversee the automation process.
In our extensive experience supporting RPA implementation projects for large organizations globally, we’ve observed that most RPA failures originate from selecting the “wrong process” to automate.
3. Over-ambitious vs. task-based approach
Implementing RPA in your organization can be approached in two ways: automating the entire end-to-end process or automating specific tasks within a process.
Attempting to automate a lengthy end-to-end process that spans months and involves numerous tasks is not ideal for RPA, which is designed for executing individual small tasks. Trying to automate the entire process can be overly ambitious, costly, time-consuming, and may hinder your ROI.
The optimal approach is to implement RPA by breaking down processes into tasks and automating selected ones to enhance the overall process. Identifying these tasks is where Process Mining comes in.
4. How to identify RPA opportunities?
Using Process Mining for complete process transparency allows you to effortlessly spot three ideal automation opportunities.
- Identifying regular bottlenecks that hinder overall processes.
- Pinpointing tasks prone to errors by employees, where manual work is less efficient than automation. Process Mining algorithms aid in uncovering the root causes of these mistakes.
- Identifying tasks disliked by employees due to being either too dull or physically demanding.
5. How to utilize Process Mining in your RPA project?
From our experience assisting customers with their RPA projects, four key steps leverage Process Mining in RPA implementation:
- Visualize and analyze your existing processes.
- Identify the most appropriate process for RPA.
- Automate the most lucrative processes and continuously monitor your bots.
- Follow up and enhance by employing machine learning in our Process Mining tool to predict potential issues in process steps.