Robotic Process Automation (RPA) is transforming how businesses handle repetitive tasks, freeing up employee time for more strategic activities. On the other hand, process mining offers deep visibility into existing processes, acting as an “X-ray” to identify inefficiencies and areas ripe for improvement.
Businesses need a clear understanding of the distinctions between RPA and process mining to take full advantage. See use cases, and examine how they can be used:
| RPA | Process Mining | |
|---|---|---|
Primary Goal | Automate rule-based tasks to improve efficiency and reduce errors | Analyze business processes to identify bottlenecks, inefficiencies, and opportunities for improvement |
How it Works | Automate tasks using pre-programmed rules and scripts | Extract event logs, create visual process maps and compare with ideal workflow |
Use Cases | Data entry, CRM updates, invoice management, etc. | Process audits, compliance checks, monitoring overall process performance, etc. |
Focus | Improve efficiency by automating routine tasks | Identify bottlenecks and gaps in workflows, offering opportunities for process improvement |
What is RPA?
RPA (Robotic Process Automation) is a technology that allows businesses to automate repetitive, rule-based tasks using software robots or “bots.” RPA works by automating these manual processes without human intervention.
RPA can be implemented through various means, such as coding, no-code deployment, drag-and-drop interfaces, or hybrid automation.
Why is RPA important today?
RPA becomes important because it can:
- Support Remote Workforce: By automating routine tasks, RPA enables employees to monitor and manage bot activities remotely via dashboards, ensuring that business operations continue smoothly regardless of employees’ physical location.
- Bridge Gaps Between Legacy Systems: RPA allows businesses to integrate and orchestrate tasks between different legacy systems without the need for complex API solutions, enabling businesses to streamline their operations without extensive system overhauls.
- Function Across Various Virtual and Remote Systems: RPA bots are versatile and can operate across different virtual and remote operating systems, making them ideal for businesses with distributed teams or remote work environments.
- Operate More Efficiently and Economically Than Outsourcing: RPA provides a cost-effective alternative to business outsourcing by automating repetitive tasks, reducing errors, and increasing the speed and accuracy of processes, all while lowering overall operational costs.
What are the use cases of RPA?
RPA’s use cases include, but are not limited to, the following:
- Data entry, updates, validation,
- Document understanding,
- Lead nurturing,
- Updating CRM,
- Regular diagnostics,
- Regulatory compliance,
- Updating user preferences,
- Bank statement reconciliation,
- Resume screening,
- Employee onboarding,
- Travel and expense management,
- Validating and processing loan applications,
- Bill of Material (BOM) processing, and more.
Learn more about the use cases of RPA in different business applications.
The common denominator with most of these tasks is that they are not particularly heavy-duty but time-consuming and mundane, which translates to workers’ time loss, could result in bottlenecks (imagine hundreds of candidates’ resumes getting piled up slows down recruitment), and are prone to errors.
RPA, by following a pre-determined, rule-based script, will execute each task as it’s been programmed to do so. This minimizes the risk of manual mistakes and completes tasks faster and on a higher scale.
What is process mining?
Process mining is a process technology that acts as an X-ray, shining a light on the actualities of a process instead of the ideals. It does that by extracting real-time event log data and activity records to show how each specific workflow is carried out from beginning to end.
Explore more on how process mining works, how it leverages machine learning, and the top algorithms it employs.
The insight that analysts gain from using process mining to direct their processes can act as an indication of which areas or specific sub-processes need optimization, largely through automation.
Discover how process mining enhances and streamlines process:
- KPIs
- Management (BPM) & improvement
- Control
- Planning & design
- Mapping & modeling
- Documentation
- Visualization
- Simulation
- Risk mitigation.
Why is process mining important today?
Process mining is important because it rids managers of the romanticized versions they might have about their processes and instead presents them with the actualities of how things are.
For instance, a company might revitalize its Bill of Material (BOM) processing. However, the managers might not be aware of the time lapse between checking the inventory level of the intermediate good that goes into the production process and its input on the BOM. It’s through mining this process that the analysts can be provided with data-driven insights into how long each process takes.
Learn how process mining contributes to process knowledge, process intelligence, and operational efficiency.
Having this information can then fuel the automation initiatives because the analysts would know that, perhaps, automating inventory control can first, decrease the time at which inventory levels are established, and second, streamline the transfer of data from the ERP database onto the BOM.
Learn more about the importance of process mining.
What are the use cases of process mining?
Companies can run process mining technology on the majority of their front and back-end office processes to achieve deeper levels of insight and understanding as to how they operate.
Specifically, users have claimed to be using process mining for these purposes:
- Auditing & Compliance
- Business process improvement
- Business process management
- Process automation
- Digital transformation
- IT operations
- Logistics
- Manufacturing
- Healthcare
- Finance, banking & insurance
- Sales & Marketing
- HR
- Cybersecurity
- Telecom
Learn more about process mining use cases.
There’s one common denominator with all the use cases that users have mentioned: they’ve used process mining as a stepping stone for automating their processes. So if the insights gained from process mining are not acted upon, they do not provide much value to enterprises by themselves. We will go over how RPA and process mining should be used in tandem later on.
Discover process mining real-life examples.
How are RPA and process mining different?
RPA is a tool for automating processes. Process mining is a process intelligence like task mining or process modeling that provides insights about processes that require improvement, harmonization, or automation.
So one tool is for identifying automation opportunities, while the other is an automation enabler. If companies leverage RPA in processes that they have little knowledge about, the automation initiative might fail because maybe:
- The wrong process had been automated,
- The scale of the automaton project was not enough,
- The process was not manual-ridden to benefit from the antidote,
- The process was too complex with multiple interdependencies, and more.
We have an article that discusses common RPA pitfalls in more detail.
On the other hand, even if companies do get the most in-depth looks into their processes, it will not count for much if they do not get used as the bedrock upon which automation gets built. That is why it’s important to act upon the gained insights.
How to use RPA and process mining complementarily?
The insights gained from the process mining tools can shed light on the actualities of the existing processes. Specifically, it can lay bare:
- Process variations, durations, and other log events,
- Pain points and bottlenecks,
- The extent of the process’ maturity (i.e. how many rounds of structural changes has it gone through within a specific timespan?)
- And the extent of manual involvement in it.
Moreover, businesses can, through process mining’s DTO (digital twin of an organization) feature that leading vendors offer, create a counterfactual scenario for their processes to get a glimpse at the optimized version of their current workflow.
For example, a DTO of the claims management process for an insurance company might show that data extraction from documents, such as written witness statements and medical reports, can be done at a faster rate. The CoE or automation consultants can then suggest the insurance company leverage IA-enabled RPA bots that benefit from OCR functionality to read documents and extract information from them at a faster rate.
FAQ
How can businesses use process mining to improve RPA implementations?
Process mining helps businesses by offering a data-driven picture of existing business processes, identifying bottlenecks, manual tasks, and potential automation opportunities. This insight guides companies to target RPA investments effectively, avoiding automation of suboptimal or unsuitable processes and ensuring that automation efforts are aligned with process optimization goals.
Can process mining automation replace robotic process automation (RPA)?
No, process mining automation does not replace RPA. Instead, the two technologies complement each other. Process mining continuously analyzes process performance, identifies compliance issues, and uncovers automation opportunities. RPA then executes these automation initiatives, streamlining operations, improving operational efficiency, and reducing human actions.
In what ways can combining RPA with advanced process mining solutions enhance digital transformation efforts?
Combining RPA with advanced process mining solutions accelerates digital transformation by enabling companies to continuously improve their processes. Process mining provides visibility into process flows, revealing opportunities to streamline operations and improve processes. RPA then automates these repetitive tasks, enhancing efficiency, reducing costs, and saving time, thus maximizing the return on digital transformation and automation technologies.
For more on RPA and process mining
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