=Paper=
{{Paper
|id=Vol-3299/Paper11
|storemode=property
|title=A Framework for Implementing Process Mining and Robotic Process Automation in Organizations (Extended Abstract)
|pdfUrl=https://ceur-ws.org/Vol-3299/Paper11.pdf
|volume=Vol-3299
|authors=Najah Mary El-Gharib
|dblpUrl=https://dblp.org/rec/conf/icpm/El-Gharib22
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==A Framework for Implementing Process Mining and Robotic Process Automation in Organizations (Extended Abstract)==
https://ceur-ws.org/Vol-3299/Paper11.pdf
A Framework for Implementing Process Mining and
Robotic Process Automation in Organizations
(Extended Abstract)
Najah Mary El-Gharib1,∗
1
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada
Abstract
Process Mining (PM) is a technique that combines data science and process modeling to discover process
models from system execution logs collecting activities and their timestamps, user identifiers, resources,
and other parameters. PM is used to understand and improve processes. Robotic Process Automation
(RPA) is an emerging technology that allows businesses to automate processes with software robots that
replicate repetitive human tasks. RPA helps achieve accuracy, consistency, and efficiency in performing
processes. Understanding processes is the key to automating them, but organizations often lack a
deep understanding of their as-is processes and often rely on guess work to discover their automation
opportunities. Using PM as a key enabler in discovering the processes that can be automated and in
monitoring improvements is not an easy task and comes with several challenges. The main focus of
this doctoral thesis is on introducing a new framework to help integrate the use of PM and RPA in an
organization’s context. The framework consists of several components including methods and tools.
Keywords
Process Mining, Robotic Process Automation, Integration
1. Introduction and Problem Definition
In the context of digital transformation, many organizations are automating their manual
processes to improve performance, save costs, and minimize errors while executing these
processes. However, there is currently a large amount of guess work in assessing the processes
that can be automated and in monitoring their actual improvement. In the past five years, there
has been a steep increase in the use of Robotic Process Automation (RPA) in organizations,
together with related tool support [1]. RPA, which uses software robots to automate human
tasks, has often been applied in the areas of administration and finance. RPA is frequently
implemented in cases where high volumes of data are processed through repetitive tasks. The
market of RPA solutions includes over 50 vendors who develop RPA tools that provide different
functionalities to automate office tasks in an intelligent way [2].
Most organizations realized the need to automate their processes where it makes the most
sense and where it positively impacts their business. The challenge here is not limited to how
ICPM 2022 Doctoral Consortium and Tool Demonstration Track
∗
Corresponding author.
Envelope-Open nelgh031@uottawa.ca (N. M. El-Gharib)
Orcid 0000-0001-6924-6154 (N. M. El-Gharib)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
51
to automate processes; it is also essential to understand which processes can benefit from
automation. The problem addressed in this thesis is to tailor process mining techniques in
order to discover candidate routines for automation using RPA, while taking into consideration
the type of the processes and how organizations can implement the integration of the two
technologies.
2. Research Goal and Research Questions
Having clear visibility into the organization’s current processes before automating them is key
to successful automation. Unfortunately, there is currently a large amount of guess work in
assessing the processes that can be automated and in monitoring their actual improvements.
Accordingly, this research aims to produce a framework that can leverage process mining
techniques to discover candidate routines for automation. Its research questions are:
• RQ1: What are the challenges encountered by organizations that want to adopt process
mining and robotic process automation technologies?
• RQ2: How can the synergy between process mining and robotic process automation
accelerate the planning of automation projects and provide continuous monitoring?
• RQ3: How can process mining methods be tailored to discover the candidate routines
that can be automated using robotic process automation?
3. Research Plan
3.1. Research Methodology
In order to conduct a good research project and study, an appropriate methodology is needed.
Design Science Methodology in Information System [3], is selected for this PhD thesis since it
aims to study the interaction between people, technology, and organizations. The framework
proposed will be developed following this approach with several iterations at each step.
As the first step, in order to answer RQ1, we conducted a Systematic Literature Review
(SLR) to assess the applicability of process mining algorithms in accelerating and improving the
discovery of processes that can be automated and implementations using RPA [4]. The literature
review focused on the methods used to record the events that occur at the level of the user
interactions, and on the preprocessing methods used to discover candidate routines that can be
automated. Additionally, the SLR covers the challenges that are encountered throughout the
project life cycle. In order to further understand which processes are currently being automated
within organizations and what challenges they face, how they are identifying processes that can
be automated, we will be launching a survey to understand what processes organizations are
currently automating using RPA and whether they are using any tools or techniques to discover
the routines that can be automated. A subset of the survey respondents will also be involved
in more detailed interviews. The purpose of conducting interviews is to help assess whether
the proposed framework could potentially help organizations identify processes that can be
automated using RPA. The outcomes and analysis of the survey results and the interviews will
help in the definition and development of the proposed framework.
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3.2. Proposed Framework
The framework focuses on how process mining can be integrated with RPA projects. Using
the two solutions and integrating them into an organization’s infrastructure is not a simple
problem to solve. There are several components that play a role, and several challenges are
expected at each phase. The framework will be structured from several components including
1) selected process, 2) datasets, 3) PM tool, 4) RPA tool, and 5) list of methods. For example, a
data preprocessing method is needed, data restructuring can be another method needed, and
several other methods will be identified as the problem is explored and investigated.
The first component is the process selected for process mining and automation. The datasets
component represents the event logs that capture the selected process. This component requires
selecting the systems that collect the datasets related to this particular process. The datasets
component represents the event logs that capture the selected process. This component requires
selecting the systems that collect the datasets related to this particular process. The desired level
of granularity for the collected event logs is also required as this will determine the necessary
preprocessing techniques that are needed. A full assessment is needed to identify the datasets
related to a process.
PM tools are able to extract knowledge and generate process models from event logs collected
in information systems. A list of requirements will be identified to help selecting the most
appropriate tool to be used for analysis. These requirements will cover, for example, whether
the tool is hosted on premise or on the cloud, its security features, and data source connectors.
The conducted survey will help discover which PM tools are widely used within organizations.
Similarly, an RPA tool is another component required to implement the software bots. Selecting
the appropriate tool is another challenge and listing the criteria that can be used to select the
tool is needed for this component. The features the tools provide will play a role in making
decision on which tool to select. Additionally, this will help in assessing how well several PM
tools can interact and integrate with RPA tools,to what extend they can be integrated together
in an organization infrastructure, and what challenges are encountered.
Finally, the framework will include a list of methods that can be used throughout the project
phases. Each method will have a list of inputs and will generate an output that will be used in
another phase. The list of methods will include:
• Problem identification and formulation: based on the organization’s objectives. For
example, which processes can be automated and what is the success rate of this automation
using RPA.
• Process domain selection: based organization’s objectives and the problem that they are
trying to solve; a process will be selected for analysis. Identifying the process that we are
analyzing is the starting point.
• Requirements gathering: To understand the scope of the analysis and the project, this
method includes identifying the information systems that collects the data associated
with the selected process, as well as gathering the information about the stakeholders
involved and the analysis questions that need to be answered.
• Datasets and traces collection: collecting datasets with the appropriate level of granularity
is the starting point. Since we are looking to automation tasks performed by humans,
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then the datasets and traces collected should be at the user interaction level with an
application.
• PM and RPA tools selection: there are several tools in market to apply process mining
and other set of tools to implement RPA solution, but selecting the right tools according
to many factors within each organization is one of the main challenges.
• Preprocessing techniques for the dataset which are needed to identify the candidate
routines that can be automated.
• Identifying whether a process can be automated based on a list of criteria such as the
frequency of execution, the number of variants, and the number of exceptions.
• Identifying what process steps to automate. Several methods have been developed to
identify candidate routines for automation building on exciting techniques [4, 5].
• Implementing new methods is another major component of this framework. Several
methods are needed to record user interactions, converting the recorded data into event
logs, preprocessing event logs to identify candidate routines for automation, implementing
automation solutions. There are several methods that already exist, so I will be leveraging
them in my solutions.
• Continuous monitoring for the robots after deployment. One of the main benefits of
process mining is the continuous monitoring approach that it can help with, so the purpose
is to leverage that after the automation deployment in order to help with monitoring the
performance of the software bots that were implemented, in order to identify any errors,
or unexpected behaviours.
3.3. Evaluation
In this research, my evaluation plan consists of a case study which is an observational evaluation
method according to the Design Science Methodology [3]. The plan is to collaborate with an
industry partner to conduct the case study on a selected process with real data. The case study
could be followed by interviews to validate some of the results with different stakeholders. The
purpose of this case study is to apply the methods and tools that are part of the framework
explained previously. I might be doing several case studies to assess different types of processes
to answer the research questions and attempt limited generalization. I am also considering
applying the framework to one process that is executed in different organizations in order to
compare whether the same methods can be used.
3.4. Technical Challenges
The challenges expected in this research are rooted in the recognized challenges of both process
mining and robotic process automation. While conducting the systematic literature review,
I extracted several challenges from the existing literature on process mining, RPA, and their
intersection. Intersecting challenges include recording user interaction logs at an appropriate
level of granularity, generating event logs from user interfaces, filtering noise, finding frequent
patterns, extracting routines, segmenting event logs, and simplifying them. I expect other types
of challenges to be identified from the survey and interviews.
54
4. Current Stage of this Research Project
An awareness of the thesis’ research problem is developed after doing a literature review
to examine the approaches and techniques where process mining techniques were used to
understand the as-is processes that can be automated with software bots. Since the literature
currently lacks a review that covers the intersection of process mining and robotic process
automation, I conducted a systematic literature review that mainly focuses on the methods to
record events that occur at a user interaction level, and on the preprocessing methods that are
needed to discover routines that can be automated [6]. The second step is to conduct a survey
in the North-American market to understand how the different organizations are currently
implementing RPA, for example for which processes and with what success rates, in addition
to process mining implementations, and how they identify if a process can be automated.
Additionally, the survey will include questions such as organization size, industry, and process
types. The survey will be followed by a series of interviews as explained above. After I finalized
my research plan and proposal with the timeline, I will start the development of the framework
components.
Acknowledgment
This research is sponsored by NSERC and the University of Ottawa. I am thankful to the
ICPM’22 Doctoral Symposium organizers, reviewers, and participants for the useful feedback
that was provided. Special thanks to Marwan Hassani and Agnes Koschmider for organizing a
successful Doctoral Symposium day.
References
[1] W. M. Van der Aalst, M. Bichler, A. Heinzl, Robotic process automation, Business &
Information Systems Engineering 60 (2018) 269–272.
[2] C. Dilmegani, Top 53 RPA tools / vendors & their features, 2022. https://research.aimultiple.
com/rpa-tools/.
[3] K. Peffers, M. A. Rothenberger, W. L. K. Jr. (Eds.), DESRIST 2012, volume 7286
of LNCS, Springer, 2012. URL: https://doi.org/10.1007/978-3-642-29863-9. doi:10.1007/
978- 3- 642- 29863- 9 .
[4] A. Jimenez-Ramirez, H. A. Reijers, I. Barba, C. Del Valle, A method to improve the early
stages of the robotic process automation lifecycle, in: CAiSE 2019, Springer, 2019, pp.
446–461.
[5] V. Leno, A. Augusto, M. Dumas, M. L. Rosa, F. M. Maggi, A. Polyvyanyy, Identifying
candidate routines for robotic process automation from unsegmented UI logs, in: ICPM
2020, IEEE, 2020, pp. 153–160.
[6] N. M. El-Gharib, D. Amyot, A review of data-driven robotic process automation exploiting
process mining, 2022. URL: https://arxiv.org/abs/2204.00751. doi:10.48550/ARXIV.2204.
00751 .
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