=Paper=
{{Paper
|id=Vol-2839/paper2
|storemode=property
|title=Towards Decision Management for Robotic Process Automation
|pdfUrl=https://ceur-ws.org/Vol-2839/paper2.pdf
|volume=Vol-2839
|authors=Simon Siegert,Maximilian Völker
|dblpUrl=https://dblp.org/rec/conf/zeus/SiegertV21
}}
==Towards Decision Management for Robotic Process Automation==
https://ceur-ws.org/Vol-2839/paper2.pdf
Towards Decision Management for Robotic
Process Automation
Simon Siegert and Maximilian Völker
Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
simon.siegert@student.hpi.de
maximilian.voelker@hpi.de
Abstract. Robotic process automation (RPA) is a rapidly growing tech-
nology for automating digital processes. While RPA allows the automation
of common tasks performed on a computer, there are only rudimentary
possibilities to represent decisions in RPA models. Especially workflows
that involve more than simple yes-no questions quickly result in confusing
models, which are difficult to understand and maintain. To overcome
these issues, an integration of an established decision management ap-
proach, Decision Model and Notation (DMN), into RPA is proposed and
motivated in this paper.
Keywords: Robotic Process Automation, RPA Lifecycle, Decision Management
1 Introduction
Automation based on process models has long been an area of interest in business
process management (BPM), for both the research and the enterprise world [13].
Robotic process automation (RPA) is a novel automation approach employing
software robots to automate undemanding tasks on the computer, that recently
gained more attention in research [2]. Whereas BPM systems target organization-
wide processes, RPA automates sequences of tasks that employees perform locally
on their computers by imitating the employee’s behavior on the graphical user
interface level [6, 12], making it a useful addition to BPM [14].
To enable business users and employees to create their own automations,
many current RPA vendors focus on the visual representation and modeling
of RPA robots [3]. However, a preliminary analysis of different RPA products
(UIPath, Blue Prism, Automation Anywhere, and Automagica) revealed disad-
vantages of these approaches, especially with respect to the design of decisions.
A majority of the representation types examined only supported simple if /else-
or switch/case constructs to model decisions. Since each decision branch must
be represented explicitly, the RPA models become complex, especially for more
elaborate decisions. This may lead to decision-intensive processes being discarded
for automation using RPA due to the excessive modeling effort and decreased
maintainability, although they might be well suited for automation.
J. Manner, S. Haarmann, S. Kolb, N. Herzberg, O. Kopp (Eds.): 13th ZEUS Workshop,
ZEUS 2021, Bamberg, held virtually due to Covid-19 pandemic, Germany, 25-26 February 2021,
published at http://ceur-ws.org
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
10 Simon Siegert and Maximilian Völker
This paper outlines a possible solution to overcome the issue of representing
complex decisions in RPA models. Based on the mature standard for decision
management, DMN [11], a way of integrating decisions in RPA robots is delineated,
maintaining the focus on business users and intuitive usability.
2 Related Work
In BPM, similar problems regarding the modeling of decisions in workflows have
been reported, such as complex, nested structures which are hard to maintain
and understand [1]. For one of the associated modeling notations, BPMN [10],
the problem was addressed by separating the decision logic and the control flow
using DMN (Decision Model and Notation) [5, 7]. DMN allows the modeling of
decision requirements and the specification of the underlying decision logic in
the form of a decision table [11]. In BPMN models, DMN is used to encapsulate
the previously branched and nested decisions into a single new decision activity.
This allows decision-intensive business process models to be presented in a clear
and compact way. Even approaches to extract the decision logic from process
models and replace it with DMN have been proposed [4].
However, although current graphical RPA approaches suffer the same prob-
lem, it has, to the best of our knowledge, not yet been addressed in research.
Nevertheless, since RPA software is known to be highly rule-based [12], it is
expected to benefit from the integration of a proven technique for data-based
and rule-based decisions, such as DMN.
3 Motivation
Consider the following scenario:
In a car dealership, orders
placed by employees for the
company are to be released. To
do this, they send their orders to
a secretary, who decides which
departments in the company
are eligible to approve an or-
der. Since orders can have differ-
ent costs and categories (such
Fig. 1. Scenario implemented with explicitly mod-
as ‘New Car’ or ‘Spare Parts’),
eled decisions (intentionally not readable)
and depending on this can have
one or more parties in the com-
pany authorized to review them (e.g., ‘Management’ or ‘Sales Department’), the
decision has a certain degree of complexity.
Implementing the scenario with RPA is a viable option here, as the costs and
category extraction, the decision, and the email notifications can be automated
using common RPA features. Figure 1 shows a possible implementation with
Towards Decision Management for Robotic Process Automation 11
the open source version of the RPA software Automagica1 , demonstrating that
explicit decision modeling can lead to an unreadable spaghetti-like model.
Here, the explicit but necessary modeling of the decision with the different
options of responsibility to decide on orders as well as the different combinations
of the input variables ‘cost’ and ‘order category’ leads to a nested decision tree
that is difficult to understand.
4 Integrating RPA with DMN
The research proposal is to apply elements from decision management of BPM
to the modeling and implementation of RPA systems. Hiding the complexity of a
decision using a decision engine makes the RPA process model more understand-
able. For example, Figure 2 shows a prototypical implementation of a software
robot that executes the same program logic as the model in Figure 1, but requires
much less modeling elements as the decision resides in a decision table that is
evaluated by a decision engine in the task “Decide on responsible party”. The
Fig. 2. Order review scenario modeled with RPA and DMN
decision takes place within a DMN decision table, which is not shown here. To
create the decision table, however, further modeling effort is necessary, especially
for users who are not familiar with DMN. Thus, two different modeling languages
have to be learned to create RPA process models.
The research approach is to investigate for all phases of the RPA lifecycle,
as defined by König et al. [9] and Jimenez-Ramirez et al. [8], how decisions in
RPA bots can be supported by DMN and how each lifecycle phase needs to be
adapted compared to existing approaches. The selection of suitable business pro-
cesses for automation, necessary capabilities of modeling tools and requirements
for RPA architectures (e.g., local decision engine versus external decision service)
as well as their implementation will be compared and discussed. Communication
flows between individual software components, verification of correct behavior
and efficient operation are also to be considered. In order to demonstrate the
feasibility and to evaluate the benefits of integrating DMN in robotic process
automation, several prototypes addressing the different architecture types (e.g.,
1
https://github.com/automagica/automagica/tree/
ae8a1846f23df6497e725c8db198b4420da82f12 (latest open source version)
12 Simon Siegert and Maximilian Völker
decision engine embedded in the RPA robot or connection to an external decision
engine) will be implemented and compared.
5 Conclusion
The proposed introduction of a dedicated decision component for RPA is ap-
plicable for rule-based and data-focused RPA processes. Although the decision
component should not and cannot be applied to every decision in RPA processes
(simple decisions can still be mapped using conventional means such as if − else),
more complex and data-driven processes are expected to benefit from the use of
more sophisticated decision management. Overall, it may lead to better monitor-
ing, easier updating, and higher overall comprehensibility of RPA models and
executions compared to explicitly modeled decision trees, which highlights the
need for further research in this direction. Future work could analyze whether
and how DMN in RPA could benefit from the integration of machine learning
technologies and conduct a user study to analyze the trade-off between reduced
model complexity and additional effort due to an additional modeling language.
References
1. van der Aa, H., Leopold, H., Batoulis, K., Weske, M., Reijers, H.A.: Integrated process
and decision modeling for data-driven processes. In: Reichert, M., Reijers, H.A.
(eds.) Business Process Management Workshops. pp. 405–417. Springer International
Publishing, Cham (2016)
2. van der Aalst, W.M.P., Bichler, M., Heinzl, A.: Robotic Process Automation. Business
& Information Systems Engineering 60(4), 269–272 (2018)
3. Aguirre, S., Rodriguez, A.: Automation of a business process using robotic process
automation (rpa): A case study. In: Figueroa-García, J.C., López-Santana, E.R.,
Villa-Ramírez, J.L., Ferro-Escobar, R. (eds.) Applied Computer Sciences in Engineering.
pp. 65–71. Springer International Publishing, Cham (2017)
4. Batoulis, K., Meyer, A., Bazhenova, E., Decker, G., Weske, M.: Extracting decision
logic from process models. In: Zdravkovic, J., Kirikova, M., Johannesson, P. (eds.)
Advanced Information Systems Engineering. pp. 349–366. Springer International
Publishing, Cham (2015)
5. Biard, T., Le Mauff, A., Bigand, M., Bourey, J.P.: Separation of decision modeling
from business process modeling using new “decision model and notation” (dmn) for
automating operational decision-making. In: Camarinha-Matos, L.M., Bénaben, F.,
Picard, W. (eds.) Risks and Resilience of Collaborative Networks. pp. 489–496.
Springer International Publishing, Cham (2015)
6. Flechsig, C., Lohmer, J., Lasch, R.: Realizing the full potential of robotic process
automation through a combination with bpm. In: Bierwirth, C., Kirschstein, T.,
Sackmann, D. (eds.) Logistics Management. pp. 104–119. Springer International
Publishing, Cham (2019)
7. Hasić, F., Devadder, L., Dochez, M., Hanot, J., De Smedt, J., Vanthienen, J.:
Challenges in refactoring processes to include decision modelling. In: Teniente, E.,
Weidlich, M. (eds.) Business Process Management Workshops. pp. 529–541. Springer
International Publishing, Cham (2018)
Towards Decision Management for Robotic Process Automation 13
8. Jimenez-Ramirez, A., Reijers, H.A., Barba, I., Del Valle, C.: A method to improve the
early stages of the robotic process automation lifecycle. In: International Conference
on Advanced Information Systems Engineering. pp. 446–461. Springer (2019)
9. König, M., Bein, L., Nikaj, A., Weske, M.: Integrating robotic process automation
into business process management. In: Asatiani, A., García, J.M., Helander, N.,
Jiménez-Ramírez, A., Koschmider, A., Mendling, J., Meroni, G., Reijers, H.A. (eds.)
Business Process Management: Blockchain and Robotic Process Automation Forum.
pp. 132–146. Springer International Publishing, Cham (2020)
10. Object Management Group (OMG): Business Process Model and Notation (BPMN)
Specification, Version 2.0. https://www.omg.org/spec/BPMN/2.0/ (2011)
11. Object Management Group (OMG): Decision Model and Notation (DMN) Specification,
Version 1.3. https://www.omg.org/spec/DMN/1.3/ (2019)
12. Syed, R., Suriadi, S., Adams, M., Bandara, W., Leemans, S.J., Ouyang, C., ter
Hofstede, A.H., van de Weerd, I., Wynn, M.T., Reijers, H.A.: Robotic process
automation: Contemporary themes and challenges. Computers in Industry 115, 103162
(2020)
13. Weske, M.: Business Process Management: Concepts, Languages, Architectures.
Springer, third edn. (2019)
14. Willcocks, L.P., Lacity, M., Craig, A.: The IT function and robotic process automation.
The Outsourcing Unit Working Research Paper Series (15/05), The London School of
Economics and Political Science, London, UK (2015)