Abstract:
Robotic Process Automation (RPA), the next level of business process
automation, provides adaptive and transformative solutions to replace timeconsuming,
non-value-adding,
and
repetitive
human
tasks
in
a
Business
Process
(BP).
RPA
based BP transformation projects differ from typical software development
projects because RPA bots are developed on stable code. It is counterproductive to use
existing software processes in RPA projects. A process template (i.e., software
implementation process and metrics to track the project) is yet to be derived for RPA
projects. The estimated initial RPA project failure rates are 30-50%, and the lack of a
fitting implementation process is attributed as one of the key contributors to failure.
We addressed this gap and derived a novel process for RPA projects named Raban and
metrics to track RPA projects.
Scrum was used to formulate the Raban. Focus group discussions were
conducted with scrum teams and identified 80 challenges. Those analyzed in
Straussian grounded theory are grouped into six categories (i.e., lack of agile mindset,
inconsistency in story estimation, client management issues, lack of adherence to agile
practices, scope change in requirement freeze, and lack of quantitative measurement).
Prioritized 15 burning challenges were classified based on significance, and taxonomy
was developed. Derived steps to estimate RPA use-cases and a framework to achieve
customer satisfaction adopting design thinking practices in agile projects. Moreover,
17 software metrics and three artifacts were derived and validated in five scrum
projects. Raban was derived based on the solutions identified and further fine-tuned
based on the feedback from follow-up interviews with the stakeholders and two
workshops conducted with the other RPA project teams. After that, 14 metrics and two
artifacts were derived for Raban and validated in a RPA project. Moreover, to select
the right candidate BP for RPA transformation, predictive machine learning model was
developed, where the decision made as yes/no on RPA suitability. We used 16 factors
and a two-class decision forest classification model to develop the model.
Citation:
Padmini, K.V.J. (2021). RABAN - a software implementation process for robotic process automation (RPA) projects [Doctoral dissertation, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/21177