Robotic Process Mining: Vision and Challenges

Robotic process automation (RPA) is an emerging technology that allows organizations automating repetitive clerical tasks by executing scripts that encode sequences of fine-grained interactions with Web and desktop applications. Examples of clerical tasks include opening a file, selecting a field in a Web form or a cell in a spreadsheet, and copy-pasting data across fields or cells. Given that RPA can automate a wide range of routines, this raises the question of which routines should be automated in the first place. This paper presents a vision towards a family of techniques, termed robotic process mining (RPM), aimed at filling this gap. The core idea of RPM is that repetitive routines amenable for automation can be discovered from logs of interactions between workers and Web and desktop applications, also known as user interactions (UI) logs. The paper defines a set of basic concepts underpinning RPM and presents a pipeline of processing steps that would allow an RPM tool to generate RPA scripts from UI logs. The paper also discusses research challenges to realize the envisioned pipeline

A Review of Data-driven Robotic Process Automation Exploiting Process Mining

Purpose: Process mining aims to construct, from event logs, process maps that can help discover, automate, improve, and monitor organizational processes. Robotic process automation (RPA) uses software robots to perform some tasks usually executed by humans. It is usually difficult to determine what processes and steps to automate, especially with RPA. Process mining is seen as one way to address such difficulty. This paper aims to assess the applicability of process mining algorithms in accelerating and improving the implementation of RPA, along with the challenges encountered throughout project lifecycles. Methodology: A systematic literature review was conducted to examine the approaches where process mining techniques were used to understand the as-is processes that can be automated with software robots. Eight databases were used to identify papers on this topic. Findings: A total of 19 papers, all published since 2018, were selected from 158 unique candidate papers and then analyzed. There is an increase in the number of publications in this domain. Originality: The literature currently lacks a systematic review that covers the intersection of process mining and robotic process automation. The literature mainly focuses on the methods to record the events that occur at the level of user interactions with the application, and on the preprocessing methods that are needed to discover routines with the steps that can be automated. Several challenges are faced with preprocessing such event logs, and many lifecycle steps of automation project are weakly supported by existing approaches.Comment: 29 pages, 5 figures, 5 table

Looking for the Why in Event Logs for Robotic Process Automation

The concept of Robotic Process Automation (RPA) has gained relevant attention in both industry and academia. RPA raises a way of automating mundane and repetitive human tasks requiring less intrusiveness with the IT infrastructure. Besides traditional user interviews and process document analysis, a common practice starts by observing the behavior of humans with the information systems while they perform the process to be automated. This sequence of human interactions with the user interface (i.e., mouse clicks and keystrokes) is stored in logs for later analysis. Analyzing these interactions brings significant benefits when conducting RPA projects. Nonetheless, some decision-based behaviors of humans require additional information to be explained. For example, a human may reject an invoice because some field is missing on a form. However, there is no interaction with that field since such information is not stored in the log. Therefore, this Ph.D. elaborates on a method to obtain additional information based on screenshots collected during the process execution. Some features are extracted from the screenshots to enrich the log, which is later used for classifying human decisions in a machine-and-human-readable form. The proposed method can be applied to generate advanced support in the RPA projects, e.g., producing an enhanced process analysis, supporting the robot development, or generating predictions and simulations. The approach has been validated using synthetic data where promising results were obtained.Ministerio de Ciencia, Innovación y Universidades PID2019-105455GB-C31Ministerio de Educación y Formación Profesional FPU20/0598

A framework to evaluate the viability of robotic process automation for business process activities

Robotic process automation (RPA) is a technology for centralized automation of business processes. RPA automates user interaction with graphical user interfaces, whereby it promises efficiency gains and a reduction of human negligence during process execution. To harness these benefits, organizations face the challenge of classifying process activities as viable automation candidates for RPA. Therefore, this work aims to support practitioners in evaluating RPA automation candidates. We design a framework that consists of thirteen criteria grouped into five perspectives which offer different evaluation aspects. These criteria leverage a profound understanding of the process step. We demonstrate and evaluate the framework by applying it to a real-life data set.Comment: This is an accepted manuscript for the "RPA Forum" at the "Int. Conference on Business Process Management (BPM 2020)". The final authenticated version is available online at https://doi.org/10.1007/978-3-030-58779-6_1

Robotic Process Automation - A Systematic Mapping Study and Classification Framework

Robotic Process Automation (RPA) deals with the automation of rule-based process tasks to increase process efficiency and to reduce process costs. Due to the utmost importance of business process automation in industry, RPA attracts increasing attention in the scientific field as well. This paper presents the state-of-the-art in the RPA field by means of a Systematic Mapping Study (SMS). In this SMS, 63 publications are identified, categorised, and analysed along well-defined research questions. From the SMS findings, additionally, a framework for systematically analysing, assessing, and comparing existing as well as upcoming RPA works is derived. The discovered thematic clusters suggest further investigations in order to develop a more elaborated structural research approach for RPA

Process Selection in RPA Projects – Towards a Quantifiable Method of Decision Making

The digital age requires companies to invest in value-creating rather than routine activities to drive innovation as a future source of competitiveness and business success. Thus, many companies are reluctant to invest in large-scale, costly backend integration projects and seek adaptable solutions to automate their front-office activities. Bridging artificial intelligence and business process management, robotic process automation (RPA) provides the promise of robots as a virtual workforce that performs these tasks in a self-determined manner. Many studies have highlighted potential benefits of RPA. However, little data is available on operationalizing and automating RPA to maximize its benefits. In this paper, we shed light on the automation potential of processes with RPA and operationalize it. Based on process mining techniques, we propose an automatable indicator system as well as present and evaluate decision support for companies that seek to better prioritize their RPA activities and to maximize their return on investment