A Method to Improve the Early Stages of the Robotic Process Automation Lifecycle

The robotic automation of processes is of much interest to organizations. A common use case is to automate the repetitive manual tasks (or processes) that are currently done by back-office staff through some information system (IS). The lifecycle of any Robotic Process Automation (RPA) project starts with the analysis of the process to automate. This is a very time-consuming phase, which in practical settings often relies on the study of process documentation. Such documentation is typically incomplete or inaccurate, e.g., some documented cases never occur, occurring cases are not documented, or documented cases differ from reality. To deploy robots in a production environment that are designed on such a shaky basis entails a high risk. This paper describes and evaluates a new proposal for the early stages of an RPA project: the analysis of a process and its subsequent design. The idea is to leverage the knowledge of back-office staff, which starts by monitoring them in a non-invasive manner. This is done through a screen-mousekey- logger, i.e., a sequence of images, mouse actions, and key actions are stored along with their timestamps. The log which is obtained in this way is transformed into a UI log through image-analysis techniques (e.g., fingerprinting or OCR) and then transformed into a process model by the use of process discovery algorithms. We evaluated this method for two real-life, industrial cases. The evaluation shows clear and substantial benefits in terms of accuracy and speed. This paper presents the method, along with a number of limitations that need to be addressed such that it can be applied in wider contexts.Ministerio de Economía y Competitividad TIN2016-76956-C3-2-

Redesigning business processes: a methodology based on simulation and process mining techniques

Nowadays, organizations have to adjust their business processes along with the changing environment in order to maintain a competitive advantage. Changing a part of the system to support the business process implies changing the entire system, which leads to complex redesign activities. In this paper, a bottom-up process mining and simulation-based methodology is proposed to be employed in redesign activities. The methodology starts with identifying relevant performance issues, which are used as basis for redesign. A process model is "mined" and simulated as a representation of the existing situation, followed by the simulation of the redesigned process model as prediction of the future scenario. Finally, the performance criteria of the current business process model and the redesigned business process model are compared such that the potential performance gains of the redesign can be predicted. We illustrate the methodology with three case studies from three different domains: gas industry, government institution and agriculture

Efficiently computing alignments:using the extended marking equation

\u3cp\u3eConformance checking is considered to be anything where observed behaviour needs to be related to already modelled behaviour. Fundamental to conformance checking are alignments which provide a precise relation between a sequence of activities observed in an event log and a execution sequence of a model. However, computing alignments is a complex task, both in time and memory, especially when models contain large amounts of parallelism. When computing alignments for Petri nets, (Integer) Linear Programming problems based on the marking equation are typically used to guide the search. Solving such problems is the main driver for the time complexity of alignments. In this paper, we adopt existing work in such a way that (a) the extended marking equation is used rather than the marking equation and (b) the number of linear problems that is solved is kept at a minimum. To do so, we exploit fundamental properties of the Petri nets and we show that we are able to compute optimal alignments for models for which this was previously infeasible. Furthermore, using a large collection of benchmark models, we empirically show that we improve on the state-of-the-art in terms of time and memory complexity.\u3c/p\u3

Heuristic Mining Approaches for High-Utility Local Process Models

International audienc

Mining local process models and their correlations

\u3cp\u3eMining local patterns of process behavior is a vital tool for the analysis of event data that originates from flexible processes, which in general cannot be described by a single process model without overgeneralizing the allowed behavior. Several techniques for mining local patterns have been developed over the years, including Local Process Model (LPM) mining, episode mining, and the mining of frequent subtraces. These pattern mining techniques can be considered to be orthogonal, i.e., they provide different types of insights on the behavior observed in an event log. In this work, we demonstrate that the joint application of LPM mining and other patter mining techniques provides benefits over applying only one of them. First, we show how the output of a subtrace mining approach can be used to mine LPMs more efficiently. Secondly, we show how instances of LPMs can be correlated together to obtain larger LPMs, thus providing a more comprehensive overview of the overall process. We demonstrate both effects on a collection of real-life event logs.\u3c/p\u3