Process Mining: The first successful Peruvian case

2016

A database containing the most comprehensive list of process mining applications (#1 worldwide) in different industries, including banking, insurance, telco, manufacturing, retail, healthcare, government. The research was presented at the CIS IEEE Task Force on Process Mining annual meeting, during 14th International Conference on BPM in Rio de Janeiro. • Scope: cases studies from 100+ companies, and 6 industries, between 2005-2016, • Target audience: executives, decision makers, consultants and researchers

International Journal of Advanced Trends in Computer Science and Engineering, 2020

To date, the amount of data collected are significant and the growth is exponential in various fields over the last decades. Most companies typically address the issue of storage system capacities and therefore such situation creates the issue handling "big data." However, most companies sometimes may find it a challenge to obtain useful knowledge from a large amount of information. As such, numerous organizations are expected to tackle the data-related difficulties. In contrast, there is a necessity to improve and support business processes in progressively changing conditions. Generally, it is possible to analyses the available data and propose improvements because some tools and methods exist to facilitate such cause. Process mining may offer effective approaches to complement the business process. Process mining allows organizations to take advantage of the information within the systems, but can also be used to check process conformance, predict execution challenges, and identify bottlenecks. For instance, healthcare has rapidly changed over decades of research and development. Through scientific discovery, more diseases can be treated. However, it also affects the feasibility of health institutions to accommodate more complicated health treatment processes and systems effectively. Indeed, many solutions exist to cure a particular disease, since machines are becoming further complex, requiring medical staff to be equipped with necessary training. Also, it significantly increases the cost of health care. This paper presents the insights of process mining, highlighting the possible approaches used to gather and analyses the data using feasible method in process mining including real discovery processes. The paper also discusses the challenges of process mining.

It is exciting to see the spectacular developments in process mining since I started to work on this in the late 1990-ties. Many of the techniques we developed 15-20 years ago have become standard functionality in today's process mining tools. Therefore, it is good to view current and future developments in this historical context. This chapter starts with a brief summary of the history of process mining showing how ideas from academia got adopted in commercial tools. This provides the basis to talk about the expanding scope of process mining, both in terms of applications and in terms of functionalities supported. Despite the rapid development of the process mining discipline, there are still several challenges. Some of these challenges are new, but there are also several challenges that have been around for a while and still need to be addressed urgently. This requires the concerted action of process mining users, technology providers, and scientists. Adoption of traditional process mining techniques Process mining started in the late nineties when I had a sabbatical and was working for one year at the University of Colorado in Boulder (USA). Before, I was mostly focusing on concurrency theory, discrete event simulation, and workflow management. We had built our own simulation engines (e.g., ExSpect) and workflow management systems. Although our research was well-received and influential, I was disappointed by the average quality of process models and the impact process models had on reality. In both simulation studies and workflow implementations, the real processes often turned out to be very different from what was modeled by the people involved. As a result, workflow and simulation projects often failed. Therefore, I decided to focus on the analysis of processes through event data [1]. Around the turn of the century, we developed the first process discovery algorithms [2]. The Alpha algorithm was the first algorithm able to learn concurrent process models from event data and still provide formal guarantees. However, at the time, little event data were available and the assumptions made by the first algorithms were unrealistic. People working on data mining and machine learning were (and perhaps still are) not interested in process analysis. Therefore, it was not easy to convince other researchers to work on this. Nevertheless, for me, it was crystal clear that process mining would become a crucial ingredient of any process management or process improvement initiative. In the period that followed, I stopped working on the traditional business process management topics and fully focused on process mining. It is interesting to see that concepts such as conformance checking, organizational process mining, decision mining, token animation, time prediction, etc. were already developed and implemented 15 years ago [2]. These capabilities are still considered to be cutting-edge and not supported by most of the commercial process mining tools.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008

Although there has been much progress in developing process mining algorithms in recent years, no effort has been put in developing a common means of assessing the quality of the models discovered by these algorithms. In this paper, we motivate the need for such an evaluation mechanism, and outline elements of an evaluation framework that is intended to enable (a) process mining researchers to compare the performance of their algorithms, and (b) end users to evaluate the validity of their process mining results.

IEEE Internet Computing, 2012

Process mining techniques help organizations discover and analyze business processes based on raw event data. The recently released "Process Mining Manifesto" presents guiding principles and challenges for process mining. Here, the authors summarize the manifesto's main points and argue that analysts should take into account the context in which events occur when analyzing processes.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Process mining is a strategy related to data science and process management which is mainly used to analyze the processes based on event logs. The goal of process mining is to transform event data into details and actions with the help of graphics. Process mining techniques use event data to show what people, machines and organizations are actually doing [1]. Process mining will be able to provide new information about the process that can be used to identify the existing technique in the process and to address their operational and compliance issues.

International Journal of Business Process Integration and Management, 2017

Process mining is a discipline that allows organisations to discover, analyse and improve their business processes. Although the techniques, algorithms and software packages intended for the application of this discipline have been evolving positively, challenges have appeared as it is applied to process improvement projects. This is so because most of the methodological approaches developed for its application provide general guidelines, but they do not define the specific steps and tactics for the challenges that a practitioner must go through when facing a process redesign project through process mining. The current development contributes to improving the applicability of this discipline by designing a methodology that allows professionals and organizations to conduct process mining projects. The methodology is illustrated by applying to three case studies, after which it was subjected to evaluation by the personnel of the companies that participated in its application. The limitations of the designed methodology and future research perspectives are finally discussed.

Lecture Notes in Computer Science, 2017

Process mining is an emerging area that synergically combines model-based and data-oriented analysis techniques to obtain useful insights on how business processes are executed within an organization. This tutorial aims at providing an introduction to the key analysis techniques in process mining that allow decision makers to discover process models from data, compare expected and actual behaviors, and enrich models with key information about the actual process executions. In addition, the tutorial will present concrete tools and will provide practical skills for applying process mining in a variety of application domains, including the one of software development.

Process mining enables organizations to uncover their actual processes, provide insights, diagnose problems, and automatically trigger corrective actions. Process mining is an emerging scientific discipline positioned at the intersection between process science and data science. The combination of process modeling and analysis with the event data present in today's information systems provides new means to tackle compliance and performance problems. This chapter provides an overview of the field of process mining introducing the different types of process mining (e.g., process discovery and conformance checking) and the basic ingredients, i.e., process models and event data. To prepare for later chapters, event logs are introduced in detail (including pointers to standards for event data such as XES and OCEL). Moreover, a brief overview of process mining applications and software is given.

Technische Universiteit Eindhoven, Tech. Rep. WP, 2006

Abstract. The basic idea of process mining is to extract knowledge from event logs recorded by an information system. Until recently, the information in these event logs was rarely used to analyze the underlying processes. Process mining aims at improving this by providing techniques and tools for discovering process, organizational, social, and performance information from event logs. Fuelled by the omnipresence of event logs in transactional information systems (cf. WFM, ERP, CRM, SCM, and B2B systems), process mining has ...