Opportunities and Challenges for Process Mining in Organisations -Results of a Delphi Study (original) (raw)

Opportunities and Challenges for Process Mining in Organizations: Results of a Delphi Study

Business & Information Systems Engineering

Process mining is an active research domain and has been applied to understand and improve business processes. While significant research has been conducted on the development and improvement of algorithms, evidence on the application of process mining in organizations has been far more limited. In particular, there is limited understanding of the opportunities and challenges of using process mining in organizations. Such an understanding has the potential to guide research by highlighting barriers for process mining adoption and, thus, can contribute to successful process mining initiatives in practice. In this respect, the paper provides a holistic view of opportunities and challenges for process mining in organizations identified in a Delphi study with 40 international experts from academia and industry. Besides proposing a set of 30 opportunities and 32 challenges, the paper conveys insights into the comparative relevance of individual items, as well as differences in the percei...

The Importance of Process Mining in Enhancing Process Performance in Organisation

2015

academic conference concerned with research, education and application in The development of business intelligence allows organisations to manage and enhance the decision-making process by providing methods and tools for analysing data. Process mining in organisation is needed to develop connection between data mining as business intelligence method and Business Process Management. The main purpose of process mining is to discover process model based on existing event log data that can be used for different objectives. This research will examine the essential concepts of process mining and its tools in order to analyse data and deliver proposal to enhance process performance in organisation. This research conducts focus group discussion as a qualitative method to discuss the advantages of process mining and to compare the process mining tools. The analysis highlights that the process mining has important role in organisation in determining: basic performance metrics; process model; and organisational model, and analysing social network and performance characteristics. Interestingly, both of process mining tools ProM and DISCO have different features and capabilities to discover the business process in organisation. This allows organisation to assess the data of business process transaction and provide some improvement approaches based on the result in process mining. By using the process-mining algorithm and tools, organisation can manage how to improve their process of business more effectively and efficiently in order to achieve their objectives.

Methodological proposal for process mining projects

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.

The need for a process mining evaluation framework in research and practice: Position paper

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.

PM 2 : a Process Mining Project Methodology

Process mining aims to transform event data recorded in information systems into knowledge of an organisation's business processes. The results of process mining analysis can be used to improve process performance or compliance to rules and regulations. However, applying process mining in practice is not trivial. In this paper we introduce PM 2 , a methodology to guide the execution of process mining projects. We successfully applied PM 2 during a case study within IBM, a multinational technology corporation, where we identified potential process improvements for one of their purchasing processes.

Challenges and Use Cases of Process Discovery in Process Mining

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.

Business Process Modelling vs. Process Mining: A Comparative Study of Methods and Software Tools

19. hrvatska konferencija o kvaliteti i 10. znanstveni skup Hrvatskog društva za kvalitetu: Book of Proceedings, 2019

In the last three decades, the importance of business process improvement and related techniques in organizational design has been widely stressed. Business process management and process analysis, with their significant impact on process and organizational excellence, continue to remain intriguing topics. Recently, a shift away from traditional business process management methods, such as value-chain approach modelling and process simulation, has occurred, alongside the prominence of process mining that has emerged due to data mining and big data opportunities. Hence, this article investigates characteristics of a more traditional technique of process modelling in well-known methodologies, characteristic of a process-mining technique, and offers a comparison of the two approaches. Additionally, a presentation of one business process modelling tool and one process-mining tool is delivered. In the end, the suitability of techniques presented is discussed, also in relation to the organizational quality and excellence.

Business process mining: from theory to practice

… Process Management Journal, 2012

Purpose -This paper presents a comparison of a number of business process mining tools currently available in the UK market. An outline of the practice of business process mining is given along with an analysis of the main techniques developed by academia and commercial entities. This paper also acts as a primer for the acceptance and further use of process mining in industry suggesting future directions for this practice.

Development of the Process Mining Discipline

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.

Process Mining – New era in process management - A Survey

Process Mining is extraction of process models from event logs recorded by information systems. It acts as a bridge between data mining and business process. It is possible to use process mining to monitor deviations (e.g., comparing the observed events with predefined models or business rules in the context of SOX). . This technology has become available only recently, but it can be applied to any type of operational processes (organizations and systems). Example applications include: analyzing treatment processes in hospitals, improving customer service processes in a multinational, understanding the browsing behavior of customers using a booking site, analyzing failures of a baggage handling system, and improving the user interface of an X-ray machine The paper does a survey of important process mining aspects- its perspective, types, tools available for it, advantages and the fundamental algorithm used in Process Mining- Alpha Miner Algorithm.