A study of the application of computational intelligence and machine learning techniques in business process mining

International audienceProcess mining is a emerging research area that combines data mining and machine learning, on one hand, and business process modeling and analysis, on the other hand. This work aims to assess the application of computational intelligence and machine learning techniques in process mining context. The main focus of the study was to identify why the computational intelligence and machine learning techniques are not being widely used in process mining field and identify the main reasons for this phenomenon. The stage of experiments in this study was carried out based on an unstructured process related to a distance learning supported by a Learning Management System (LMS). ABSTRACT BACKGROUN

Process mining for healthcare: Characteristics and challenges

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.This work is partially supported by ANID FONDECYT 1220202, Dirección de Investigación de la Vicerrectoría de Investigación de la Pontificia Universidad Católica de Chile - PUENTE [Grant No. 026/ 2021]; and Agencia Nacional de Investigación y Desarrollo [Grant Nos. ANID-PFCHA/Doctorado Nacional/2019–21190116, ANID-PFCHA/ Doctorado Nacional/2020–21201411]. With regard to the co-author Hilda Klasky, this manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-accessplan).Peer ReviewedArticle signat per 55 autors/es: Jorge Munoz-Gama (a)* , Niels Martin (b,c)* , Carlos Fernandez-Llatas (d,g)* , Owen A. Johnson (e)* , Marcos Sepúlveda (a)* , Emmanuel Helm (f)* , Victor Galvez-Yanjari (a)* , Eric Rojas (a) , Antonio Martinez-Millana (d) , Davide Aloini (k) , Ilaria Angela Amantea (l,q,r) , Robert Andrews (ab), Michael Arias (z) , Iris Beerepoot (o) , Elisabetta Benevento (k) , Andrea Burattin (ai), Daniel Capurro (j) , Josep Carmona (s) , Marco Comuzzi (w), Benjamin Dalmas (aj,ak), Rene de la Fuente (a) , Chiara Di Francescomarino (h) , Claudio Di Ciccio (i) , Roberto Gatta (ad,ae), Chiara Ghidini (h) , Fernanda Gonzalez-Lopez (a) , Gema Ibanez-Sanchez (d) , Hilda B. Klasky (p) , Angelina Prima Kurniati (al), Xixi Lu (o) , Felix Mannhardt (m), Ronny Mans (af), Mar Marcos (v) , Renata Medeiros de Carvalho (m), Marco Pegoraro (x) , Simon K. Poon (ag), Luise Pufahl (u) , Hajo A. Reijers (m,o) , Simon Remy (y) , Stefanie Rinderle-Ma (ah), Lucia Sacchi (t) , Fernando Seoane (g,am,an), Minseok Song (aa), Alessandro Stefanini (k) , Emilio Sulis (l) , Arthur H. M. ter Hofstede (ab), Pieter J. Toussaint (ac), Vicente Traver (d) , Zoe Valero-Ramon (d) , Inge van de Weerd (o) , Wil M.P. van der Aalst (x) , Rob Vanwersch (m), Mathias Weske (y) , Moe Thandar Wynn (ab), Francesca Zerbato (n) // (a) Pontificia Universidad Catolica de Chile, Chile; (b) Hasselt University, Belgium; (c) Research Foundation Flanders (FWO), Belgium; (d) Universitat Politècnica de València, Spain; (e) University of Leeds, United Kingdom; (f) University of Applied Sciences Upper Austria, Austria; (g) Karolinska Institutet, Sweden; (h) Fondazione Bruno Kessler, Italy; (i) Sapienza University of Rome, Italy; (j) University of Melbourne, Australia; (k) University of Pisa, Italy; (l) University of Turin, Italy; (m) Eindhoven University of Technology, The Netherlands; (n) University of St. Gallen, Switzerland; (o) Utrecht University, The Netherlands; (p) Oak Ridge National Laboratory, United States; (q) University of Bologna, Italy; (r) University of Luxembourg, Luxembourg; (s) Universitat Politècnica de Catalunya, Spain; (t) University of Pavia, Italy; (u) Technische Universitaet Berlin, Germany; (v) Universitat Jaume I, Spain; (w) Ulsan National Institute of Science and Technology (UNIST), Republic of Korea; (x) RWTH Aachen University, Germany; (y) University of Potsdam, Germany; (z) Universidad de Costa Rica, Costa Rica; (aa) Pohang University of Science and Technology, Republic of Korea; (ab) Queensland University of Technology, Australia; (ac) Norwegian University of Science and Technology, Norway; (ad) Universita degli Studi di Brescia, Italy; (ae) Lausanne University Hospital (CHUV), Switzerland; (af) Philips Research, the Netherlands; (ag) The University of Sydney, Australia; (ah) Technical University of Munich, Germany; (ai) Technical University of Denmark, Denmark; (aj) Mines Saint-Etienne, France; (ak) Université Clermont Auvergne, France; (al) Telkom University, Indonesia; (am) Karolinska University Hospital, Sweden; (an) University of Borås, SwedenPostprint (published version

Process mining and digital transformation of organizations: A literature review

It is unquestionable that digital transformation impacts directly on organizations and the way they do business. With the emergence of Business Intelligence techniques, such as Process Mining, it was generated an expectation that the use of these techniques could allow organizations to obtain competitive advantages and optimize their results. The soaring availability and volume of event logs volume suggests that Process Mining will increasingly assume an important part in the organizations developments. The purpose of this article is to identify and analyze, through a literature review, the role of Process Mining on the digital transformation process of organizations

Process mining for healthcare: Characteristics and challenges

[EN] Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.This work is partially supported by ANID FONDECYT 1220202, Direccion de Investigacion de la Vicerrectoria de Investigacion de la Pontificia Universidad Catolica de Chile-PUENTE [Grant No. 026/2021] ; and Agencia Nacional de Investigacion y Desarrollo [Grant Nos. ANID-PFCHA/Doctorado Nacional/2019-21190116, ANID-PFCHA/Doctorado Nacional/2020-21201411] . With regard to the co-author Hilda Klasky, this manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE) . The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan)Munoz Gama, J.; Martin, N.; Fernández Llatas, C.; Johnson, OA.; Sepúlveda, M.; Helm, E.; Galvez-Yanjari, V.... (2022). Process mining for healthcare: Characteristics and challenges. Journal of Biomedical Informatics. 127:1-15. https://doi.org/10.1016/j.jbi.2022.10399411512

Performance Analysis and Improvement of Bank of Industry and Mine Working Capital Facility Processes Based on Process Mining Approach

Banks have complex, long processes and activities with many points of control and approval, especially for facility processes. The survival of these institutions, providing quality and fast services and customer satisfaction requires improvement and analysis of results after the implementation of these processes. The main purpose of this study is to analyze the performance and improve the working capital facility processes. For this purpose, a method based on process mining and fuzzy algorithm is used. The method includes six steps: log extraction of the Bank of Industry & Mine facility system, log inspection, control flow analysis, performance analysis based on time indicator, making suggestions and reviewing the results, and finally improving the processes using simulation.The results of the present study include the discovery of a real and improved process model, the detection of bottlenecks and max repetition activities, the reduction of the mean throughput time by 23% and the number of activities by 21%, and finally the efficiency of process mining

Mineração de processos em concessionarias da indústria automotiva: estudo de caso, análise e negócio

Objetivo: Como objetivo, busca-se aplicar uma metodologia de mineração de processos nas informações de pós-venda de concessionárias de automóveis, de uma montadora brasileira, analisando como negócio.   Referencial teórico: A utilização da mineração de processos na indústria automotiva proporciona diversos benefícios gerenciais, que são refletidos na melhora da qualidade do produto. Quando se trata de concessionária de automóveis e seus serviços de pós-venda, satisfação e/ou reclamações de cliente, evidencia uma escassez na literatura que reforçam o uso da técnica de mineração de processos. Dessa forma, para cobrir tal lacuna, há clara a necessidade desta pesquisa.   Metodologia/abordagem: Primeiramente, técnicas de estruturação de dados foram desenvolvidas para a adequação dos dados brutos cedidos pela empresa estudada, para um padrão adequado à mineração de processos. Busca-se assim, obter informações, tendências e padrões em dados de clientes através do sistema de pós-venda destas concessionárias, durante um período e, apresentar o resultado destas análises.   Resultados: Como resultados, destaca-se a criação de gráficos e tabelas que mostram a evolução de cada problema, relatados pelos clientes, em quilometragens de revisão e qual foi encontrado pelas concessionárias, baseando-se em palavras-chave citadas pelos clientes no momento da reclamação.   Pesquisa, implicações práticas e sociais: As implicações desta pesquisa culminam na melhora da análise e utilização dos dados pela empresa estudada, exaltam a importância da mineração de processos, para encontrar informações escondidas na grande quantidade de dados, gerada por uma empresa multinacional do setor automovível.   Originalidade: Nesta pesquisa foi detectada para o setor de concessionárias de automóveis há uma escasses de pesquisas voltadas a mineração de processos. Logo, esta pesquisa, teve como objetivo propor esta metodologia

Influence of process mining in robotic process automation

Abstract. Companies are in an on-going competition against each other of the customers’ favour. Customers demand cheaper products and services with better quality. To be able to meet the customer requirements and stand up against their competitors, companies must be able to do more with less money and time invested. For this reason, process automation has been implemented widely in various companies. The latest breakthrough within process automation has been the implementation of Robotic Process Automation (RPA) which automatizes simple and rule-based back office tasks. While the existing literature reports high return on investment for RPA, challenges in identifying the best use cases to receive these returns remain. Some studies have suggested to combine RPA with Process Mining (PM) technology to help with this challenge. PM itself is still an emerging technology much like RPA. While both of these technologies have been studied somewhat, their combination remains undiscovered by prior literature. Therefore, the ambition of this research is to generate new understanding on how Process Mining can influence the efficiency of RPA’s utilisation. This ambition is achieved with a qualitative interview study that answers the following research questions: • RQ1: What are the benefits of RPA? • RQ2: What are the pre-requisites for successful RPA? • RQ3: How can process mining influence the efficiency of RPA? To answer the research questions, a literature review consisting of Business Process Management, RPA, and PM was conducted. The literature review forms an understanding of the key elements of RPA and PM while also identifying why businesses need these technologies. Interviews were conducted with professionals who had experience on RPA and PM to validate and expand the literature findings and to identify how RPA and PM could be combined. Combining the findings of literature review and interviews, a framework for RPA lifecycle augmented with PM was created to illustrate how RPA and PM can be beneficially combined. The results of this research reveal that PM is able to improve the efficiency of RPA by enabling data-based process understanding for organizations which helps to identify the best opportunities for automation, spot pre-automation process improvement needs, and to support decision making. In addition, PM is able to monitor and analyse the performance of the processes and the RPA robots to spot any deviances and to report the realized benefits of applying RPA to the process. This research contributes to the existing literature by providing new knowledge about the combination of RPA and PM. These results can be generally applied within organizations using RPA without restrictions to their industries.Tiivistelmä. Yritykset kilpailevat jatkuvasti toisiaan vastaan asiakkaidensa suosiosta. Asiakkaat vaativat halvempia ja laadukkaampia tuotteita ja palveluita. Vastatakseen asiakasvaatimuksiin ja säilyttääkseen kilpailukykynsä, yritysten täytyy tehdä enemmän asioita pienemmillä rahallisilla ja ajallisilla investoinneilla. Vastauksena tähän tarpeeseen, monet yritykset ovat automatisoineet prosessejaan. Viimeisin läpimurto prosessiautomaatiossa on ohjelmistorobotiikka, joka automatisoi yksinkertaisia ja sääntöpohjaisia tukitoimintojen työtehtäviä. Tämänhetkinen kirjallisuus raportoi suurta tuottoa investoinneille ohjelmistorobotiikkaan, mutta haasteita tuoton todelliseen toteutumiseen aiheutuu parhaiden käyttökohteiden tunnistamisen vaikeudesta. Muutamat tutkimukset ovat väläyttäneet prosessilouhinnan yhdistämistä ohjelmistorobotiikkaan, jotta parhaiden käyttökohteiden tunnistaminen helpottuisi. Prosessilouhinta, kuten ohjelmistorobotiikka, on myös kasvava teknologia. Sekä ohjelmistorobotiikkaa että prosessilouhintaa on tutkittu aikaisemmin, mutta näiden teknologioiden yhdistämistä ei ole juurikaan tutkittu aiemmin. Tästä syystä tämän tutkimuksen päämääränä on luoda uutta ymmärrystä siitä, miten prosessilouhinta vaikuttaa ohjelmistorobotiikan hyödyntämiseen. Tähän päämäärään päästään laadullisella haastattelututkimuksella, joka vastaa seuraaviin tutkimuskysymyksiin: • TK1: Mitkä ovat ohjelmistorobotiikan hyödyt? • TK2: Mitä ennakkovaatimuksia onnistuneelle ohjelmistorobotille on? • TK3: Kuinka prosessilouhinta voi vaikuttaa ohjelmistorobotiikan tehokkuuteen? Tutkimuskysymyksiin vastataan tekemällä aluksi kirjallisuuskatsaus, joka koostuu liiketoimintaprosessienhallinnasta, ohjelmistorobotiikasta ja prosessilouhinnasta. Kirjallisuuskatsaus luo ymmärrystä ohjelmistorobotiikan ja prosessilouhinnan perusteista sekä tunnistaa, miksi liiketoiminta tarvitsee näitä teknologioita. Kirjallisuuskatsauksen löydösten validointia ja laajentamista varten toteutettiin haastatteluja alan ammattilaisten kanssa, joilla oli työkokemusta ohjelmistorobotiikasta ja prosessilouhinnasta. Haastattelujen pohjalta pyrittiin myös tunnistamaan, miten prosessilouhintaa ja ohjelmistorobotiikkaa voitaisiin yhdistää. Kirjallisuuskatsauksen ja haastattelujen tulosten yhdistämisen ja analysoinnin pohjalta luotiin viitekehys ohjelmistorobotiikan elinkaarelle, johon liitettiin prosessilouhinnan käyttökohteet. Tämän viitekehyksen tarkoituksena on havainnollistaa, miten ohjelmistorobotiikkaa ja prosessilouhintaa voidaan hyödyntää yhdessä. Tutkimustulokset osoittavat, että prosessilouhinnan mahdollistama dataan perustuva prosessiymmärrys auttaa organisaatioita tunnistamaan parhaat käyttökohteet automaatiolle, havaitsemaan prosessinkehitys tarpeita sekä tukemaan päätöksentekoa, mitkä puolestaan parantavat ohjelmistorobotiikan tehokkuutta. Näiden lisäksi prosessilouhinnan avulla pystytään monitoroimaan ja analysoimaan prosessien ja robottien suorituskykyä, jolloin voidaan havaita mahdolliset poikkeamat sekä raportoimaan saavutetut hyödyt ohjelmistorobotiikan käytöstä prosessissa. Tämä tutkimus tukee aiempaa tutkimustietoa luomalla uutta tietoa ohjelmistorobotiikan ja prosessilouhinnan yhdistämisestä. Tutkimuksen tuloksia voidaan soveltaa yleisesti ohjelmistorobotiikkaa käyttävissä organisaatioissa toimialasta riippumattomasti

Mineração de processos em concessionárias da indústria automotiva : um estudo de caso em uma montadora brasileira

Orientador: Prof. Dr. Márcio Fontana CatapanCoorientador: Prof. Dr. Fenando DeschampsDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia de Manufatura. Defesa : Curitiba, 01/09/2021Inclui referências: p. 63-66Área de concentração: Simulação e Integração de ProcessosResumo: A utilização da mineração de processos na indústria automotiva proporciona diversos benefícios gerenciais, que são refletidos na melhora da qualidade do produto. Quando se trata de áreas de conhecimento relacionadas a concessionária de automóveis e seus serviços de pós-venda, satisfação e/ou reclamações de cliente, evidencia uma escassez de artigos e pesquisas que reforçam o uso da técnica de mineração de processos, mesmo quando se relaciona com disciplinas correlatas como, por exemplo, "machine learning" ou "data mining". Dessa forma, para cobrir tal lacuna, este estudo tem como objetivo, aplicar uma metodologia de mineração de processos nas informações de pós-venda de concessionárias de automóveis de uma montadora brasileira. Para testar a metodologia proposta, um caso de aplicação foi desenvolvido em uma indústria automobilística, onde primeiramente, técnicas de estruturação de dados foram desenvolvidas para a adequação dos dados brutos cedidos pela empresa, para um padrão adequado à mineração de processos. Busca-se assim, obter informações, tendências e padrões em dados de clientes através do sistema de pós-venda, destas concessionárias, durante o período compreendido entre 2019 e 2020 e, apresentar o resultado destas análises. Técnicas de estruturação de dados são desenvolvidas para a adequação dos dados brutos cedidos pela empresa para um padrão para a mineração de processos. Como resultados, destaca-se a criação de gráficos e tabelas que mostram a evolução de cada problema, relatado pelos clientes, em quilometragens de revisão e qual foi encontrado pelas concessionárias, baseando-se em palavras-chave citadas pelos clientes no momento da reclamação. Ou seja, a metodologia testada proporciona uma grande proximidade dos tomadores de decisão com os analistas de dados da empresa, permitindo que os objetivos sejam melhor definidos e, ao final do projeto, seja possível confrontar o resultado obtido com o esperado. Com isso, os resultados encontrados culminaram na melhora da análise e utilização dos dados pela empresa, exaltam a importância da mineração de processos para encontrar informações escondidas na grande quantidade de dados gerada por uma empresa multinacional do setor automovível.Abstract: The use of process mining in the automotive industry provides several managerial benefits, which are reflected in the improvement of product quality. When it comes to areas of knowledge related to the car dealership and its after-sales services, customer satisfaction and/or complaints, it shows a lack of articles and research that reinforce the use of the process mining technique, even when related to disciplines such as "machine learning" or "data mining". Thus, to cover this gap, this study aims to apply a process mining methodology to after-sales information from car dealerships of a Brazilian automaker. To test the proposed methodology, an application case was developed in an automobile industry, where firstly, data structuring techniques were developed to adapt the raw data provided by the company to an adequate standard for process mining. The aim is thus to obtain information, trends, and patterns in customer data through the after-sales system of these dealerships, during the period between 2019 and 2020, and present the result of these analyses. Data structuring techniques are developed to adapt the raw data provided by the company to a standard for process mining. As a result, the creation of graphs and tables that show the evolution of each problem, reported by customers, based on vehicle mileage. In other words, the tested methodology provides a great proximity of stakeholders with the company's data analysts, allowing the objectives to be better defined and, at the end of the project, it is possible to compare the obtained result with the expected one. Thus, the results found culminated in the improvement of the analysis and use of data by the company, highlighting the importance of process mining to find hidden information in the large amount of data generated by a multinational company in the automotive sector