Generic Services Model for the Automatic Improvement of Business Processes

Introduction: Organizations require more productivity and efficiency in their business processes every day. Currently, various tools provide support to optimize time and resources according to the complexity of the activities of the business processes. However, by automating processes, few companies can define a successful workflow, thus failing to anticipate the difficulties in a production environment. Consequently, it is impossible to provide an early solution to problems, which implies cost overruns, loss of time, and in some cases, affectation of the organization's human talent. Objective: This article presents a generic service model for the automatic improvement of business processes that allows identifying bottlenecks, reprocesses, failures, and delays when analyzing the event logs of a business process. It also summarizes the implementation of the bottleneck management service to support decision-making in a simulated process, applying regression models to predict the performance of manual process activities based on delays and queue lengths. By predicting performance and making resource allocation suggestions, the level of process improvement was determined. Method: The research was conducted following the Iterative Research Pattern proposed by Pratt. First, the main problems in process management were identified, then a review of the state of the art was carried out to find out the proposed solutions to these problems. A solution model independent of the process management software used was proposed, and finally, two evaluations were carried out, one at a conceptual level with the focus group technique and the other based on the implementation of one of the proposed services and data collected from an experiment in a business process simulator. Results: The conceptual evaluation of the services proposed in the model was conducted by a group of experts, based on the design and content guidelines of the BPMN modeling nomenclature standard, giving a rating of 4.8 out of 5.0 for each service. Experimentation with the business process simulator and the recommendations provided by the implemented service (bottleneck management) made it possible to evaluate the reduction in the processing time of the instances of a process in relation to the added resources. Conclusions: The proposed model is composed of three main services, bottleneck management, resource management, and input management. The first service helps to establish the corrective measures so that the process flows and the instances of this do not get stuck in specific tasks or activities, which helps to improve the response time and the quality of the service. Resource management seeks to optimize the execution time of manual activities and input management seeks to ensure that an instance of the process has the data and documents required to be processed from start to finish, avoiding reprocessing and improving the quality of the data received and processed.Introducción: Las organizaciones requieren día a día más productividad y eficiencia en sus procesos de negocio. En la actualidad se cuenta con diversas herramientas que brindan soporte para optimizar tiempos y recursos de acuerdo con la complejidad de las actividades de los procesos de negocio. Sin embargo, al automatizar procesos, pocas empresas logran definir un flujo de trabajo exitoso, por lo que no pueden prever las dificultades que surgen en un entorno de producción. En consecuencia, no es posible dar una solución anticipada a los problemas, lo que implica sobrecostos, pérdida de tiempo y en algunos casos afectación al talento humano de la organización. Objetivo: En este artículo se presenta un modelo de servicios genérico para la mejora automática de procesos de negocio que permite identificar cuellos de botella, reprocesos, fallas y retrasos al analizar los logs de eventos de un proceso de negocio. También resume la implementación del servicio de gestión de cuellos de botella para soportar la toma de decisiones en un proceso simulado, aplicando modelos de regresión para predecir el rendimiento de las actividades manuales del proceso con base en los retrasos y la longitud de las colas. Mediante la predicción del rendimiento y la elaboración de sugerencias de asignación de recursos, se determinó el nivel de mejora del proceso. Metodología: La investigación se realizó siguiendo el Patrón de Investigación Iterativa propuesto por Pratt. Primero se identificaron los principales problemas en la gestión de procesos, luego se realizó una revisión del estado del arte para conocer las propuestas de solución a estos problemas, después se propuso un modelo de solución independiente del software de gestión de procesos que se use y finalmente se realizaron dos evaluaciones, una a nivel conceptual con la técnica de grupo focal y la otra basada en la implementación de uno de los servicios propuestos y datos recolectados de un experimento en un simulador de procesos de negocio. Resultados: La evaluación conceptual de los servicios propuestos en el modelo se realizó por parte de un grupo de expertos, con base en los lineamientos de diseño y contenido del estándar de la nomenclatura de modelado BPMN, otorgando una calificación de 4,8 sobre 5,0 para cada servicio. La experimentación con el simulador de procesos de negocio y las recomendaciones entregadas por el servicio implementado (gestión de cuellos de botella) permitió evaluar la reducción en tiempo del procesamiento de las instancias de un proceso en relación con los recursos adicionados. Conclusiones: El modelo propuesto este compuesto de tres servicios principales, la gestión de cuellos de botella, la gestión de recursos y la gestión de entradas. El primer servicio ayuda a establecer las medidas correctivas para que el proceso fluya y las instancias de este no se encolen en tareas o actividades específicas, lo que ayuda a mejorar el tiempo de respuesta y la calidad del servicio. La gestión de recursos busca optimizar el tiempo de ejecución de las actividades manuales y la gestión de entradas busca asegurar que una instancia del proceso cuente con los datos y documentos requeridos para ser procesado de inicio a fin, evitando reprocesos y mejorando la calidad de los datos que se reciben y procesan

Mining complex structured data: Enhanced methods and applications

Conventional approaches to analysing complex business data typically rely on process models, which are difficult to construct and use. This thesis addresses this issue by converting semi-structured event logs to a simpler flat representation without any loss of information, which then enables direct applications of classical data mining methods. The thesis also proposes an effective and scalable classification method which can identify distinct characteristics of a business process for further improvements

Process Mining Workshops

This open access book constitutes revised selected papers from the International Workshops held at the Third International Conference on Process Mining, ICPM 2021, which took place in Eindhoven, The Netherlands, during October 31–November 4, 2021. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 28 papers included in this volume were carefully reviewed and selected from 65 submissions. They stem from the following workshops: 2nd International Workshop on Event Data and Behavioral Analytics (EDBA) 2nd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) 2nd International Workshop on Streaming Analytics for Process Mining (SA4PM) 6th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) 4th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) 2nd International Workshop on Trust, Privacy, and Security in Process Analytics (TPSA) One survey paper on the results of the XES 2.0 Workshop is included

A Literature Review on Predictive Monitoring of Business Processes

Oleme läbi vaadanud mitmesuguseid ennetava jälgimise meetodeid äriprotsessides. Prognoositavate seirete eesmärk on aidata ettevõtetel oma eesmärke saavutada, aidata neil valida õige ärimudel, prognoosida tulemusi ja aega ning muuta äriprotsessid riskantsemaks. Antud väitekirjaga oleme hoolikalt kogunud ja üksikasjalikult läbi vaadanud selle väitekirja teemal oleva kirjanduse. Kirjandusuuringu tulemustest ja tähelepanekutest lähtuvalt oleme hoolikalt kavandanud ennetava jälgimisraamistiku. Raamistik on juhendiks ettevõtetele ja teadlastele, teadustöötajatele, kes uurivad selles valdkonnas ja ettevõtetele, kes soovivad neid tehnikaid oma valdkonnas rakendada.The goal of predictive monitoring is to help the business achieve their goals, help them take the right business path, predict outcomes, estimate delivery time, and make business processes risk aware. In this thesis, we have carefully collected and reviewed in detail all literature which falls in this process mining category. The objective of the thesis is to design a Predictive Monitoring Framework and classify the different predictive monitoring techniques. The framework acts as a guide for researchers and businesses. Researchers who are investigating in this field and businesses who want to apply these techniques in their respective field

Process Mining Workshops

This open access book constitutes revised selected papers from the International Workshops held at the Third International Conference on Process Mining, ICPM 2021, which took place in Eindhoven, The Netherlands, during October 31–November 4, 2021. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 28 papers included in this volume were carefully reviewed and selected from 65 submissions. They stem from the following workshops: 2nd International Workshop on Event Data and Behavioral Analytics (EDBA) 2nd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) 2nd International Workshop on Streaming Analytics for Process Mining (SA4PM) 6th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) 4th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) 2nd International Workshop on Trust, Privacy, and Security in Process Analytics (TPSA) One survey paper on the results of the XES 2.0 Workshop is included

Evaluation of patient transport service in hospitals using process mining methods: Patients\u27 perspective

Designing healthcare facilities and their processes is a complex task which influences the quality and efficiency of healthcare services. The ongoing demand for healthcare services and cost burdens necessitate the application of analytical methods to enhance the overall service efficiency in hospitals. However, the variability in healthcare processes makes it highly complicated to accomplish this aim. This study addresses the complexity in the patient transport service process at a German hospital, and proposes a method based on process mining to obtain a holistic approach to recognise bottlenecks and main reasons for delays and resulting high costs associated with idle resources. To this aim, the event log data from the patient transport software system is collected and processed to discover the sequences and the timeline of the activities for the different cases of the transport process. The comparison between the actual and planned processes from the data set of the year 2020 shows that, for example, around 36% of the cases were 10 or more minutes delayed. To find delay issues in the process flow and their root causes the data traces of certain routes are intensively assessed. Additionally, the compliance with the predefined Key Performance Indicators concerning travel time and delay thresholds for individual cases was investigated. The efficiency of assignment of the transport requests to the transportation staff are also evaluated which gives useful understanding regarding staffing potential improvements. The research shows that process mining is an efficient method to provide comprehensive knowledge through process models that serve as Interactive Process Indicators and to extract significant transport pathways. It also suggests a more efficient patient transport concept and provides the decision makers with useful managerial insights to come up with efficient patient-centred analysis of transportation services through data from supporting information systems

A systematic approach for performance assessment using process mining. An industrial experience report

Software performance engineering is a mature field that offers methods to assess system performance. Process mining is a promising research field applied to gain insight on system processes. The interplay of these two fields opens promising applications in the industry. In this work, we report our experience applying a methodology, based on process mining techniques, for the performance assessment of a commercial data-intensive software application. The methodology has successfully assessed the scalability of future versions of this system. Moreover, it has identified bottlenecks components and replication needs for fulfilling business rules. The system, an integrated port operations management system, has been developed by Prodevelop, a medium-sized software enterprise with high expertise in geospatial technologies. The performance assessment has been carried out by a team composed by practitioners and researchers. Finally, the paper offers a deep discussion on the lessons learned during the experience, that will be useful for practitioners to adopt the methodology and for researcher to find new routes

Process Mining Handbook

This is an open access book. This book comprises all the single courses given as part of the First Summer School on Process Mining, PMSS 2022, which was held in Aachen, Germany, during July 4-8, 2022. This volume contains 17 chapters organized into the following topical sections: Introduction; process discovery; conformance checking; data preprocessing; process enhancement and monitoring; assorted process mining topics; industrial perspective and applications; and closing