Predictive Monitoring of Business Processes

Modern information systems that support complex business processes generally maintain significant amounts of process execution data, particularly records of events corresponding to the execution of activities (event logs). In this paper, we present an approach to analyze such event logs in order to predictively monitor business goals during business process execution. At any point during an execution of a process, the user can define business goals in the form of linear temporal logic rules. When an activity is being executed, the framework identifies input data values that are more (or less) likely to lead to the achievement of each business goal. Unlike reactive compliance monitoring approaches that detect violations only after they have occurred, our predictive monitoring approach provides early advice so that users can steer ongoing process executions towards the achievement of business goals. In other words, violations are predicted (and potentially prevented) rather than merely detected. The approach has been implemented in the ProM process mining toolset and validated on a real-life log pertaining to the treatment of cancer patients in a large hospital

Aiding compliance governance in service-based business processes

Assessing whether a company's business practices conform to laws and regulations and follow standards and SLAs, i.e., compliance management, is a complex and costly task. Few software tools aiding compliance management exist; yet, they typically do not address the needs of who is actually in charge of assessing and understanding compliance. We advocate the use of a compliance governance dashboard and suitable root cause analysis techniques that are specifically tailored to the needs of compliance experts and auditors. The design and implementation of these instruments are challenging for at least three reasons: (1) it is fundamental to identify the right level of abstraction for the information to be shown; (2) it is not trivial to visualize different analysis perspectives; and (3) it is difficult to manage and analyze the large amount of involved concepts, instruments, and data. This chapter shows how to address these issues, which concepts and models underlie the problem, and, eventually, how IT can effectively support compliance analysis in Service-Oriented Architectures (SOAs). © 2012, IGI Global

SOA-enabled compliance management: Instrumenting, assessing, and analyzing service-based business processes

Facilitating compliance management, that is, assisting a company's management in conforming to laws, regulations, standards, contracts, and policies, is a hot but non-trivial task. The service-oriented architecture (SOA) has evolved traditional, manual business practices into modern, service-based IT practices that ease part of the problem: the systematic definition and execution of business processes. This, in turn, facilitates the online monitoring of system behaviors and the enforcement of allowed behaviors-all ingredients that can be used to assist compliance management on the fly during process execution. In this paper, instead of focusing on monitoring and runtime enforcement of rules or constraints, we strive for an alternative approach to compliance management in SOAs that aims at assessing and improving compliance. We propose two ingredients: (i) a model and tool to design compliant service-based processes and to instrument them in order to generate evidence of how they are executed and (ii) a reporting and analysis suite to create awareness of a company's compliance state and to enable understanding why and where compliance violations have occurred. Together, these ingredients result in an approach that is close to how the real stakeholders-compliance experts and auditors-actually assess the state of compliance in practice and that is less intrusive than enforcing compliance. © 2013 Springer-Verlag London

Analyze Customer Complaint in Healthcare Using Root Cause Analysis Technique

This project is to presents an approach for applying Root Cause Analysis (RCA) in improving the healthcare service for the purpose of investigating of need for corrective action, and tracking and trending the services problems. For trending the organization will be able to determine how often a particular error occurs or how often a particular unit or department of the hospital involved. Root Cause Analysis should be performed as soon as possible after the error or variance occurs and should be involved by all parties, to avoid speculation that will dilute the facts. Otherwise the important details may be missed. The development and utility of the proposed methodology presented in this research is iiiustrated using both a hypothetical example and a real world application

Exploring Features of a Full-Coverage Integrated Solution for Business Process Compliance

The last few years have seen the introduction of several techniques for automatically tackling some aspects of compliance checking between business processes and business rules. Some of them are quite robust and mature and are provided with software support that partially or fully implement them. However, as far as we know there is not yet a tool that provides for the complete management of business process compliance in the whole lifecycle of business processes. The goal of this paper is to move towards an integrated business process compliance management system (BPCMS) on the basis of current literature and existing support. For this purpose, we present a description of some compliance-related features such a system should have in order to provide full coverage of the business process lifecycle, from compliance aware business process design to the audit process. Hints about what existing approaches can fit in each feature and challenges for future work are also provided

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

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

Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud

With the advent of cloud computing, organizations are nowadays able to react rapidly to changing demands for computational resources. Not only individual applications can be hosted on virtual cloud infrastructures, but also complete business processes. This allows the realization of so-called elastic processes, i.e., processes which are carried out using elastic cloud resources. Despite the manifold benefits of elastic processes, there is still a lack of solutions supporting them. In this paper, we identify the state of the art of elastic Business Process Management with a focus on infrastructural challenges. We conceptualize an architecture for an elastic Business Process Management System and discuss existing work on scheduling, resource allocation, monitoring, decentralized coordination, and state management for elastic processes. Furthermore, we present two representative elastic Business Process Management Systems which are intended to counter these challenges. Based on our findings, we identify open issues and outline possible research directions for the realization of elastic processes and elastic Business Process Management.Comment: Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and P. Hoenisch (2015). Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud. Future Generation Computer Systems, Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.00

Modernising operational risk management in financial institutions via data-driven causal factors analysis: A pre-registered study

In an effort to contribute a quantitative, objective and real-time tool to proactively and precisely manage the factors underlying and exacerbating operational risks, this pre-registered study executes the empirical methodology approved in the associated pre-registered report (Cornwell et al., 2023). The application of the Bayesian network-based approach to an Australian insurance company shows that integrating a financial institution's loss and operational data in this way can effectively model the probability of an operational loss event within its interconnected operational risk environment. Further insights and efficiencies are gained by modelling multiple operational loss events together, rather than in isolation. A novel two-module framework derived specifically for causal factors analysis from the resulting operational risk model helps to highlight the relative importance of causal factors, their collective effects and critical thresholds requiring proactivity. These insights derived from the framework are expected to be strategically valuable in helping an organisation design intentional and targeted controls for and monitoring of operational risks. Given existing knowledge of the improvements quantitative risk management tools make to risk management effectiveness and subsequently firm value, the enhanced risk management and the operational efficiencies this tool seeks to afford should ultimately contribute to driving financial performance and firm value