An overview of process model quality literature - The Comprehensive Process Model Quality Framework

The rising interest in the construction and the quality of (business) process models resulted in an abundancy of emerged research studies and different findings about process model quality. The lack of overview and the lack of consensus hinder the development of the research field. The research objective is to collect, analyse, structure, and integrate the existing knowledge in a comprehensive framework that strives to find a balance between completeness and relevance without hindering the overview. The Systematic Literature Review methodology was applied to collect the relevant studies. Because several studies exist that each partially addresses this research objective, the review was performed at a tertiary level. Based on a critical analysis of the collected papers, a comprehensive, but structured overview of the state of the art in the field was composed. The existing academic knowledge about process model quality was carefully integrated and structured into the Comprehensive Process Model Quality Framework (CPMQF). The framework summarizes 39 quality dimensions, 21 quality metrics, 28 quality (sub)drivers, 44 (sub)driver metrics, 64 realization initiatives and 15 concrete process model purposes related to 4 types of organizational benefits, as well as the relations between all of these. This overview is thus considered to form a valuable instrument for both researchers and practitioners that are concerned about process model quality. The framework is the first to address the concept of process model quality in such a comprehensive way

Literature review of QM and SCM: a perspective of integration

Purpose – To explore the practices of supply chain management and quality management, in order to study the integration of both management fields by means of a structural model. Design/methodology/approach – An overview of the main concepts of supply chain management and quality management were reviewed from the literature, and some practices have been identified in order to understand how these areas are related to each other, and the benefits that this integration can bring to companies’ performance. Findings –The use of integrated approaches to quality management and supply chain management becomes necessary to accomplish some objectives as produce value and optimize sustainability. Due to similar characteristics of these two management areas as: the adoption of holistic approaches, the promotion of continuous improvement and innovation; customer satisfaction; leadership; strategic planning, among others; they can been seen as complementary, and improved global performance can be achieved from their synergies. Thus, they offer a unique framework to integrate participation and partnership between stakeholders. Research limitations/implications – This paper presents a structural model that is based on a literature review. A comprehensive validation process is required to get further insight on the subject, allowing to understand how companies implement supply chain management and quality management strategies and the way it impacts on the overall organization performance. Originality/value – There are some studies concerning the relationship between supply chain management and quality management, although, as far as we were able to find out based on the literature review carried out, there is a lack of studies that covers downstream and upstream dimensions of the whole supply chain. For that reason, we present a conceptual model proposal where it is possible to see the major areas that affect both quality management and supply chain management. We also present some practices that affects quality management and others that affect supply chain management, that the authors consider being of great importance for the integration of these two areas. With this model we consider that we can embrace the most important issues concerning both area

Process mining : conformance and extension

Today’s business processes are realized by a complex sequence of tasks that are performed throughout an organization, often involving people from different departments and multiple IT systems. For example, an insurance company has a process to handle insurance claims for their clients, and a hospital has processes to diagnose and treat patients. Because there are many activities performed by different people throughout the organization, there is a lack of transparency about how exactly these processes are executed. However, understanding the process reality (the "as is" process) is the first necessary step to save cost, increase quality, or ensure compliance. The field of process mining aims to assist in creating process transparency by automatically analyzing processes based on existing IT data. Most processes are supported by IT systems nowadays. For example, Enterprise Resource Planning (ERP) systems such as SAP log all transaction information, and Customer Relationship Management (CRM) systems are used to keep track of all interactions with customers. Process mining techniques use these low-level log data (so-called event logs) to automatically generate process maps that visualize the process reality from different perspectives. For example, it is possible to automatically create process models that describe the causal dependencies between activities in the process. So far, process mining research has mostly focused on the discovery aspect (i.e., the extraction of models from event logs). This dissertation broadens the field of process mining to include the aspect of conformance and extension. Conformance aims at the detection of deviations from documented procedures by comparing the real process (as recorded in the event log) with an existing model that describes the assumed or intended process. Conformance is relevant for two reasons: 1. Most organizations document their processes in some form. For example, process models are created manually to understand and improve the process, comply with regulations, or for certification purposes. In the presence of existing models, it is often more important to point out the deviations from these existing models than to discover completely new models. Discrepancies emerge because business processes change, or because the models did not accurately reflect the real process in the first place (due to the manual and subjective creation of these models). If the existing models do not correspond to the actual processes, then they have little value. 2. Automatically discovered process models typically do not completely "fit" the event logs from which they were created. These discrepancies are due to noise and/or limitations of the used discovery techniques. Furthermore, in the context of complex and diverse process environments the discovered models often need to be simplified to obtain useful insights. Therefore, it is crucial to be able to check how much a discovered process model actually represents the real process. Conformance techniques can be used to quantify the representativeness of a mined model before drawing further conclusions. They thus constitute an important quality measurement to effectively use process discovery techniques in a practical setting. Once one is confident in the quality of an existing or discovered model, extension aims at the enrichment of these models by the integration of additional characteristics such as time, cost, or resource utilization. By extracting aditional information from an event log and projecting it onto an existing model, bottlenecks can be highlighted and correlations with other process perspectives can be identified. Such an integrated view on the process is needed to understand root causes for potential problems and actually make process improvements. Furthermore, extension techniques can be used to create integrated simulation models from event logs that resemble the real process more closely than manually created simulation models. In Part II of this thesis, we provide a comprehensive framework for the conformance checking of process models. First, we identify the evaluation dimensions fitness, decision/generalization, and structure as the relevant conformance dimensions.We develop several Petri-net based approaches to measure conformance in these dimensions and describe five case studies in which we successfully applied these conformance checking techniques to real and artificial examples. Furthermore, we provide a detailed literature review of related conformance measurement approaches (Chapter 4). Then, we study existing model evaluation approaches from the field of data mining. We develop three data mining-inspired evaluation approaches for discovered process models, one based on Cross Validation (CV), one based on the Minimal Description Length (MDL) principle, and one using methods based on Hidden Markov Models (HMMs). We conclude that process model evaluation faces similar yet different challenges compared to traditional data mining. Additional challenges emerge from the sequential nature of the data and the higher-level process models, which include concurrent dynamic behavior (Chapter 5). Finally, we point out current shortcomings and identify general challenges for conformance checking techniques. These challenges relate to the applicability of the conformance metric, the metric quality, and the bridging of different process modeling languages. We develop a flexible, language-independent conformance checking approach that provides a starting point to effectively address these challenges (Chapter 6). In Part III, we develop a concrete extension approach, provide a general model for process extensions, and apply our approach for the creation of simulation models. First, we develop a Petri-net based decision mining approach that aims at the discovery of decision rules at process choice points based on data attributes in the event log. While we leverage classification techniques from the data mining domain to actually infer the rules, we identify the challenges that relate to the initial formulation of the learning problem from a process perspective. We develop a simple approach to partially overcome these challenges, and we apply it in a case study (Chapter 7). Then, we develop a general model for process extensions to create integrated models including process, data, time, and resource perspective.We develop a concrete representation based on Coloured Petri-nets (CPNs) to implement and deploy this model for simulation purposes (Chapter 8). Finally, we evaluate the quality of automatically discovered simulation models in two case studies and extend our approach to allow for operational decision making by incorporating the current process state as a non-empty starting point in the simulation (Chapter 9). Chapter 10 concludes this thesis with a detailed summary of the contributions and a list of limitations and future challenges. The work presented in this dissertation is supported and accompanied by concrete implementations, which have been integrated in the ProM and ProMimport frameworks. Appendix A provides a comprehensive overview about the functionality of the developed software. The results presented in this dissertation have been presented in more than twenty peer-reviewed scientific publications, including several high-quality journals

Green BPM as a business-oriented discipline : a systematic mapping study and research agenda

Green Business Process Management (BPM) focuses on the ecological impact of business processes. This article provides a systematic mapping study of Green BPM literature to evaluate five attributes of the Green BPM research area: (1) scope, (2) disciplines, (3) accountability, (4) researchers and (5) quality control. The results allow developing a research agenda to enhance Green BPM as an approach for environmentally sustainable organizations. We rely on a dichotomy of knowledge production to present research directives relevant for both academics and practitioners in order to help close a rigor-relevance gap. The involvement of both communities is crucial for Green BPM to advance as an applied, business-oriented discipline

The Credit Rating Industry: Competition and Regulation

This study provides a comprehensive analysis of credit rating economics and draws conclusions on the nature of regulation. It starts with an overview of the credit rating industry and introduces a framework that structures multiple rating agency functions. At the heart of the credit rating business model lies the reputation mechanism, which is analyzed in detail. Despite several frictions in the process that give rise to entry barriers and market power, the quality assuring function of the reputation mechanism is very robust � contrary to the fear of many. After analyzing the reputation mechanism, the study takes a wider look at the industry and identifies the forces behind credit rating supply and demand. The structure of demand is dependent on the relation between the �information value� and the �license value� attached to ratings because they are used in �rating-based regulations�. On the supply side the question is whether the high industry concentration is a �natural� result of market forces or whether it is the result of state interference. Aspects such as economies of scale, switching costs, and market segmentation are discussed. From an industrial organization perspective competition in the credit rating industry is limited. A comprehensive review of potential reasons for regulating the credit rating industry reveals that there are only few compelling arguments despite the large number of different aspects discussed by practitioners and researchers. In general, the reputation mechanism and competition should be strengthened. Specifically, the study discusses five regulatory areas: the use of rating-based regulation, competition, official recognition, civil liability, and implementation methods. The regulatory approaches of the EU under the Capital Requirements Directive of 2005 and the USA under the Credit Rating Agency Reform Act of 2006 are contrasted against an optimal regulatory regime. The study closes with a summary and a tabular literature review

Applied Evaluative Informetrics: Part 1

This manuscript is a preprint version of Part 1 (General Introduction and Synopsis) of the book Applied Evaluative Informetrics, to be published by Springer in the summer of 2017. This book presents an introduction to the field of applied evaluative informetrics, and is written for interested scholars and students from all domains of science and scholarship. It sketches the field's history, recent achievements, and its potential and limits. It explains the notion of multi-dimensional research performance, and discusses the pros and cons of 28 citation-, patent-, reputation- and altmetrics-based indicators. In addition, it presents quantitative research assessment as an evaluation science, and focuses on the role of extra-informetric factors in the development of indicators, and on the policy context of their application. It also discusses the way forward, both for users and for developers of informetric tools.Comment: The posted version is a preprint (author copy) of Part 1 (General Introduction and Synopsis) of a book entitled Applied Evaluative Bibliometrics, to be published by Springer in the summer of 201

Annotated Bibliography: Understanding Ambulatory Care Practices in the Context of Patient Safety and Quality Improvement.

The ambulatory care setting is an increasingly important component of the patient safety conversation. Inpatient safety is the primary focus of the vast majority of safety research and interventions, but the ambulatory setting is actually where most medical care is administered. Recent attention has shifted toward examining ambulatory care in order to implement better health care quality and safety practices. This annotated bibliography was created to analyze and augment the current literature on ambulatory care practices with regard to patient safety and quality improvement. By providing a thorough examination of current practices, potential improvement strategies in ambulatory care health care settings can be suggested. A better understanding of the myriad factors that influence delivery of patient care will catalyze future health care system development and implementation in the ambulatory setting