Eine Analyse der Literatur zur Referenzmodellierung im Geschäftsprozessmanagement unter Berücksichtigung quantitativer Methoden

Im Geschäftsprozessmanagement nimmt die Referenzmodellierung bei der Gestaltung von Geschäftsprozessen eine große Bedeutung ein, da auf bereits existierende Modelle zurückgegriffen werden kann. So kann Zeit für die Entwicklung der Prozesse eingespart und von bereits etabliertem Wissen profitiert werden. Die vorliegende Masterarbeit analysiert die Literatur im Bereich der Referenzmodellierung im Geschäftsprozessmanagement unter Berücksichtigung quantitativer Methoden. Es werden insbesondere die Forschungsrichtungen bzw. Themenbereiche, Entwicklungen und der aktuelle Stand der Literatur in diesem Bereich ermittelt. Zunächst werden deutsch- und englischsprachige Artikel nach bestimmten Kriterien ausgewählt. Anschließend folgt eine quantitativ orientierte Analyse der Literatur. Dabei kommt die Latente Semantische Analyse zum Einsatz, mit deren Hilfe Themenbereiche ermittelt werden und die einzelnen Beiträge den ermittelten Themenbereichen zugeordnet werden können. Darüber hinaus wird die Entwicklung der Anzahl der Artikel in den Themenbereichen im Zeitverlauf betrachtet und auf Unterschiede zwischen der deutsch- und englischsprachigen Literatur eingegangen. In der darauf folgenden qualitativ orientierten Analyse werden die Artikel der einzelnen Themenbereiche inhaltlich analysiert und der aktuelle Stand der Forschung dargestellt. Nicht zuletzt werden die Ergebnisse der qualitativen Analyse in Bezug zu den Ergebnissen der quantitativen Analyse gesetzt

Evaluation of automated business process optimization

Today's highly competitive markets tend to favor enterprises, in which business processes are analyzed and optimized regularly, in order to be able to operate in accordance with their business goals. The variety of business process management (BPM) methods applied for this purpose, since the emergence of the concept of business reengineering in the 1990s, ranges from incremental adjustments to radical restructuring. In combination with contemporary workflow automation technology, modern redesign methods are powerful tools for enhancing business performance, enabling companies to maintain a winning margin. Optimization methods that deliver sustainable results using evolutionary approaches, however, are nowadays becoming increasingly popular - once again, two decades after continuous improvement paradigms had almost completely been abandoned in favor of revolutionary process redesign. This diploma thesis explores one such evolutionary BPM approach employed in the deep Business Optimization Platform (dBOP), a research prototype, which assists analysts with the selection and application of suitable process improvement techniques. The present work demonstrates an evaluation of dBOP with the help of simulated business scenarios based on real case studies, and documents the types of optimization patterns most readily applied through automated process redesign. For this purpose two business processes, one from a car rental enterprise and one from a health insurance company, are modeled and deployed on a process server, and executed using web services and sample data warehouses based on actual statistics. These processes are then analyzed with dBOP, in order to compare its optimization recommendations with those expected from a human analyst's perspective

Rethinking the risk matrix

So far risk has been mostly defined as the expected value of a loss, mathematically PL (being P the probability of an adverse event and L the loss incurred as a consequence of the adverse event). The so called risk matrix follows from such definition. This definition of risk is justified in a long term “managerial” perspective, in which it is conceivable to distribute the effects of an adverse event on a large number of subjects or a large number of recurrences. In other words, this definition is mostly justified on frequentist terms. Moreover, according to this definition, in two extreme situations (high-probability/low-consequence and low-probability/high-consequence), the estimated risk is low. This logic is against the principles of sustainability and continuous improvement, which should impose instead both a continuous search for lower probabilities of adverse events (higher and higher reliability) and a continuous search for lower impact of adverse events (in accordance with the fail-safe principle). In this work a different definition of risk is proposed, which stems from the idea of safeguard: (1Risk)=(1P)(1L). According to this definition, the risk levels can be considered low only when both the probability of the adverse event and the loss are small. Such perspective, in which the calculation of safeguard is privileged to the calculation of risk, would possibly avoid exposing the Society to catastrophic consequences, sometimes due to wrong or oversimplified use of probabilistic models. Therefore, it can be seen as the citizen’s perspective to the definition of risk