Context Of Business Process Management Research Articles

Advancing business process management lifecycle implementation through the integration of the design for Six Sigma method

Purpose This research proposes how Design for Six Sigma (DFSS) provides a complementary approach for business process management (BPM) lifecycle implementation in order to address gaps identified in the current literature.Design/methodology/approach The mandatory elements of a method (MEM) framework is used to illustrate DFSS's maturity as a process redesign method. The use of DFSS in a BPM context is described through several action research case examples.Findings This research specifies the procedure model (order of development activities), techniques, results, roles and information/meta model (conceptual data model of results) associated with using DFSS to address BPM-related challenges. The action research case examples provided discuss the details of implementing BPM using DFSS to design, implement and test redesigned processes to ensure they fulfill the needs of process participants.Research limitations/implications While the case examples discussed were performed in only a few settings, which limits the generalizability of their results, they provide evidence regarding the wide range of domains in which the proposed DFSS-BPM approach can be applied and how the tools are used in different contexts.Practical implications This research offers a road map for addressing the challenges practitioners often face with BPM lifecycle implementation.Originality/valueThis research provides the first attempt to integrate DFSS as a complementary method for BPM lifecycle implementation.

Context-Aware Business Process Management

Context awareness is essential for successful business process management (BPM). So far, research has covered relevant BPM context factors and context-aware process design, but little is known about how to assess and select BPM methods in a context-aware manner. As BPM methods are involved in all stages of the BPM lifecycle, it is key to apply appropriate methods to efficiently use organizational resources. Following the design science paradigm, the study at hand addresses this gap by developing and evaluating the Context-Aware BPM Method Assessment and Selection (CAMAS) Method. This method assists method engineers in assessing in which contexts their BPM methods can be applied and method users in selecting appropriate BPM methods for given contexts. The findings of this study call for more context awareness in BPM method design and for a stronger focus on explorative BPM. They also provide insights into the status quo of existing BPM methods.

A Process Mining Application for the Analysis of Hospital-at-Home Admissions.

This article proposes the analysis of the admissions to hospital-at-home service within the framework of process mining. In addition to conventional modeling in standard languages, relying on interviews and continuous improvement, we propose the adoption of an automatic process discovery technique based on data collected by the hospital information system. We focus on the patient admission process, in which staff discriminate cases of interest for the service. Our methodological framework starts with the extraction of process information from the existing dataset. Once obtained meaningful data for an event log analysis, we propose the adoption of a process discovery algorithm by using a specific tool for process mining. In the context of Business Process Management, we suggest a practical application to be explored in order to improve standard modeling, opening the way to perform business process simulation with scenario analysis.

Business process outsourcing to cloud containers: How to find the optimal deployment?

Containers are a new service model that empowers cloud computing by offering horizontally scalable systems while bypassing high-performance challenges of traditional hypervisors. In the business process management context, Containers-as-a-Service can be used to outsource business processes to the cloud and allow an enterprise to bundle its processes and data in a simpler and more performance-oriented manner. To profit from containers, an enterprise must however have a means to identify the optimal resource allocation. Towards this end, we propose a system architecture for optimal containers-based deployment of business processes. The proposed system architecture relies on our extension of ContainerCloudSim simulator to estimate the execution time of business processes deployed according to the CaaS model. In addition, it encloses a business process deployment optimizer. To develop this latter, we examine a linear program and a genetic algorithm to find out the optimal deployment of a business process on cloud containers. We show experimentally the effective performance of containers-based versus VM-based deployment, and linear program versus the First-Fit container strategy and the genetic algorithm.

Business process outsourcing enhanced by fuzzy linguistic consensus model

Business process outsourcing represents a strategic option to obtain the overall improvement of performance in business process management context. It consists in externalizing whole sub-processes (e.g., production, logistics, human resources) of a value chain. Last decade, the concept of value chain moved toward the more flexible concept of value net that implies the assembly of several value chains tailored to specifics, objectives, markets, etc. Thus, the composition of a value chain within a value net environments can be understood as the modeling of a macro business process in which sub-processes can be outsourced. Such composition activity foresees crucial decision-making moments that need to be sustained by a group of decision-makers owning several and heterogeneous competences in order to select the most suitable external providers to which delegate specific sub-processes. This work proposes a framework to enhance business process outsourcing by introducing group decision-making support that relies on a fuzzy linguistic consensus model. In addition, the framework implements algorithms to learn and assign different weights to decision-makers considering the context and time at which they participate in the group decision making. The framework is applied to an Italian footwear company by describing a numerical example.

A model-driven method for enacting the design-time QoS analysis of business processes

Business Process Management (BPM) is a holistic approach for describing, analyzing, executing, managing, and improving large enterprise business processes. A business process can be seen as a flow of tasks that are orchestrated to accomplish well-defined goals such as goods production or services delivery. From an IT perspective, BPM is closely related to a business process automation approach carried out by use of IT standards and technologies, such as service-oriented architectures (SOAs) and Web Services. This paper specifically focuses on fully automated business processes that are defined and executed as orchestrations of software services. In a BPM context, the ability to predict at design time the business process behavior assumes a strategic relevance, both to early assess whether or not the business goals are achieved and to gain a competitive advantage. A business process is typically specified by use of Business Process Modeling Notation (BPMN), the standard language for the high-level description of business processes. Unfortunately, BPMN does not support the characterization of the business process in terms of nonfunctional or QoS properties, such as performance and reliability. To overcome such a limitation, this paper introduces Performability-enabled BPMN (PyBPMN), a lightweight BPMN extension for the specification of performance and reliability properties. PyBPMN enables the design time prediction of the business processes behavior, in terms of performance and reliability properties. Such prediction activity requires the use of models that are to be first built and then evaluated. In this respect, this work introduces a model-driven method that exploits PyBPMN to predict, at design time, the performance and the reliability of a business process, either to select the process configuration that provides the best behavior or to check if a given configuration satisfies the overall requirements. The proposed model-driven method that enacts the automated analysis of a business process behavior embraces the complete business process development cycle, from the specification phase down to the implementation phase. The paper also describes how the proposed model-driven method is implemented. The several model transformations at the core of the method have been implemented by use of QVT, and the standard language for specifying model transformations provided by OMG's MDA. The availability of such automated model transformations allows business analysts to predict the process behavior with no extra effort and without being required to own specific skills of performance or reliability theory, as shown by use of an example application.

Analyzing Employee Agreement on Maturity Assessment Tools for Organizations

The objective of this paper is to investigate the impact that individual raters have on maturity assessments of organizations in the particular context of business process management (BPM). The hypotheses tested relate to the extent of the impact of individuals on maturity score variances and with the enforcing effect of organizational size on the disagreement among employees within organizations. Eight multilevel random‐effects analyses for eight separate maturity dimensions clarify the intra‐class correlation (agreement) within organizations. The analyses are based on a data set with a strictly hierarchical two‐level data structure of employees (1755) nested within organizations (61). Results show that variance within organizations is significantly larger than zero and is even more important than variance between organizations. We conclude that a large individual background effect exists when rating an organization's business process maturity. In addition, we find that the larger the organizations are, the more disagreement within organizations is visible. Copyright © 2012 John Wiley & Sons, Ltd.

Empirical research in business process management – analysis of an emerging field of research

PurposeThe paper aims at providing a survey of the development of empirical research in business process management (BPM). It seeks to study trends in empirical BPM research and applied methodologies by means of a developed framework in order to identify the status quo and to assess the probable future development of the research field.Design/methodology/approachIn order to analyse the development of the research field a systematic literature review of empirical journal articles in the BPM context is conducted. The retrieved literature is analyzed by means of scientometric methods and a developed reference framework.FindingsThe steadily growing number of published articles in empirical BPM research shows an increase in interest in the research field. Research interests, applied methodologies, the underlying research paradigm and the level of maturity of empirical BPM research differ depending on regional aspects. BPM gains importance in the industry as well as in the public administration context.Research limitations/implicationsThe findings are based on a sample of 355 articles and not on an exhaustive amount of available empirical research contributions. Nevertheless, significant analyses can be conducted. Future research could apply the developed reference framework for further literature reviews in order to be able to compare the findings and to measure progress.Originality/valueThe presented literature review gives an overview of trends in empirical BPM research. The developed and strictly applied reference framework supports a systematic analysis of contributions and can thus draw a significant picture of the state‐of‐the‐art of the research field. To the best knowledge of the authors no such survey has currently been undertaken.