Showing papers on "Process modeling published in 2010"

Seven process modeling guidelines (7PMG)

Abstract: Business process modeling is heavily applied in practice, but important quality issues have not been addressed thoroughly by research A notorious problem is the low level of modeling competence that many casual modelers in process documentation projects have Existing approaches towards model quality might be of benefit, but they suffer from at least one of the following problems On the one hand, frameworks like SEQUAL and the Guidelines of Modeling are too abstract to be applicable for novices and non-experts in practice On the other hand, there are collections of pragmatic hints that lack a sound research foundation In this paper, we analyze existing research on relationships between model structure on the one hand and error probability and understanding on the other hand As a synthesis we propose a set of seven process modeling guidelines (7PMG) Each of these guidelines builds on strong empirical insights, yet they are formulated to be intuitive to practitioners Furthermore, we analyze how the guidelines are prioritized by industry experts In this regard, the seven guidelines have the potential to serve as an important tool of knowledge transfer from academia into modeling practice

Business process modeling languages: Sorting through the alphabet soup

Abstract: Requirements capture is arguably the most important step in software engineering, and yet the most difficult and the least formalized one [Phalp and Shepperd 2000] Enterprises build information systems to support their business processes Software engineering research has typically focused on the development process, starting with user requirements—if that—with business modeling often confused with software system modeling [Isoda 2001] Researchers and practitioners in management information systems have long recognized that understanding the business processes that an information system must support is key to eliciting the needs of its users (see eg, Eriksson and Penker 2000]), but lacked the tools to model such business processes or to relate such models to software requirements Researchers and practitioners in business administration have long been interested in modeling the processes of organizations for the purposes of understanding, analyzing, and improving such processes [Hammer and Champy 1993], but their models were often too coarse to be of use to software engineers The advent of ecommerce and workflow management systems, among other things, has led to a convergence of interests and tools, within the broad IT community, for modeling and enabling business processes In this article we present an overview of business process modeling languages We first propose a categorization of the various languages and then describe representative languages from each family

Opportunities and constraints: the current struggle with BPMN

Abstract: Purpose – The business process modeling notation (BPMN) is an increasingly important standard for process modeling and has enjoyed high levels of attention in business practice. In this paper, experiences are shared from several research projects investigating the uptake and user acceptance of BPMN by analysts world‐wide. This personal viewpoint aims to offer a number of implications for business process management (BPM) practice and seeks to stimulate and guide further research and other developments in this area.Design/methodology/approach – This paper offers a personal viewpoint based on the experiences and findings gathered from survey research and interviews on the use of BPMN. While details on research execution are mostly omitted, references are provided to guide the interested reader to the methodology used in the original studies.Findings – First, statistics are provided on the usage of BPMN by process modelers world‐wide. Amongst others, it is shown that the high interest in BPMN has created a m...

Capturing variability in business process models: the Provop approach

Abstract: Usually, for a particular business process different variants exist. Each of them constitutes an adjustment of a reference process model to specific requirements building the process context. Contemporary process management tools do not adequately support the modeling of such process variants. Either the variants have to be specified as separate process models or they are expressed in terms of conditional branches within the same process model. Both methods often lead to redundancies making model adaptations a time-consuming and error-prone task. In this article, we discuss selected concepts of the Provop approach for modeling and managing process variants. A particular process variant can be configured at a high level of abstraction by applying a set of well-defined change operations to a reference process model. In particular, this article discusses advanced concepts for the design and modeling of such a reference process model as well as for the adjustments required to configure the different process variants. Altogether, Provop provides a flexible and powerful solution for process variant management. Copyright © 2010 John Wiley & Sons, Ltd.

Capturing variability in business process models: the Provop approach

Abstract: Usually, for a particular business process different variants exist. Each of them constitutes an adjustment of a reference process model to specific requirements building the process context. Contemporary process management tools do not adequately support the modeling of such process variants. Either the variants have to be specified as separate process models or they are expressed in terms of conditional branches within the same process model. Both methods often lead to redundancies making model adaptations a time-consuming and error-prone task. In this article, we discuss selected concepts of the Provop approach for modeling and managing process variants. A particular process variant can be configured at a high level of abstraction by applying a set of well-defined change operations to a reference process model. In particular, this article discusses advanced concepts for the design and modeling of such a reference process model as well as for the adjustments required to configure the different process variants. Altogether, Provop provides a flexible and powerful solution for process variant management. Copyright © 2010 John Wiley & Sons, Ltd.

Structuring acyclic process models

Abstract: This paper addresses the problem of transforming a process model with an arbitrary topology into an equivalent well-structured process model. While this problem has received significant attention, there is still no full characterization of the class of unstructured process models that can be transformed into well-structured ones, nor an automated method to structure any process model that belongs to this class. This paper fills this gap in the context of acyclic process models. The paper defines a necessary and sufficient condition for an unstructured process model to have an equivalent structured model under fully concurrent bisimulation, as well as a complete structuring method.

On enabling data-aware compliance checking of business process models

Abstract: In the light of an increasing demand on business process compliance, the verification of process models against compliance rules has become essential in enterprise computing. To be broadly applicable compliance checking has to support data-aware compliance rules as well as to consider data conditions within a process model. Independently of the actual technique applied to accomplish compliance checking, data-awareness means that in addition to the control flow dimension, the data dimension has to be explored during compliance checking. However, naive exploration of the data dimension can lead to state explosion. We address this issue by introducing an abstraction approach in this paper. We show how state explosion can be avoided by conducting compliance checking for an abstract process model and abstract compliance rules. Our abstraction approach can serve as preprocessing step to the actual compliance checking and provides the basis for more efficient application of existing compliance checking algorithms.

The performance impact of business process standardization: An empirical evaluation of the recruitment process

Abstract: Purpose – The purpose of this paper is to show if business process standardization (BPS) has an impact on business process performance and should be considered as both a valid business process management (BPM) measure and a regular driver of process success.Design/methodology/approach – An empirical analysis based on data from 156 firms is used to evaluate the hypothesis that process standardization positively impacts business process time, cost, and quality.Findings – First, the paper proposes a model and empirical operationalization to analyze the impact of process standardization on process performance. Second, empirical analysis shows that BPS has a decisive impact on process performance (R2=61.9 percent). Precisely, there is a significant impact on process time, cost, and most notably on quality. The results indicate that the impact is strongest in services firms and varies subject to a firm's strategy type.Practical implications – The results suggest that BPS should regularly be considered a prime a...

Supply chain process modelling for humanitarian organizations

Abstract: Purpose – The purpose of this paper is to present a supply chain process modelling method adapted to the requirements of humanitarian organisations.Design/methodology/approach – Empirical research was conducted to identify the state of practice of supply chain management (SCM) in humanitarian organisations. An established procedure was selected, in order to develop a reference task model which forms the basis of the process modelling method. A literature review, expert interviews and other primary sources were used to identify the SCM activities that are required for the reference task model.Findings – An empirical survey revealed that process modelling and optimisation are in their infancy at humanitarian organisations. A reference task model identifying over 100 SCM tasks is constructed. The applicability and feasibility of the developed process modelling method is exemplified by means of a case study.Research limitations/implications – The reference task model provides the basis for further research on...

Preserving correctness during business process model configuration

Abstract: A configurable process model captures a family of related process models in a single artifact. Such models are intended to be configured to fit the requirements of specific organizations or projects, leading to individualized process models that are subsequently used for domain analysis or solution design. This article proposes a formal foundation for individualizing configurable process models incrementally, while preserving correctness, both with respect to syntax and behavioral semantics. Specifically, assuming the configurable process model is behaviorally sound, the individualized process models are guaranteed to be sound. The theory is first developed in the context of Petri nets and then extended to a process modeling notation widely used in practice, namely Event-driven Process Chains.

Exception Handling Patterns for Process Modeling

Abstract: Process modeling allows for analysis and improvement of processes that coordinate multiple people and tools working together to carry out a task. Process modeling typically focuses on the normative process, that is, how the collaboration transpires when everything goes as desired. Unfortunately, real-world processes rarely proceed that smoothly. A more complete analysis of a process requires that the process model also include details about what to do when exceptional situations arise. We have found that, in many cases, there are abstract patterns that capture the relationship between exception handling tasks and the normative process. Just as object-oriented design patterns facilitate the development, documentation, and maintenance of object-oriented programs, we believe that process patterns can facilitate the development, documentation, and maintenance of process models. In this paper, we focus on the exception handling patterns that we have observed over many years of process modeling. We describe these patterns using three process modeling notations: UML 2.0 Activity Diagrams, BPMN, and Little-JIL. We present both the abstract structure of the pattern as well as examples of the pattern in use. We also provide some preliminary statistical survey data to support the claim that these patterns are found commonly in actual use and discuss the relative merits of the three notations with respect to their ability to represent these patterns.

Continued use of process modeling grammars: the impact of individual difference factors

Abstract: Process modeling grammars are used by analysts to describe information systems domains in terms of the business operations an organization is conducting. While prior research has examined the factors that lead to continued usage behavior, little knowledge has been established as to what extent characteristics of the users of process modeling grammars inform usage behavior. In this study, a theoretical model is advanced that incorporates determinants of continued usage behavior as well as key antecedent individual difference factors of the grammar users, such as modeling experience, modeling background and perceived grammar familiarity. Findings from a global survey of 529 grammar users support the hypothesized relationships of the model. The study offers three central contributions. First, it provides a validated theoretical model of post-adoptive modeling grammar usage intentions. Second, it discusses the effects of individual difference factors of grammar users in the context of modeling grammar usage. Third, it provides implications for research and practice.

Configuration and Management of Process Variants

Abstract: This chapter deals with advanced concepts for the configuration and management of business process variants. Typically, for a particular business process, different variants exist. Each of them constitutes an adjustment of a master process (e.g., a reference process) to specific requirements building the process context. Contemporary Business Process Management tools do not adequately support the modeling and management of such process variants. Either the variants have to be specified in separate process models or they are expressed in terms of conditional branches within the same process model. Both methods can result in high model redundancies, which make model adaptations a time-consuming and error-prone task. In this chapter, we discuss advanced concepts of our Provop approach, which provides a flexible and powerful solution for managing business process variants along their lifecycle. Such variant support will foster more systematic process configuration as well as process maintenance.

Towards Robust Conformance Checking

Abstract: The growing complexity of processes in many organizations stimulates the adoption of business process management (BPM) techniques. Process models typically lie at the basis of these techniques and generally, the assumption is made that the operational business processes as they are taking place in practice conform to these models. However, recent experience has shown that this often isn’t the case. Therefore, the problem of checking to what extent the operational process conforms to the process model is increasingly important.

Process mining : conformance and extension

Abstract: 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.

Mining process model variants: Challenges, Techniques, Examples

Abstract: During the last years a new generation of process-aware information systems has emerged, which enables process model configurations at buildtime as well as process instance changes during runtime. Respective model adaptations result in large collections of process model variants that are derived from same process model, but slightly differ in structure. Generally, such model variants are expensive to maintain and configure. Starting with a collection of related process model variants, the major goal of this thesis is to discover a reference process model out of which these variants can be easily configured; i.e., a reference process model with minimal average change distance to the variants. To achieve this goal we present two advanced algorithms which are applicable in different scenarios. Our clustering algorithm does not presume any knowledge about the original reference process model out of which the process model variants were configured. By only looking at the process model variants, this algorithm can quickly discover a reference process model in polynomial time, which allows us to scale up when solving real-world problems. Our heuristic algorithm, in turn, can take the original reference model into account as well. In particular, the user can control to what degree the discovered model differs from the original one. This way we can avoid spaghetti-like process models and additionally control how many changes we want to perform on the original reference model. We systematically evaluate and compare the two algorithms based on simulations that comprise more than 7000 process models. Simulation results indicate good performance and make the differences between the two algorithms explicit. For example, the simulation results indicate that our clustering algorithm runs significantly faster than our heuristic algorithm. However, our heuristic algorithm can identify important changes at the beginning of the search and can discover better results than the clustering algorithm. We successfully applied the two algorithms to cases from the automotive and the healthcare domain. During these case studies, the practical relevance and benefit of our work has become evident once more.

Investigating the Process of Process Modeling with Cheetah Experimental Platform { Tool Paper {

Abstract: When assessing the usability of BPM technologies enter- prises have to rely on vendor promises or qualitative data rather than on empirical or experimental research. To address this need Cheetah Ex- perimental Platform (CEP) has been developed fostering experimental research on business process modeling. CEP provides components that are frequently used in controlled experiments and allows their assem- bly to experimental workows. CEP supports experimental execution by mitigating risks endangering data validity through better user guid- ance. Additionally, CEP provides richer evaluation techniques compared to paper based experiments fostering the experiment's data analysis.

A Structural Measurement System for Engineering Design Processes

Abstract: To achieve a systematic and holistic analysis and improvement of an engineering design process, the thesis on “A Structural Measurement System for Engineering Design Processes” proposes a measurement system that makes use of complexity metrics to embody various patterns of the interplay of a process’ entities (e.g. tasks, documents, organizational units, etc.). These metrics are used to draw inferences about the process’ behavior (e.g. timeliness, need for communication, risks, etc.). The 52 metrics are supported by a meta-model for process modeling and a framework to select the metrics in accordance with the goals of the process analysis. The metrics embody the foundations of network theory and the management of structural complexity to generate a practice-oriented application.

Conceptual modeling in simulation projects by mean adapted idef: an application in a brazilian tech company

Abstract: Several process modeling techniques have been used in simulation projects. However, most of these techniques provide little specific support to the programming. The main cause of this is the fact that these techniques were not developed with the same logic used in simulation models. From this issue, this paper presents an industrial application of a new conceptual modeling technique, named IDEF-SIM (Integrated Definition Methods -- Simulation) currently under development by the authors. This adapted IDEF uses logic elements present in techniques such as IDEF0 and IDEF3, but in a way that is similar to the process interpretation logic usually used in simulation projects. This way, it can be noticed an increase in the conceptual model's utility, which might facilitate the simulation model programming, verification and validation and the scenarios creation. Additionally, the paper presents the benefits of using IDEF-SIM to create the conceptual model of a Brazilian tech company manufacturing cell.

The ontological deficiencies of process modeling in practice

Abstract: Business process modeling is widely regarded as one of the most popular forms of conceptual modeling. However, little is known about the capabilities and deficiencies of process modeling grammars and how existing deficiencies impact actual process modeling practice. This paper is a first contribution towards a theory-driven, exploratory empirical investigation of the ontological deficiencies of process modeling with the industry standard Business Process Modeling Notation (BPMN). We perform an analysis of BPMN using a theory of ontological expressiveness. Through a series of semi-structured interviews with BPMN adopters we explore empirically the actual use of this grammar. Nine ontological deficiencies related to the practice of modeling with BPMN are identified, for example, the capture of business rules and the specification of process decompositions. We also uncover five contextual factors that impact on the use of process modeling grammars, such as tool support and modeling conventions. We discuss implications for research and practice, highlighting the need for consideration of representational issues and contextual factors in decisions relating to BPMN adoption in organizations.

Investigating the Process of Process Modeling with Cheetah Experimental Platform

Abstract: When assessing the usability of BPM technologies enter- prises have to rely on vendor promises or qualitative data rather than on empirical or experimental research. To address this need Cheetah Ex- perimental Platform (CEP) has been developed fostering experimental research on business process modeling. CEP provides components that are frequently used in controlled experiments and allows their assem- bly to experimental workows. CEP supports experimental execution by mitigating risks endangering data validity through better user guid- ance. Additionally, CEP provides richer evaluation techniques compared to paper based experiments fostering the experiment's data analysis.

Value‐oriented process modeling: integrating financial perspectives into business process re‐design

Abstract: Purpose – Financial information about costs and return on investments are of key importance to strategic decision-making but also in the context of process improvement or business engineering. In this paper we propose a value-oriented approach to business process modeling based on key concepts and metrics from operations and financial management, to aid decision making in process re-design projects on the basis of process models. Design/methodology/approach – We suggest a theoretically founded extension to current process modeling approaches, and delineate a framework as well as methodical support to incorporate financial information into process re-design. We use two case studies to evaluate the suggested approach. Findings – Based on two case studies, we show that the value-oriented process modeling approach facilitates and improves managerial decision-making in the context of process re-design. Research limitations / implications – We present design work and two case studies. More research is needed to more thoroughly evaluate the presented approach in a variety of real-life process modeling settings. Practical implications – We show how our approach enables decision makers to make investment decisions in process re-design projects, and also how other decisions, for instance in the context of enterprise architecture design, can be facilitated. Originality/value – This study reports on an attempt to integrate financial considerations into the act of process modeling, in order to provide more comprehensive decision making support in process re-design projects.

Dynamic Business Process Formation for Instant Virtual Enterprises

Abstract: • A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.

Business Process Simulation Revisited

Abstract: Computer simulation attempts to “mimic” real-life or hypothetical behavior on a computer to see how processes or systems can be improved and to predict their performance under different circumstances. Simulation has been successfully applied in many disciplines and is considered to be a relevant and highly applicable tool in Business Process Management (BPM). Unfortunately, in reality the use of simulation is limited. Few organizations actively use simulation. Even organizations that purchase simulation software (stand-alone or embedded in some BPM suite), typically fail to use it continuously over an extended period. This keynote paper highlights some of the problems causing the limited adoption of simulation. For example, simulation models tend to oversimplify the modeling of people working part-time on a process. Also simulation studies typically focus on the steady-state behavior of business processes while managers are more interested in short-term results (a “fast forward button” into the future) for operational decision making. This paper will point out innovative simulation approaches leveraging on recent breakthroughs in process mining.

A conceptually rich model of business process compliance

Abstract: In this paper we extend the preliminary work developed elsewhere and investigate how to characterise many aspects of the compliance problem in business process modeling. We first define a formal and conceptually rich language able to represent, and reason about, chains of reparational obligations of various types. Second, we devise a mechanism for normalising a system of legal norms. Third, we specify a suitable language for business process modeling able to automate and optimise business procedures and to embed normative constraints. Fourth, we develop an algorithm for compliance checking and discuss some computational issues regarding the possibility of checking compliance runtime or of enforcing it at design time.