Fredrik Milani

Business Process Variability Modeling: A Survey

It is common for organizations to maintain multiple variants of a given business process, such as multiple sales processes for different products or multiple bookkeeping processes for different countries. Conventional business process modeling languages do not explicitly support the representation of such families of process variants. This gap triggered significant research efforts over the past decade, leading to an array of approaches to business process variability modeling. In general, each of these approaches extends a conventional process modeling language with constructs to capture customizable process models. A customizable process model represents a family of process variants in a way that a model of each variant can be derived by adding or deleting fragments according to customization options or according to a domain model. This survey draws up a systematic inventory of approaches to customizable process modeling and provides a comparative evaluation with the aim of identifying common and differentiating modeling features, providing criteria for selecting among multiple approaches, and identifying gaps in the state of the art. The survey puts into evidence an abundance of customizable process-modeling languages, which contrasts with a relative scarcity of available tool support and empirical comparative evaluations.

Modelling families of business process variants

Business processes usually do not exist as singular entities that can be managed in isolation, but rather as families of business process variants. When modelling such families of variants, analysts are confronted with the choice between modelling each variant separately, or modelling multiple or all variants in a single model. Modelling each variant separately leads to a proliferation of models that share common parts, resulting in redundancies and inconsistencies. Meanwhile, modelling all variants together leads to less but more complex models, thus hindering on comprehensibility. This paper introduces a method for modelling families of process variants that addresses this trade-off. The key tenet of the method is to alternate between steps of decomposition (breaking down processes into sub-processes) and deciding which parts should be modelled together and which ones should be modelled separately. We have applied the method to two case studies: one concerning the consolidation of existing process models, and another dealing with green-field process discovery. In both cases, the method produced fewer models with respect to the baseline and reduced duplicity by up to 50% without significant impact on complexity. Managing the trade-off of modelling process variants together versus separately.Method for alternating decomposition with managing variants together or separately.Applied on consolidation and greenfield process discovery case studies.Results show reduced duplicity by 50% without significant impact on complexity.

Decomposition driven consolidation of process models

Oftentimes business processes exist not as singular entities that can be managed in isolation, but as families of variants that need to be managed together. When it comes to modelling these variants, analysts are faced with the dilemma of whether to model each variant separately or to model multiple or all variants as a single model. The former option leads to a proliferation of models that share common parts, leading to redundancy and possible inconsistency. The latter approach leads to less but more complex models, thus hindering on their comprehensibility. This paper presents a decomposition driven method to capture a family of process variants in a consolidated manner taking into account the above trade-off. We applied our method on a case study in the banking sector. A reduction of 50% of duplication was achieved in this case study.

Identifying and Classifying Variations in Business Processes

Many business processes exist not as singular entities but rather as a plurality of variants that need to be collectively managed. The spectrum of approaches for managing collections of process variants range from capturing all variants in a large consolidated model, down to capturing each variant as a separate model. Most of these approaches are built on the assumption that the variation points and variation drivers are given as input. The question of how process variation is elicited and conceptualized in the first place has received relatively little attention. As a step to filling this gap, this paper puts forward a framework for identifying and classifying variation drivers in business processes. We apply the framework on two collections of process models: one consisting of a collection of process models implicitly clustered along product type and the other one along market type. In both cases, the framework allowed us to identify and to classify additional variation drivers that were not evident from the initial clustering.

Criteria and Heuristics for Business Process Model Decomposition

It is generally agreed that large process models should be decomposed into sub-processes in order to enhance understandability and maintainability. Accordingly, a number of process decomposition criteria and heuristics have been proposed in the literature. This paper presents a review of the field revealing distinct classes of criteria and heuristics. The study raises the question of how different decomposition heuristics affect process model understandability and maintainability. To address this question, an experiment is conducted where two different heuristics, one based on breakpoints and the other on data objects, were used to decompose a flat process model. The results of the experiment show that, although there are minor differences, the heuristics cause very similar results in regard to understandability and maintainability as measured by various process model metrics.

Predictive Process Monitoring Methods: Which One Suits Me Best?

Predictive process monitoring has recently gained traction in academia and is maturing also in companies. However, with the growing body of research, it might be daunting for companies to navigate in this domain in order to find, provided certain data, what can be predicted and what methods to use. The main objective of this paper is developing a value-driven framework for classifying existing work on predictive process monitoring. This objective is achieved by systematically identifying, categorizing, and analyzing existing approaches for predictive process monitoring. The review is then used to develop a value-driven framework that can support organizations to navigate in the predictive process monitoring field and help them to find value and exploit the opportunities enabled by these analysis techniques.

Requirement Elicitation Using Business Process Models

Oftentimes, when eliciting requirements for system development, the input of domain experts is of great importance. Domain experts are not familiar with artifacts predominantly used by system analysts but rather more attuned to models representing the flow of their work such as business process models. However, these models are rarely used as basis for requirement elicitation. As such, there is a communication barrier, which can cause misunderstandings that translate into imprecise requirements. To address this gap, we propose a systematic approach for eliciting functional requirements using business process models as artifacts in discussion with domain experts. We call this method for Requirement Elicitation from Business Process Models (REB). Based on a mapping of requirement components and elements of business process models, the method guides the discussion between system analysts and domain experts around process models for the purpose of eliciting requirements. The method is validated with a case study and the results show that the REB method was successful in eliciting higher number of relevant requirements with less time required.

A Comparative Evaluation of Log-Based Process Performance Analysis Techniques.

Process mining has gained traction over the past decade and an impressive body of research has resulted in the introduction of a variety of process mining approaches measuring process performance. Having this set of techniques available, organizations might find it difficult to identify which approach is best suited considering context, performance indicator, and data availability. In light of this challenge, this paper aims at introducing a framework for categorizing and selecting performance analysis approaches based on existing research. We start from a systematic literature review for identifying the existing works discussing how to measure process performance based on information retrieved from event logs. Then, the proposed framework is built starting from the information retrieved from these studies taking into consideration different aspects of performance analysis.