A fuzzy logic-based approach for assessing the quality of business process models

Copyright © 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. Similar to software products, the quality of a Business Process model is vital to the success of all the phases of its lifecycle. Indeed, a high quality BP model paves the way to the successful implementation, execution and performance of the business process. In the literature, the quality of a BP model has been assessed through either the application of formal verification, or most often the evaluation of quality metrics calculated in the static and/or simulated model. Each of these assessment means addresses different quality characteristics and meets particular analysis needs. In this paper, we adopt metrics-based assessment to evaluate the quality of business process models, modeled with Business Process Modeling and Notation (BPMN), in terms of their comprehensibility and modifiability. We propose a fuzzy logic-based approach that uses existing quality metrics for assessing the attainment level of these two quality characteristics. By analyzing the static model, the proposed approach is easy and fast to apply. In addition, it overcomes the threshold determination problem by mining a repository of BPMN models. Furthermore, by relying on fuzzy logic, it resembles human reasoning during the evaluation of the quality of business process models. We illustrate the approach through a case study and its tool support system developed under the eclipse framework. The preliminary experimental evaluation of the proposed system shows encouraging results

Requirements Catalog for Business Process Modeling Recommender Systems

The manual construction of business process models is a time-consuming and error-prone task. To improve the quality of business process models, several modeling support techniques have been suggested spanning from strict auto-completion of a business process model with pre-defined model elements to suggesting closely matching recommendations. While recommendation systems are widely used and auto-completion functions are a standard feature of programming tools, such techniques have not been exploited for business process modeling although implementation strategies have already been suggested. Therefore, this paper collects requirements from different perspectives (literature and empirical studies) of how to effectively and efficiently assist process modelers in their modeling task. The condensation of requirements represents a comprehensive catalog, which constitutes a solid foundation to implement effective and efficient Process Modeling Recommender Systems (PMRSs). We expect that our contribution will fertilize the field of modeling support techniques to make them a common feature of BPM tools

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

On the Differences Between Process Models by Novice and Expert Modellers

Viimase kümnekonna aasta jooksul on äriprotsesside modelleerimine saanud järjest suurema tähelepanu osaliseks, kuna see võimaldab ettevõtetel analüüsida, täiustada ja seirata äriprotsesse. Niisiis võimaldavad kvaliteetsed äriprotsesside mudelid firma efektiivsust tõsta. Senised uuringud on aga näidanud, et äriringkonnas kasutatavate mudelite kvaliteet varieerub kõvasti. Need uuringud on peaasjalikult keskendunud kogenud äriprotsesside loojatele, samas kui algajate tüüpilised vead on jäänud tähelepanuta. Just algajate tavalisemate vigade uurimine on aga kasulik, et tõhustada õppematerjalide arendamist. Käesolev töö sisaldab kahte uuringut, mille eesmärk on pakkuda vastuseid nendele küsimustele. Esimene neist uurib algajate poolt tehtud äriprotsesside mudeleid ja selgitab välja, milliseid vigu nad kõige enam teevad. Teine uuring võrdleb algajate ja professionaalide poolt tehtud mudeleid ja toob välja erinevused nende vahel.During the last decade, business process modelling has gained popularity as a way to make the processes of a company explicit and to support the analysis, improvement, implementation and monitoring of business processes. High-quality business process models can therefore support an organization in its continuous improvement efforts. Previous research however has found that the quality of business process models in commercial use is highly heterogeneous. These previous studies have largely focused on models produced by expert users, while the question of what typical errors are made by novice modellers has thus far been left relatively unexplored. Yet, insights into the question of typical errors of novice users (relative to expert ones) can inform the design of process modelling learning material. This thesis contains two studies aimed at providing some answers to the above question. The first part of this thesis examines process models produced by novices and investigates typical errors in their syntax and style. The second part aims at identifying differences between models of the same business process produced by expert vs. novice modellers

Generating optimized configurable business process models in scenarios subject to uncertainty

Context: The quality of business process models (i.e., software artifacts that capture the relations between the organizational units of a business) is essential for enhancing the management of business processes. However, such modeling is typically carried out manually. This is already challenging and time consuming when (1) input uncertainty exists, (2) activities are related, and (3) resource allocation has to be considered. When including optimization requirements regarding flexibility and robustness it becomes even more complicated potentially resulting into non-optimized models, errors, and lack of flexibility. Objective: To facilitate the human work and to improve the resulting models in scenarios subject to uncertainty, we propose a software-supported approach for automatically creating configurable business process models from declarative specifications considering all the aforementioned requirements. Method: First, the scenario is modeled through a declarative language which allows the analysts to specify its variability and uncertainty. Thereafter, a set of optimized enactment plans (each one representing a potential execution alternative) are generated from such a model considering the input uncertainty. Finally, to deal with this uncertainty during run-time, a flexible configurable business process model is created from these plans. Results: To validate the proposed approach, we conduct a case study based on a real business which is subject to uncertainty. Results indicate that our approach improves the actual performance of the business and that the generated models support most of the uncertainty inherent to the business. Conclusions: The proposed approach automatically selects the best part of the variability of a declarative specification. Unlike existing approaches, our approach considers input uncertainty, the optimization of multiple objective functions, as well as the resource and the control-flow perspectives. However, our approach also presents a few limitations: (1) it is focused on the control-flow and the data perspective is only partially addressed and (2) model attributes need to be estimated.Ministerio de Ciencia e Innovación TIN2009-1371

Quantitative Measurable Concepts to Visualize Business Process Improvement

Business process improvement evaluation enables performance indicators to be used alongside process improvement techniques in order to quantitatively compare measurement information between the as-is and to-be processes. Limitations of the present methods of business process improvement indicate there is scope for looking at the problem in a different way. Business processes are commonly modelled as diagrams which at their fundamental level are complex networks. This suggests the question as to whether complex network analysis (CNA) has anything to contribute to business process improvement. We develop a technique of projecting a business process model onto the sub-space of a complex network and identify the measurable concepts that can be useful in business process improvement. The measurable concepts from CNA are combined with Time and Cost metrics from the simulation technique to visualize and track improvement efforts and satisfy improvement requirements

Використання комп'ютерного бачення та нечіткої логіки для оцінки якості моделей бізнес-процесів

In this paper we propose a method for quality assessment of business process models using computer vision and fuzzy logic. OpenCV library usage as well as bypassing of its drawbacks of template matching is considered. Membership functions of metrics of the business process model quality are outlined. Obtained results and future research are discussed.В даній роботі пропонується метод оцінки якості моделей бізнес-процесів на основі комп'ютерного бачення та нечіткої логіки. Розглянуто використання бібліотеки OpenCV та обхід її недоліків щодо пошуку за шаблоном. Наведено функції належності метрик якості моделей бізнес-процесів. Описано отримані результати та напрямки подальших досліджень