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2024 | Book

Enterprise Design, Operations, and Computing

27th International Conference, EDOC 2023, Groningen, The Netherlands, October 30 – November 3, 2023, Proceedings

Editors: Henderik A. Proper, Luise Pufahl, Dimka Karastoyanova, Marten van Sinderen, João Moreira

Publisher: Springer Nature Switzerland

Book Series : Lecture Notes in Computer Science

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About this book


This book constitutes the refereed proceedings of the 27th International Conference on Enterprise Design, Operations, and Computing, EDOC 2023, held in Groningen, The Netherlands, during October 30–November 3, 2023.
The 12 full papers included in this book were carefully reviewed and selected from 36submissions. They were organized in topical sections as follows: Enterprise Modeling, Enterprise Architecture & Engineering, Model-Based Software Engineering, Enterprise Analysis with Process Mining, Process Improvement & Engineering, and Modeling in an Enterprise Context.

Table of Contents

Frontmatter

Enterprise Modeling, Architecture and Engineering

Frontmatter
A System Core Ontology for Capability Emergence Modeling
Abstract
To properly understand organizational adaptation and innovation, it is critical to understand the emergence phenomenon, i.e., how the capabilities of a system emerge after changes. However, for this, we should be able to explain systems, their structure, behavior, and capabilities. In pursuit of an understanding of the emergence phenomenon and the nature of those new kinds of systems in organizations, we propose a well-founded system core ontology based on the Unified Foundational Ontology. The ontology is also grounded in system science definitions and disposition theories. For a more integrated explanation of emergence, the proposed ontology considers distinct perspectives of a system, such as its composition, structure, properties, and functions. In the end, we discuss the applications and implications of the proposed ontology on the enterprise architecture area and emergence modeling.
Rodrigo F. Calhau, Tiago Prince Sales, Ítalo Oliveira, Satyanarayana Kokkula, Luís Ferreira Pires, David Cameron, Giancarlo Guizzardi, João Paulo A. Almeida
What Do I Get from Modeling?
An Empirical Study on Using Structural Conceptual Models
Abstract
In the context of enterprises, a wide range of models is developed and used for diverse purposes. Due to the investments involved in modeling, these models should ideally be used in projects in which their benefits outweigh their costs. The analysis of modeling benefits and costs requires an in-depth understanding of the goals of modeling and the properties of models that influence their achievement. This is an issue that has not been sufficiently investigated in the literature. Therefore, we conducted an empirical study to identify and understand the goals modelers aim to achieve through their models, the properties of the models that can aid in the achievement of these goals, and how they assess this achievement. In this study, we focus on a subset of these models, namely structural conceptual models. We found empirical evidence to state that modelers usually achieve more than one goal that can vary among six types of functional goals of modeling and four types of quality goals of modeling. Moreover, according to them, there are six properties of structural conceptual models that can aid in satisfying these goals. Finally, the analysis presented insights into why modelers only subjectively assess the satisfaction of their modeling goals.
Isadora Valle Sousa, Tiago Prince Sales, Eduardo Guerra, Luiz Olavo Bonino da Silva Santos, Giancarlo Guizzardi
A Generic and Customizable Genetic Algorithms-Based Conceptual Model Modularization Framework
Abstract
Conceptual models need to be comprehensible and maintainable by humans to exploit their full value in faithfully representing a subject domain. Modularization, i.e. breaking down the monolithic model into smaller, comprehensible chunks has proven very valuable to maintain this value even for very large models. The quality of modularization however often depends on application-specific requirements, the domain, and the modeling language. A well-defined generic modularizing framework applicable to different modeling languages and requirements is lacking. In this paper, we present a customizable and generic multi-objective conceptual models modularization framework. The multi-objective aspect supports addressing heterogeneous requirements while the framework’s genericity supports modularization for arbitrary modeling languages and its customizability is provided by adopting the modularization configuration up to the level of using user-defined heuristics. Our approach applies genetic algorithms to search for a set of optimal solutions. In this paper, we present the details of our Generic Genetic Modularization Framework with a case study to show i) the feasibility of our approach by modularizing models from multiple modeling languages, ii) the customizability by using different objectives for the modularization quality, and, finally, iii) a comparative performance evaluation of our approach on a dataset of ER and ECore models.
Syed Juned Ali, Jan Michael Laranjo, Dominik Bork
MARTSIA: Enabling Data Confidentiality for Blockchain-Based Process Execution
Abstract
Multi-party business processes rely on the collaboration of various players in a decentralized setting. Blockchain technology can facilitate the automation of these processes, even in cases where trust among participants is limited. Transactions are stored in a ledger, a replica of which is retained by every node of the blockchain network. The operations saved thereby are thus publicly accessible. While this enhances transparency, reliability, and persistence, it hinders the utilization of public blockchains for process automation as it violates typical confidentiality requirements in corporate settings. In this paper, we propose MARTSIA: A Multi-Authority Approach to Transaction Systems for Interoperating Applications. MARTSIA enables precise control over process data at the level of message parts. Based on Multi-Authority Attribute-Based Encryption (MA-ABE), MARTSIA realizes a number of desirable properties, including confidentiality, transparency, and auditability. We implemented our approach in proof-of-concept prototypes, with which we conduct a case study in the area of supply chain management. Also, we show the integration of MARTSIA with a state-of-the-art blockchain-based process execution engine to secure the data flow.
Edoardo Marangone, Claudio Di Ciccio, Daniele Friolo, Eugenio Nerio Nemmi, Daniele Venturi, Ingo Weber

Model-Based Software Engineering

Frontmatter
Building an Ontological Bridge Between Supply Chain Resilience and IoT Applications
Abstract
The complexity of modern-day supply chains makes logistics operations more vulnerable towards disturbances, which endangers sustainability goals in the short-term. Local disturbances might effect logistics at large, as we typically see in congested urban areas. As a consequence, the Internet of Things (IoT) is gaining attention as a novel paradigm that promotes interconnected networks of context-aware electronic devices used for remote monitoring and control. These capabilities may stimulate anticipatory behaviour and more resilient supply chains, but a clear framework prescribing which objects to empower with electronic devices is still lacking. In this paper, we aim to semantically bridge the resilience and IoT paradigms in logistics environments. The ontology is developed by means of a bibliometric- and systematic literature study in search of essential concepts, and a field study to evaluate the ontology’s effectiveness. Our ontology can form the basis to enhance resilience by replacing risk assessments with condition-based control mechanisms, resulting in better cooperation between human and software agents to resolve disturbances quicker, and more accurate training of machine learning algorithms in favour of autonomous decision making.
Martijn Koot, Martijn R. K. Mes, Maria E. Iacob
A Model-Driven Approach to SAP S/4HANA Development
Abstract
While Enterprise Resource Planning systems such as SAP S/4HANA play a key role for many companies, they rarely come alone but are connected to other applications via interfaces. Usually, interface development is done for each project individually. However, there can still be many common requirements shared by multiple projects causing repetitive coding and leading to a maintenance nightmare. In this work, we introduce a novel approach to SAP S/4HANA development driven by models from which running code can be generated automatically. Thus, repetitive coding is avoided and development effort reduced. To this end, we discuss different methods of importing externally generated code into SAP S/4HANA. This is contrary to the development style pursued traditionally and required an analysis of how different development objects must be represented to be importable. As a case study, we apply our approach to interface development. However, beyond this use case, we hope to see applications of our approach in various other areas in the future.
Jonathan Neugebauer, Jonas Hochstrat, Konrad Schneid, Daniel Sigge, Herbert Kuchen

Enterprise Analysis and Improvement with Process Mining

Frontmatter
A Methodology for the Analysis of Robotic Systems via Process Mining
Abstract
Robotic systems are widely adopted in various application scenarios. A very complex task for developers is the analysis of robotic systems’ behavior, which is required to ensure trustworthy interaction with the surrounding environment. Available analysis techniques, like field tests, depend on human observations, while automated techniques, like formal analysis, suffer from the complexity of the systems. Recent works show the applicability of process mining for the analysis of event data generated by robots to increase the understanding of system behavior. However, robots produce data at such a low granularity that process mining cannot provide a meaningful description of the system’s behavior. We tackle this problem by proposing a process mining-based methodology to prepare and analyze the data coming from the execution of a robotic system. The methodology supports the system developer in producing an event log compliant with process mining techniques and is used to analyze multiple perspectives of robots’ behavior. We implemented the methodology in a tool supporting its phases. We use the tool on a robotic smart agriculture scenario to evaluate the feasibility and effectiveness of the methodology.
Flavio Corradini, Sara Pettinari, Barbara Re, Lorenzo Rossi, Francesco Tiezzi
An Approach for Face Validity Assessment of Agent-Based Simulation Models Through Outlier Detection with Process Mining
Abstract
When designing simulations, the objective is to create a representation of a real-world system or process to understand, analyze, predict, or improve its behavior. Typically, the first step in assessing the credibility of a simulation model for its intended purpose involves conducting a face validity check. This entails a subjective assessment by individuals knowledgeable about the system to determine if the model appears plausible. The emerging field of process mining can aid in the face validity assessment process by extracting process models and insights from event logs generated by the system being simulated. Process mining techniques, combined with the visual representation of discovered process models, offer a novel approach for experts to evaluate the validity and behavior of simulation models. In this context, outliers can play a key role in evaluating the face validity of simulation models by drawing attention to unusual behaviors that can either raise doubts about or reinforce the model’s credibility in capturing the full range of behaviors present in the real world. Outliers can provide valuable information that can help identify concerns, prompt improvements, and ultimately enhance the validity of the simulation model. In this paper, we propose an approach that uses process mining techniques to detect outlier behaviors in agent-based simulation models with the aim of utilizing this information for evaluating face validity of simulation models. We illustrate our approach using the Schelling segregation model.
Rob Bemthuis, Sanja Lazarova-Molnar
Progressing from Process Mining Insights to Process Improvement: Challenges and Recommendations
Abstract
Many organizations have adopted process mining to analyze their business processes, gain insights into their performance, and identify improvement opportunities. Several academic case studies and reports from practice leave no doubt that process mining tools can deliver substantial value to organizations and help them to realize improvements. However, both organizations and academics have also realized that the path from obtaining insights via process mining to realizing the desired improvements is far from trivial. Existing process mining methodologies pay little to no attention to this matter and mainly focus on how to obtain insights through process mining. In this paper, we address this research gap by conducting a qualitative study based on 17 semi-structured interviews. We identify seven challenges pertaining to translating process mining insights into process improvements. Furthermore, we provide five specific recommendations for practitioners and stakeholders that should be considered before starting a new process mining initiative. By doing so, we aim to close the gap between insights and action and help organizations to effectively use process mining to realize process improvements.
Vinicius Stein Dani, Henrik Leopold, Jan Martijn E. M. van der Werf, Hajo A. Reijers
Developing Taxonomies for Business Process Engineering
Abstract
In many business environments, we find extensive business process structures that consist of many individual processes, each with a complex composition of activities. The elements in the processes are often based on an ad-hoc, existing way of working, which is not always properly documented. The processes evolve over time, not rarely on a per-process basis. Consequently, process definitions diverge and the use of process elements within and between process definitions becomes misaligned. To address this issue, we propose the use of catalogs of standardized process building blocks in business process engineering. Different from approaches using patterns, we base our catalogs on foundational parts (which we call primitives) organized in three dimensions: business process activities, objects manipulated by activities, and actors performing activities – starting with the semantics of processes, not the syntax. To provide a solid basis for the structuring of each of the dimensions (and hence the organization of the foundational parts in the catalogs), we use taxonomies. In this paper, we discuss the development of these taxonomies. We apply a slightly modified existing taxonomy development method, which uses both deductive and inductive steps. We discuss the development of one taxonomy in detail, basing the inductive steps on processes from a complex, real-world case organization. In doing so, we make a first step towards a business process engineering approach that is centered on a process-content-first point of view, aligned with the needs of the process management practice.
Ton Soetekouw, Paul Grefen, Irene Vanderfeesten, Oktay Turetken

Modeling in an Enterprise Context

Frontmatter
A Taxonomy for Platform Revenue Models: An Empirical-to-Conceptual Development Approach
Abstract
In the field of Information Systems and Software Engineering, taxonomies are widely employed to organize and present well-designed knowledge. They play a crucial role in identifying relevant dimensions and characteristics associated with the objects under study. This paper focuses specifically on revenue models for platform business models, which facilitate the connection between providers and consumers in two-sided markets. For example, the Vinted Marketplace charges a transaction-based fee of 5% for each item sold, while nebenan.de offers platform access for a monthly subscription fee. Although these revenue model types differ, they both lead to distinctive and successful revenue models. Understanding and formalizing these revenue mechanisms is fundamental for the systematic design of revenue models for platform business models. This paper follows a proven taxonomy development method with two empirical-to-conceptual iteration cycles involving seven use cases. It introduces a comprehensive taxonomy comprising 15 dimensions and 79 characteristics. The proposed taxonomy contributes to the formalization of revenue models for platform business models and enhances the current understanding of the monetization strategies used by digital platforms to generate revenues. This paper supports researchers and practitioners involved in the design process of platform business models.
Nedo Bartels, Matthias Koch, Anna Schmitt, Jaap Gordijn
Conceptual Modeling in Support of Economic and Regulatory Viability Assessment - A Reality Check on the Example of Developing an Energy Community
Abstract
This paper offers an assessment of the extent to which conceptual modeling can be used for a conjoint assessment of regulatory and economic viability of new projects in the electricity sector, with a particular focus on developing energy communities. To this end, we establish a set of challenges resulting out of a confrontation of, on the one hand, the observed relevance of conjointly assessing the regulatory and economic viability for electricity sector projects, and on the other hand, the fact that no dedicated efforts exist which explicitly target such a conjoint assessment. Then, using a realistic scenario we show how two selected conceptual modeling languages can be used for a conjoint assessment: Legal GRL, for regulatory viability, and e \(^{3}\) value, for economic viability. Finally, we discuss lessons learned from our experience, among others, a need for a taxonomy for energy sector specific regulation and the use of value network patterns.
Sybren de Kinderen, Qin Ma, Monika Kaczmarek-Heß, Rik Eshuis
Backmatter
Metadata
Title
Enterprise Design, Operations, and Computing
Editors
Henderik A. Proper
Luise Pufahl
Dimka Karastoyanova
Marten van Sinderen
João Moreira
Copyright Year
2024
Electronic ISBN
978-3-031-46587-1
Print ISBN
978-3-031-46586-4
DOI
https://doi.org/10.1007/978-3-031-46587-1

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