A Domain Ontology for Platform Ecosystems - Zarnescu, Corinna; Dunzer, Sebastian (Proceedings of the 16th International Conference on Wirtschaftsinformatik, 2021)
Platforms have disrupted several business sectors and daily life in general. Platforms facilitate collaboration between different partners, which leads to the emergence of an ecosystem. During recent years, both research fields platforms and ecosystems have made significant progress. Since the terminologies originate from different backgrounds and are put into play in various sectors, a certain vagueness surrounds platforms and ecosystems. The present paper, therefore, adds to academia by providing an ontology – an abstraction of a real-world phenomenon – for platform ecosystems. The ontology comprises concepts from the platforms, business ecosystems, and platform ecosystems domains. The evaluation with three real-world platform ecosystems from different industries verifies that the platform-ecosystem-specific requirements were met in the ontology. <br
Keywords: Platform Ecosystem; Domain Ontology; Platform; Ecosystem

A Framework to Evaluate the Viability of Robotic Process Automation for Business Process Activities - Wellmann, Christian; Stierle, Matthias; Dunzer, Sebastian; Matzner, Martin (Business Process Management: Blockchain and Robotic Process Automation Forum, 2020)
Robotic process automation (RPA) is a technology for centralized automation of business processes. RPA automates user interaction with graphical user interfaces, whereby it promises efficiency gains and a reduction of human negligence during process execution. To harness these benefits, organizations face the challenge of classifying process activities as viable automation candidates for RPA. Therefore, this work aims to support practitioners in evaluating RPA automation candidates. We design a framework that consists of thirteen criteria grouped into five perspectives which offer different evaluation aspects. These criteria leverage a profound understanding of the process step. We demonstrate and evaluate the framework by applying it to a real-life data set.

A technology-specific process mining maturity grid for manufacturing and logistics - Dunzer, Sebastian; Zilker, Sandra; Weinzierl, Sven; Tang, Willi; Dieckmann, Felix; Stenglein, Simon; Rist, Johannes; Matzner, Martin (International Journal of Production Research, 2025)
Process mining applies data mining to the data produced in the slipstream of the executing process instances. Hence, it enables organisations gain transparency about how exactly the processes are executed. Organisations can use this transparency to find opportunities to improve their business processes, such as manufacturing and logistics. Previous research provides methodological guidance on how process mining projects can be conducted and how process mining capabilities can be assessed from an organisational perspective. However, organisations tend to lack experience with novel technologies, which is why they often miss out on opportunities to improve their operations using the insights gained through process mining. This study is the first to contribute to the existing literature by examining process mining capabilities from a technological perspective, focussing on the manufacturing and logistics domain. We conducted a systematic case study review and 12 semi-structured interviews with different manufacturing organisations to develop and validate our technology-specific process mining maturity grid for manufacturing and logistics. The resulting maturity grid is a tool for demonstrating development paths for the further application of process mining in organisations. The grid details maturity using three focus areas (i.e. data input, process mining utilisation, and integration) structured into five maturity level
Keywords: process mining; maturity grid; process improvement; manufacturing; logistics

Conceptualizing Business Process Violations - Dunzer, Sebastian; Ließmann, Annina; Stierle, Matthias; Matzner, Martin (CEUR Workshop Proceedings, 2024)
Process analysts seek to gain insights into process execution to continuously improve their business processes. Business process deviation analysis can prove useful in identifying process improvements. However, differentiating between relevant and irrelevant deviations poses challenges. This is due to commercial process mining software flagging a high number of process instances as deviations from process definitions (e.g., process models). Against this background, we define and conceptualize business process violations that indicate undesired behavior. This paper defines business process violations based on regulatory business process compliance. Furthermore, this paper conveys a conceptual model connecting business process violations, the process definition they violate, and their organizational setting. We structure these components in ten dimensions of business process violations. The conceptual model and dimensions were developed and evaluated based on real-world examples provided by a German software vendor. Our conceptualization provides the vocabulary and raises awareness to discuss deviations’ important aspects that delineate violations
Keywords: business process deviations; business process violation; process analysis; conformance; compliance

Conformance checking: A state-of-the-art literature review - Dunzer, Sebastian; Stierle, Matthias; Matzner, Martin; Baier, Stephan (Proceedings of the 11th International Conference on Subject-Oriented Business Process Management, S-BPM ONE 2019, 2019)

Design Principles for Comprehensible Process Discovery in Process Mining - Stierle, Matthias; Zilker, Sandra; Dunzer, Sebastian; Tenschert, Johannes; Karagegova, Gergana (28th European Conference on Information Systems - Liberty, Equality, and Fraternity in a Digitizing World, ECIS 2020, Marrakech, Morocco, June 15-17, 2020, 2020)

Design Principles for Using Business Process Management Systems - Dunzer, Sebastian; Tang, Willi; Höchstädter, Nico; Zilker, Sandra; Matzner, Martin (Business Process Management Workshops, 2023)

Design Principles for Using Business Process Management Systems - Dunzer, Sebastian; Tang, Willi; Höchstädter, Nico; Zilker, Sandra; Matzner, Martin (Business Process Management Workshops, 2023)
Organizations aim to achieve operational excellence to reduce costs and improve the quality of their business processes. Business process management (BPM) enables continuous improvement of business processes. Business process management systems (BPMS) serve as an entry point to BPM activities and afford firms to manage, execute, and automate business processes. This study follows an action design research approach to design a BPMS in use together with a medium-sized German fashion company. We concurrently evaluated the artifact-in-use by tracking performance indicators that are aligned with the company’s objective. As a result of our formalization of learning, we propose seven design principles for using BPMSs to achieve continuous improvement of business processes. These design principles comprise user management, process modeling, automation, logging, monitoring, integration, and case handling.<br
Keywords: Business process management systems; action design research; design principles

Machine learning in business process management: A systematic literature review - Weinzierl, Sven; Zilker, Sandra; Dunzer, Sebastian; Matzner, Martin (Expert Systems With Applications, 2024)
Machine learning (ML) provides algorithms to create computer programs based on data without explicitly programming them. In business process management (BPM), ML applications are used to analyse and improve processes efficiently. Three frequent examples of using ML are providing decision support through predictions, discovering accurate process models, and improving resource allocation. This paper organises the body of knowledge on ML in BPM. We extract BPM tasks from different literature streams, summarise them under the phases of a process's lifecycle, explain how ML helps perform these tasks and identify technical commonalities in ML implementations across tasks. This study is the first exhaustive review of how ML has been used in BPM. We hope that it can open the door for a new era of cumulative research by helping researchers to identify relevant preliminary work and then combine and further develop existing approaches in a focused fashion. Our paper helps managers and consultants to find ML applications that are relevant in the current project phase of a BPM initiative, like redesigning a business process. We also offer – as a synthesis of our review – a research agenda that spreads ten avenues for future research, including applying novel ML concepts like federated learning, addressing less regarded BPM lifecycle phases like process identification, and delivering ML applications with a focus on end-users.
CRIS-Team Scopus Importer:2024-06-14
Keywords: BPM lifecycle; Business process management; Deep learning; Literature review; Machine learning

Modeling Platform Ecosystems - Pauli, Tobias; Marx, Emanuel; Dunzer, Sebastian; Matzner, Martin (ER Forum, Demo and Posters co-located with 39th International Conference on Conceptual Modeling (ER), 2020)
Platforms facilitate the creation of complementary modulesby third parties and act as intermediaries between different groups of ac-tors. Due to the high degree of collaboration between actors, ecosystemsevolve around such platforms. As a result of digitalization, platform busi-ness models are becoming viable in more and more domains. Despite theincreasing variety of different platform ecosystems, means to clearly spec-ify them are scarce. This conceptual ambiguity impedes comparabilityof research and knowledge accumulation. To solve this problem, we pro-pose a domain-specific platform ecosystems modeling language (PEML)which builds on seminal platform ecosystem literature. We demonstratePEML by modeling two real-life platform ecosystems based on onlinecase studies. Our results support researchers and practitioners alike inclearly specifying platform ecosystem
Keywords: Platform·Ecosystem·Domain-Specific Modeling Language

Notation-agnostic Subprocess Modeling for Adaptive Case Management - Tenschert, Johannes; Dunzer, Sebastian; Matzner, Martin (Lecture Notes in Information Systems and Organisation, 2021)
Even though knowledge work comprises tasks that cannot be modeled a priori, some structure for routine work or an outlined course of action is often necessary. Knowledge workers perform structured and ad-hoc activities – and a wide range of requirements typically yields many tools. To avoid redundant and scattered process information, a single system of record capable of consolidating flexibility and modeling of different aspects of processes is desirable. For knowledge-intensive processes, we outline how to combine process models for structured routine aspects with the ad-hoc activities and flexibility of social software in the same context. Therefore, a case comprises modeled subprocesses as well as ad-hoc activities and artifacts. The shared context allows to transparently combine aspects and deviate from predefined models. We prototypically implement our approach and show that ad-hoc project management and different notations can be applied within the same case.<br
CRIS-Team Scopus Importer:2021-11-26
Keywords: adaptive case management; subprocess modeling; knowledge-intensive business process; process flexibility

Predictive business process deviation monitoring - Weinzierl, Sven; Dunzer, Sebastian; Tenschert, Johannes; Zilker, Sandra; Matzner, Martin (Proceedings of the 29th European Conference on Information Systems, 2021)
Business processes run at the core of an organisation’s value creation and are often the target of optimisation efforts. Organisations aim at adhering to their optimised processes. However, deviations from the optimised process still occur and may potentially impede efficiency in process executions. Conformance checking can provide valuable insights regarding past process deviations, but it cannot identify deviations before they occur. Outcome-oriented predictive business process monitoring (PBPM) provides a set of methods to predict process outcomes, e. g. key performance indicators. We propose an outcome-oriented PBPM method for predictive deviation monitoring using conformance checking and deep learning to draw the most out of the two domains. By leveraging early intervention, the method supports the proactive handling of deviations, i.e., inserted and missing events in process instances, to reduce their potential harm. Our evaluation shows that the method can predict business process deviations with high predictive quality, particularly for processes with fewer variants. <br
Keywords: predictive business process monitoring; conformance checking; deep learning; process mining

Prescriptive business process monitoring for recommending next best actions - Weinzierl, Sven; Dunzer, Sebastian; Zilker, Sandra; Matzner, Martin (Proceedings of the 18th International Conference on Business Process Management Forum, 2020)
Predictive business process monitoring (PBPM) techniques predict future process behaviour based on historical event log data to improve operational business processes. Concerning the next activity prediction, recent PBPM techniques use state-of-the-art deep neural networks (DNNs) to learn predictive models for producing more accurate predictions in running process instances. Even though organisations measure process performance by key performance indicators (KPIs), the DNN’s learning procedure is not directly affected by them. Therefore, the resulting next most likely activity predictions can be less beneficial in practice. Prescriptive business process monitoring (PrBPM) approaches assess predictions regarding their impact on the process performance (typically measured by KPIs) to prevent undesired process activities by raising alarms or recommending actions. However, none of these approaches recommends actual process activities as actions that are optimised according to a given KPI. We present a PrBPM technique that transforms the next most likely activities into the next best actions regarding a given KPI. Thereby, our technique uses business process simulation to ensure the control-flow conformance of the recommended actions. Based on our evaluation with two real-life event logs, we show that our technique’s next best actions can outperform next activity predictions regarding the optimisation of a KPI and the distance to the actual process instances. <br
Keywords: Prescriptive business process monitoring; predictive business process monitoring; business process management

Recent Advances in Data-Driven Business Process Management - Ackermann, Lars; Käppel, Martin; Marcus, Laura; Moder, Linda; Dunzer, Sebastian; Hornsteiner, Markus; Ließmann, Annina; Zisgen, Yorck; Empl, Philip; Herm, Lukas-Valentin; Neis, Nicolas; Neuberger, Julian; et al. (2024)
The rapid development of cutting-edge technologies, the increasing volume of data and also the availability and processability of new types of data sources has led to a paradigm shift in data-based management and decision-making. Since business processes are at the core of organizational work, these developments heavily impact business process management (BPM) as a crucial success factor for organizations. In view of this emerging potential, data-driven business process management has become a relevant and vibrant research area. Given the complexity and interdisciplinarity of the research field, this position paper therefore presents research insights regarding data-driven BP
Keywords: BPM; Process Mining; Research Agenda; Data-centric

The MobIS-Challenge 2019 - A Report on the WI-2019-Workshop on Model-Based Compliance in Information Systems - Baier, Stephan; Dunzer, Sebastian; Fettke, Peter; Houy, Constantin; Matzner, Martin; Pfeiffer, Peter; Rehse, Jana Rebecca; Scheid, Martin; Stephan, Sebastian; Stierle, Matthias; Willems, Brian (Enterprise Modelling and Information Systems Architectures - International Journal of Conceptual Modeling, 2020)
Information systems (IS) can significantly support the organization of business processes. However, the proceeding digitalization of processes can also lead to an increasing organizational complexity and the need to more intensely investigate the adherence to external or internal compliance rules. Process-related data from IS and underlying process models can, however, also contribute to an effective compliance checking. This paper summarizes the motivation, the setup, the data set and the results of the 2019 MobIS-Challenge which was conducted as a workshop at WI 2019 in Siegen, Germany. Results submitted to the challenge are presented in detail and directions for future work are discussed.<br
Keywords: GRC; Governance; Risk and Compliance; BPM; Business Process Management; Process Mining; Data Set; DCR; Declarative; Dynamic Condition Response Graph

The Status Quo of Process Mining in the Industrial Sector - Dunzer, Sebastian; Zilker, Sandra; Marx, Emanuel; Grundler, Valentin; Matzner, Martin (Proceedings of the 16th International Conference on Wirtschaftsinformatik (WI), 2021)
Since process mining started to reveal the potential of event logs, it has been applied in various process settings ranging from healthcare to production. Every single setting poses its challenges to process analysts who want to apply process mining. The present paper aims at minimizing such challenges for enterprises in the industrial sector by providing a coherent overview of existing cases. Our systematic literature review relates each production form and layout from existing case studies to the applied process mining type. Further, we use Porter's value chain to distinguish operations from other primary and support activities in production. We present the application of process mining, particularly for the production process and the primary activities other than the operations. The results indicate that process mining fits best with cellular production layouts with batch or line production processes.<br
Keywords: process mining; production; production form; production layout