@inproceedings{63397,
  abstract     = {{Decarbonizing industrial process heat is a crucial step in mitigating climate change. While Process Mining (PM) has gained traction in sustainability research—such as optimizing production scheduling to reduce energy use or accounting for carbon footprints—it has largely overlooked the challenges and opportunities related to thermal energy, accounting for 66% of total energy demand in industrial processes. At the same time, Heat Integration (HI) is an established engineering discipline focused on maximizing the efficiency of thermal energy systems. However, HI traditionally relies on static or incomplete data about energy demands, limiting its effectiveness and accuracy. In this paper, we propose a novel framework that combines PM and HI to enable data-driven, process- and product-centric modeling of industrial energy demands. By integrating event logs and thermal energy data, our approach allows for a fine-grained analysis of heat demand patterns corresponding to specific process activities and product variants. We demonstrate the applicability and advantages of the framework by simulating a pharmaceutical manufacturing process and evaluating energy demands and heat recovery potentials. Our findings show that our PM-enabled HI framework provides more accurate and actionable insights into the temporal and product-specific variation of thermal energy demands. By capturing the causal relationships between process activities, product characteristics, and energy consumption, our approach enables improved analysis, planning, and optimization for heat recovery and process decarbonization. This integration of PM and HI expands the analytical tools for both disciplines and contributes to advancing the sustainable transformation of industrial processes.}},
  author       = {{Zapata Gonzalez, David Ricardo and Brennig, Katharina and Benkert, Kay and Schlosser, Florian and Müller, Oliver}},
  booktitle    = {{ACM SIGEnergy Energy Informatics Review}},
  issn         = {{2770-5331}},
  number       = {{3}},
  pages        = {{19--31}},
  publisher    = {{Association for Computing Machinery (ACM)}},
  title        = {{{Process Mining for Robust Heat Integration through Process- and Product-Centric Energy Demand Modeling}}},
  doi          = {{10.1145/3777518.3777520}},
  volume       = {{5}},
  year         = {{2025}},
}

@article{54434,
  author       = {{Brock, Jonathan and Brennig, Katharina and Löhr, Bernd and Bartelheimer, Christian and von Enzberg, Sebastian and Dumitrescu, Roman}},
  journal      = {{Business & Information Systems Engineering}},
  title        = {{{Improving Process Mining Maturity: From Intentions to Action}}},
  doi          = {{10.1007/s12599-024-00882-7}},
  year         = {{2024}},
}

@inproceedings{54589,
  author       = {{Brennig, Katharina and Löhr, Bernd and Brock, Jonathan and Reineke, Malte Fabian and Bartelheimer, Christian}},
  booktitle    = {{Americas Conference on Information Systems (AMCIS)}},
  title        = {{{Maximizing the Impact of Process Mining Research: Four Strategic Guidelines}}},
  year         = {{2024}},
}

@inproceedings{37058,
  abstract     = {{Digital technologies have made the line of visibility more transparent, enabling customers to get deeper insights into an organization’s core operations than ever before. This creates new challenges for organizations trying to consistently deliver high-quality customer experiences. In this paper we conduct an empirical analysis of customers’ preferences and their willingness-to-pay for different degrees of process transparency, using the example of digitally-enabled business-to-customer delivery services. Applying conjoint analysis, we quantify customers’ preferences and willingness-to-pay for different service attributes and levels. Our contributions are two-fold: For research, we provide empirical measurements of customers’ preferences and their willingness-to-pay for process transparency, suggesting that more is not always better. Additionally, we provide a blueprint of how conjoint analysis can be applied to study design decisions regarding changing an organization’s digital line of visibility. For practice, our findings enable service managers to make decisions about process transparency and establishing different levels of service quality.
}},
  author       = {{Brennig, Katharina and Müller, Oliver}},
  booktitle    = {{Hawaii International Conference on System Sciences}},
  keywords     = {{Digital Services, Line of Visibility, Process Transparency, Customer Preferences, Conjoint Analysis}},
  location     = {{Lāhainā}},
  title        = {{{More Isn’t Always Better – Measuring Customers’ Preferences for Digital Process Transparency}}},
  year         = {{2023}},
}

@inbook{50450,
  author       = {{Brennig, Katharina and Benkert, Kay and Löhr, Bernd and Müller, Oliver}},
  booktitle    = {{Business Process Management Workshops}},
  isbn         = {{9783031509735}},
  issn         = {{1865-1348}},
  title        = {{{Text-Aware Predictive Process Monitoring of Knowledge-Intensive Processes: Does Control Flow Matter?}}},
  doi          = {{10.1007/978-3-031-50974-2_33}},
  year         = {{2023}},
}

@inproceedings{50459,
  abstract     = {{Organizations employ process mining to discover, check, or enhance process models based on data from information systems to improve business processes. Even though process mining is increasingly relevant in academia and organizations, achieving process mining excellence and generating business value through its application is elusive. Maturity models can help to manage interdisciplinary teams in their efforts to plan, implement, and manage process mining in organizations. However, while numerous maturity models on business process management (BPM) are available, recent calls for process mining maturity models indicate a gap in the current knowledge base. We systematically design and develop a comprehensive process mining maturity model that consists of five factors comprising 23 elements, which organizations need to develop to apply process mining sustainably and successfully. We contribute to the knowledge base by the exaptation of existing BPM maturity models, and validate our model through its application to a real-world scenario.}},
  author       = {{Brock, Jonathan and Löhr, Bernd and Brennig, Katharina and Seger, Thilo and Bartelheimer, Christian and von Enzberg, Sebastian and Kühn, Arno and Dumitrescu, Roman}},
  booktitle    = {{European Conference on Information Systems (ECIS)}},
  title        = {{{A Process Mining Maturity Model: Enabling Organizations to Assess and Improve their Process Mining Activities}}},
  year         = {{2023}},
}

@inproceedings{36912,
  abstract     = {{Existing process mining methods are primarily designed for processes that have reached a high degree of digitalization and standardization. In contrast, the literature has only begun to discuss how process mining can be applied to knowledge-intensive processes—such as product innovation processes—that involve creative activities, require organizational flexibility, depend on single actors’ decision autonomy, and target process-external goals such as customer satisfaction. Due to these differences, existing Process Mining methods cannot be applied out-of-the-box to analyze knowledge-intensive processes. In this paper, we employ Action Design Research (ADR) to design and evaluate a process mining approach for knowledge-intensive processes. More specifically, we draw on the two processes of product innovation and engineer-to-order in manufacturing contexts. We collected data from 27 interviews and conducted 49 workshops to evaluate our IT artifact at different stages in the ADR process. From a theoretical perspective, we contribute five design principles and a conceptual artifact that prescribe how process mining ought to be designed for knowledge-intensive processes in manufacturing. From a managerial perspective, we demonstrate how enacting these principles enables their application in practice.}},
  author       = {{Löhr, Bernd and Brennig, Katharina and Bartelheimer, Christian and Beverungen, Daniel and Müller, Oliver}},
  booktitle    = {{International Conference on Business Process Management}},
  isbn         = {{978-3-031-16103-2}},
  title        = {{{Process Mining of Knowledge-Intensive Processes: An Action Design Research Study in Manufacturing}}},
  doi          = {{10.1007/978-3-031-16103-2_18}},
  year         = {{2022}},
}