@inproceedings{GronauGrumBender2016,
  author    = {Gronau, Norbert and Grum, Marcus and Bender, Benedict},
  title     = {Determining the optimal level of autonomy in cyber-physical production systems},
  series = {IEEE 14th International Conference on Industrial Informatics (INDIN)},
  booktitle = {IEEE 14th International Conference on Industrial Informatics (INDIN)},
  publisher = {IEEE},
  address   = {New York},
  doi       = {10.1109/INDIN.2016.7819367},
  pages     = {1293 -- 1299},
  year      = {2016},
  abstract  = {Traditional production systems are enhanced by cyber-physical systems (CPS) and Internet of Things. A kind of next generation systems, those cyber-physical production systems (CPPS) are able to raise the level of autonomy of its production components. To find the optimal degree of autonomy in a given context, a research approach is formulated using a simulation concept. Based on requirements and assumptions, a cyber-physical market is modeled and qualitative hypotheses are formulated, which will be verified with the help of the CPPS of a hybrid simulation environment.},
  language  = {en}
}
@incollection{GrumKotarskiAmbrosetal.2021,
  author    = {Grum, Marcus and Kotarski, David and Ambros, Maximilian and Biru, Tibebu and Krallmann, Hermann and Gronau, Norbert},
  title     = {Managing knowledge of intelligent systems},
  series = {Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5-7, 2021, Proceedings},
  volume    = {422},
  booktitle = {Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5-7, 2021, Proceedings},
  editor    = {Shishkov, Boris},
  publisher = {Springer International Publishing},
  address   = {Cham},
  isbn      = {978-3-030-79975-5},
  doi       = {10.1007/978-3-030-79976-2_5},
  pages     = {78 -- 96},
  year      = {2021},
  abstract  = {Since more and more business tasks are enabled by Artificial Intelligence (AI)-based techniques, the number of knowledge-intensive tasks increase as trivial tasks can be automated and non-trivial tasks demand human-machine interactions. With this, challenges regarding the management of knowledge workers and machines rise [9]. Furthermore, knowledge workers experience time pressure, which can lead to a decrease in output quality. Artificial Intelligence-based systems (AIS) have the potential to assist human workers in knowledge-intensive work. By providing a domain-specific language, contextual and situational awareness as well as their process embedding can be specified, which enables the management of human and AIS to ease knowledge transfer in a way that process time, cost and quality are improved significantly. This contribution outlines a framework to designing these systems and accounts for their implementation.},
  language  = {en}
}
@incollection{GrumGronau2021,
  author    = {Grum, Marcus and Gronau, Norbert},
  title     = {Quantification of knowledge transfers},
  series = {Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5-7, 2021, Proceedings},
  volume    = {422},
  booktitle = {Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5-7, 2021, Proceedings},
  editor    = {Shishkov, Boris},
  publisher = {Springer International Publishing},
  address   = {Cham},
  isbn      = {978-3-030-79975-5},
  doi       = {10.1007/978-3-030-79976-2_13},
  pages     = {224 -- 242},
  year      = {2021},
  abstract  = {Faced with the triad of time-cost-quality, the realization of knowledge-intensive tasks at economic conditions is not trivial. Since the number of knowledge-intensive processes is increasing more and more nowadays, the efficient design of knowledge transfers at business processes as well as the target-oriented improvement of them is essential, so that process outcomes satisfy high quality criteria and economic requirements. This particularly challenges knowledge management, aiming for the assignment of ideal manifestations of influence factors on knowledge transfers to a certain task. Faced with first attempts of knowledge transfer-based process improvements [1], this paper continues research about the quantitative examination of knowledge transfers and presents a ready-to-go experiment design that is able to examine quality of knowledge transfers empirically and is suitable to examine knowledge transfers on a quantitative level. Its use is proven by the example of four influence factors, which namely are stickiness, complexity, competence and time pressure.},
  language  = {en}
}
@incollection{GrumGronau2020,
  author    = {Grum, Marcus and Gronau, Norbert},
  title     = {Adaptable knowledge-driven information systems improving knowledge transfers},
  series = {Business modeling and software design : 10th International Symposium, BMSD 2020, Berlin, Germany, July 6-8, 2020, Proceedings},
  volume    = {391},
  booktitle = {Business modeling and software design : 10th International Symposium, BMSD 2020, Berlin, Germany, July 6-8, 2020, Proceedings},
  editor    = {Shishkov, Boris},
  publisher = {Springer International Publishing},
  address   = {Cham},
  isbn      = {978-3-030-52305-3},
  doi       = {10.1007/978-3-030-52306-0_13},
  pages     = {202 -- 220},
  year      = {2020},
  abstract  = {A growing number of business processes can be characterized as knowledge-intensive. The ability to speed up the transfer of knowledge between any kind of knowledge carriers in business processes with AR techniques can lead to a huge competitive advantage, for instance in manufacturing. This includes the transfer of person-bound knowledge as well as externalized knowledge of physical and virtual objects. The contribution builds on a time-dependent knowledge transfer model and conceptualizes an adaptable, AR-based application. Having the intention to accelerate the speed of knowledge transfers between a manufacturer and an information system, empirical results of an experimentation show the validity of this approach. For the first time, it will be possible to discover how to improve the transfer among knowledge carriers of an organization with knowledge-driven information systems (KDIS). Within an experiment setting, the paper shows how to improve the quantitative effects regarding the quality and amount of time needed for an example manufacturing process realization by an adaptable KDIS.},
  language  = {en}
}
@incollection{RojahnAmbrosBiruetal.2023,
  author    = {Rojahn, Marcel and Ambros, Maximilian and Biru, Tibebu and Krallmann, Hermann and Gronau, Norbert and Grum, Marcus},
  title     = {Adequate basis for the data-driven and machine-learning-based identification},
  series = {Artificial intelligence and soft computing},
  booktitle = {Artificial intelligence and soft computing},
  editor    = {Rutkowski, Leszek and Scherer, Rafał and Korytkowski, Marcin and Pedrycz, Witold and Tadeusiewicz, Ryszard and Zurada, Jacek M.},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-031-42504-2},
  doi       = {10.1007/978-3-031-42505-9_48},
  pages     = {570 -- 588},
  year      = {2023},
  abstract  = {Process mining (PM) has established itself in recent years as a main method for visualizing and analyzing processes. However, the identification of knowledge has not been addressed adequately because PM aims solely at data-driven discovering, monitoring, and improving real-world processes from event logs available in various information systems. The following paper, therefore, outlines a novel systematic analysis view on tools for data-driven and machine learning (ML)-based identification of knowledge-intensive target processes. To support the effectiveness of the identification process, the main contributions of this study are (1) to design a procedure for a systematic review and analysis for the selection of relevant dimensions, (2) to identify different categories of dimensions as evaluation metrics to select source systems, algorithms, and tools for PM and ML as well as include them in a multi-dimensional grid box model, (3) to select and assess the most relevant dimensions of the model, (4) to identify and assess source systems, algorithms, and tools in order to find evidence for the selected dimensions, and (5) to assess the relevance and applicability of the conceptualization and design procedure for tool selection in data-driven and ML-based process mining research.},
  language  = {en}
}
@misc{GrumGronau2018,
  author    = {Grum, Marcus and Gronau, Norbert},
  title     = {Process modeling within augmented reality},
  series = {Business Modeling and Software Design, BMSD 2018},
  volume    = {319},
  journal   = {Business Modeling and Software Design, BMSD 2018},
  publisher = {Springer},
  address   = {Berlin},
  isbn      = {978-3-319-94214-8},
  issn      = {1865-1348},
  doi       = {10.1007/978-3-319-94214-8_7},
  pages     = {99 -- 115},
  year      = {2018},
  abstract  = {The collaboration during the modeling process is uncomfortable and characterized by various limitations. Faced with the successful transfer of first process modeling languages to the augmented world, non-transparent processes can be visualized in a more comprehensive way. With the aim to rise comfortability, speed, accuracy and manifoldness of real world process augmentations, a framework for the bidirectional interplay of the common process modeling world and the augmented world has been designed as morphologic box. Its demonstration proves the working of drawn AR integrations. Identified dimensions were derived from (1) a designed knowledge construction axiom, (2) a designed meta-model, (3) designed use cases and (4) designed directional interplay modes. Through a workshop-based survey, the so far best AR modeling configuration is identified, which can serve for benchmarks and implementations.},
  language  = {en}
}
@inproceedings{GrumBenderGronauetal.2020,
  author    = {Grum, Marcus and Bender, Benedict and Gronau, Norbert and Alfa, Attahiru S.},
  title     = {Efficient task realizations in networked production infrastructures},
  series = {Proceedings of the Conference on Production Systems and Logistics},
  booktitle = {Proceedings of the Conference on Production Systems and Logistics},
  publisher = {publish-Ing.},
  address   = {Hannover},
  doi       = {10.15488/9682},
  pages     = {397 -- 407},
  year      = {2020},
  abstract  = {As Industry 4.0 infrastructures are seen as highly evolutionary environment with volatile, and time-dependent workloads for analytical tasks, particularly the optimal dimensioning of IT hardware is a challenge for decision makers because the digital processing of these tasks can be decoupled from their physical place of origin. Flexible architecture models to allocate tasks efficiently with regard to multi-facet aspects and a predefined set of local systems and external cloud services have been proven in small example scenarios. This paper provides a benchmark of existing task realization strategies, composed of (1) task distribution and (2) task prioritization in a real-world scenario simulation. It identifies heuristics as superior strategies.},
  language  = {en}
}
@article{GrumSultanowFriedmannetal.2021,
  author    = {Grum, Marcus and Sultanow, Eldar and Friedmann, Daniel and Ulrich, Andre and Gronau, Norbert},
  title     = {Tools des Maschinellen Lernens},
  publisher = {Gito},
  address   = {Berlin},
  isbn      = {978-3-95545-380-0},
  doi       = {10.30844/grum_2020},
  pages     = {143},
  year      = {2021},
  abstract  = {K{\"u}nstliche Intelligenz ist in aller Munde. Immer mehr Anwendungsbereiche werden durch die Auswertung von vorliegenden Daten mit Algorithmen und Frameworks z.B. des Maschinellen Lernens erschlossen. Dieses Buch hat das Ziel, einen {\"U}berblick {\"u}ber gegenw{\"a}rtig vorhandene L{\"o}sungen zu geben und dar{\"u}ber hinaus konkrete Hilfestellung bei der Auswahl von Algorithmen oder Tools bei spezifischen Problemstellungen zu bieten. Um diesem Anspruch gerecht zu werden, wurden 90 L{\"o}sungen mittels einer systematischen Literaturrecherche und Praxissuche identifiziert sowie anschließend klassifiziert. Mit Hilfe dieses Buches gelingt es, schnell die notwendigen Grundlagen zu verstehen, g{\"a}ngige Anwendungsgebiete zu identifizieren und den Prozess zur Auswahl eines passenden ML-Tools f{\"u}r das eigene Projekt systematisch zu meistern.},
  language  = {de}
}
@misc{GrumKoerppenKorjahnetal.2022,
  author    = {Grum, Marcus and K{\"o}rppen, Tim and Korjahn, Nicolas and Gronau, Norbert},
  title     = {Entwicklung eines KI-ERP-Indikators},
  publisher = {Center for Enterprise Research, Universit{\"a}t Potsdam},
  address   = {Potsdam},
  pages     = {27},
  year      = {2022},
  abstract  = {K{\"u}nstliche Intelligenz (KI) gewinnt in zahlreichen Branchen rasant an Bedeutung und wird zunehmend auch in Enterprise Resource Planning (ERP)-Systemen als Anwendungsbereich erschlossen. Die Idee, dass Maschinen die kognitiven F{\"a}higkeiten des Menschen imitieren k{\"o}nnen, indem Wissen durch Lernen auf Basis von Beispielen in Daten, Informationen und Erfahrungen generiert wird, ist heute ein Schl{\"u}sselelement der digitalen Transformation. Jedoch charakterisiert der Einsatz von KI in ERP-System einen hohen Komplexit{\"a}tsgrad, da die KI als Querschnittstechnologie zu verstehen ist, welche in unterschiedlichen Unternehmensbereichen zum Einsatz kommen kann. Auch die Anwendungsgrade k{\"o}nnen sich dabei erheblich voneinander unterscheiden. Um trotz dieser Komplexit{\"a}t den Einsatz der KI in ERP-Systemen erfassen und systembezogen vergleichen zu k{\"o}nnen, wurde im Rahmen dieser Studie ein Reifegradmodell entwickelt. Dieses bildet die Ausgangsbasis zur Ermittlung der KI-Reife in ERP-Systemen und grenzt dabei die folgenden vier KI- bzw. systembezogenen Ebenen voneinander ab: 1) Technische M{\"o}glichkeiten, 2) Datenreife, 3) Funktionsreife und 4) Erkl{\"a}rf{\"a}higkeit des Systems.},
  language  = {de}
}
@misc{BenderGrumGronauetal.2019,
  author    = {Bender, Benedict and Grum, Marcus and Gronau, Norbert and Alfa, Attahiru and Maharaj, B. T.},
  title     = {Design of a worldwide simulation system for distributed cyber-physical production networks},
  series = {2019 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC)},
  journal   = {2019 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC)},
  publisher = {IEEE},
  address   = {New York},
  isbn      = {978-1-7281-3401-7},
  issn      = {2334-315X},
  doi       = {10.1109/ICE.2019.8792609},
  pages     = {7},
  year      = {2019},
  abstract  = {Modern production infrastructures of globally operating companies usually consist of multiple distributed production sites. While the organization of individual sites consisting of Industry 4.0 components itself is demanding, new questions regarding the organization and allocation of resources emerge considering the total production network. In an attempt to face the challenge of efficient distribution and processing both within and across sites, we aim to provide a hybrid simulation approach as a first step towards optimization. Using hybrid simulation allows us to include real and simulated concepts and thereby benchmark different approaches with reasonable effort. A simulation concept is conceptualized and demonstrated qualitatively using a global multi-site example.},
  language  = {en}
}