TY - CHAP A1 - Rojahn, Marcel A1 - Ambros, Maximilian A1 - Biru, Tibebu A1 - Krallmann, Hermann A1 - Gronau, Norbert A1 - Grum, Marcus ED - Rutkowski, Leszek ED - Scherer, Rafał ED - Korytkowski, Marcin ED - Pedrycz, Witold ED - Tadeusiewicz, Ryszard ED - Zurada, Jacek M. T1 - Adequate basis for the data-driven and machine-learning-based identification T2 - Artificial intelligence and soft computing N2 - 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. KW - data mining KW - knowledge engineering KW - various applications Y1 - 2023 SN - 978-3-031-42504-2 SN - 978-3-031-42505-9 U6 - https://doi.org/10.1007/978-3-031-42505-9_48 SP - 570 EP - 588 PB - Springer CY - Cham ER - TY - JOUR A1 - Klippert, Monika A1 - Stolpmann, Robert A1 - Grum, Marcus A1 - Thim, Christof A1 - Gronau, Norbert A1 - Albers, Albert T1 - Knowledge transfer quality improvement BT - the quality enhancement of knowledge transfers in product engineering JF - Procedia CIRP N2 - Developing a new product generation requires the transfer of knowledge among various knowledge carriers. Several factors influence knowledge transfer, e.g., the complexity of engineering tasks or the competence of employees, which can decrease the efficiency and effectiveness of knowledge transfers in product engineering. Hence, improving those knowledge transfers obtains great potential, especially against the backdrop of experienced employees leaving the company due to retirement, so far, research results show, that the knowledge transfer velocity can be raised by following the Knowledge Transfer Velocity Model and implementing so-called interventions in a product engineering context. In most cases, the implemented interventions have a positive effect on knowledge transfer speed improvement. In addition to that, initial theoretical findings describe factors influencing the quality of knowledge transfers and outline a setting to empirically investigate how the quality can be improved by introducing a general description of knowledge transfer reference situations and principles to measure the quality of knowledge artifacts. To assess the quality of knowledge transfers in a product engineering context, the Knowledge Transfer Quality Model (KTQM) is created, which serves as a basis to develop and implement quality-dependent interventions for different knowledge transfer situations. As a result, this paper introduces the specifications of eight situation-adequate interventions to improve the quality of knowledge transfers in product engineering following an intervention template. Those interventions are intended to be implemented in an industrial setting to measure the quality of knowledge transfers and validate their effect. KW - knowledge transfer KW - product generation engineering KW - improvement KW - quality KW - intervention Y1 - 2023 U6 - https://doi.org/10.1016/j.procir.2023.02.171 SN - 2212-8271 VL - 119 SP - 919 EP - 925 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Thim, Christof A1 - Gronau, Norbert A1 - Haase, Jennifer A1 - Grum, Marcus A1 - Schüffler, Arnulf A1 - Roling, Wiebke A1 - Kluge, Annette ED - Shishkov, Boris T1 - Modeling change in business processes T2 - Business modeling and software design N2 - Business processes are regularly modified either to capture requirements from the organization’s environment or due to internal optimization and restructuring. Implementing the changes into the individual work routines is aided by change management tools. These tools aim at the acceptance of the process by and empowerment of the process executor. They cover a wide range of general factors and seldom accurately address the changes in task execution and sequence. Furthermore, change is only framed as a learning activity, while most obstacles to change arise from the inability to unlearn or forget behavioural patterns one is acquainted with. Therefore, this paper aims to develop and demonstrate a notation to capture changes in business processes and identify elements that are likely to present obstacles during change. It connects existing research from changes in work routines and psychological insights from unlearning and intentional forgetting to the BPM domain. The results contribute to more transparency in business process models regarding knowledge changes. They provide better means to understand the dynamics and barriers of change processes. KW - intentional forgetting KW - routines KW - business processes KW - unlearning Y1 - 2023 SN - 978-3-031-36756-4 SN - 978-3-031-36757-1 U6 - https://doi.org/10.1007/978-3-031-36757-1_1 SP - 3 EP - 17 PB - Springer Nature CY - Cham ER - TY - CHAP A1 - Grum, Marcus A1 - Thim, Christof A1 - Roling, Wiebke A1 - Schüffler, Arnulf A1 - Kluge, Annette A1 - Gronau, Norbert ED - Masrour, Tawfik ED - El Hassani, Ibtissam ED - Barka, Noureddine T1 - AI case-based reasoning for artificial neural networks T2 - Artificial intelligence and industrial applications N2 - Faced with the triad of time-cost-quality, the realization of production tasks under economic conditions is not trivial. Since the number of Artificial-Intelligence-(AI)-based applications in business processes is increasing more and more nowadays, the efficient design of AI cases for production processes as well as their target-oriented improvement is essential, so that production outcomes satisfy high quality criteria and economic requirements. Both challenge production management and data scientists, aiming to assign ideal manifestations of artificial neural networks (ANNs) to a certain task. Faced with new attempts of ANN-based production process improvements [8], this paper continues research about the optimal creation, provision and utilization of ANNs. Moreover, it presents a mechanism for AI case-based reasoning for ANNs. Experiments clarify continuously improving ANN knowledge bases by this mechanism empirically. Its proof-of-concept is demonstrated by the example of four production simulation scenarios, which cover the most relevant use cases and will be the basis for examining AI cases on a quantitative level. KW - case-based reasoning KW - neural networks KW - industry 4.0 Y1 - 2023 SN - 978-3-031-43523-2 SN - 978-3-031-43524-9 U6 - https://doi.org/10.1007/978-3-031-43524-9_2 VL - 771 SP - 17 EP - 35 PB - Springer CY - Cham ER - TY - GEN A1 - Grum, Marcus A1 - Körppen, Tim A1 - Korjahn, Nicolas A1 - Gronau, Norbert T1 - Entwicklung eines KI-ERP-Indikators BT - Evaluation der Potenzialerschließung von Künstlicher Intelligenz in Enterprise-Resource-Planning-Systemen N2 - Künstliche 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 Fähigkeiten des Menschen imitieren können, indem Wissen durch Lernen auf Basis von Beispielen in Daten, Informationen und Erfahrungen generiert wird, ist heute ein Schlüsselelement der digitalen Transformation. Jedoch charakterisiert der Einsatz von KI in ERP-System einen hohen Komplexitätsgrad, da die KI als Querschnittstechnologie zu verstehen ist, welche in unterschiedlichen Unternehmensbereichen zum Einsatz kommen kann. Auch die Anwendungsgrade können sich dabei erheblich voneinander unterscheiden. Um trotz dieser Komplexität den Einsatz der KI in ERP-Systemen erfassen und systembezogen vergleichen zu können, 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 Möglichkeiten, 2) Datenreife, 3) Funktionsreife und 4) Erklärfähigkeit des Systems. KW - Künstliche Intelligenz KW - Enterprise-Resource-Planning KW - KI-ERP-Indikator Y1 - 2022 UR - https://lswi.de/assets/downloads/publikationen/110/Grum-Entwicklung-eines-KI-ERP-Indikators--.pdf PB - Center for Enterprise Research, Universität Potsdam CY - Potsdam ER - TY - CHAP A1 - Grum, Marcus A1 - Kotarski, David A1 - Ambros, Maximilian A1 - Biru, Tibebu A1 - Krallmann, Hermann A1 - Gronau, Norbert ED - Shishkov, Boris T1 - Managing knowledge of intelligent systems BT - the design of a chatbot using domain-specific knowledge T2 - Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5–7, 2021, Proceedings N2 - 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. KW - domain-specific language KW - morphologic box KW - explainability Y1 - 2021 SN - 978-3-030-79975-5 SN - 978-3-030-79976-2 U6 - https://doi.org/10.1007/978-3-030-79976-2_5 VL - 422 SP - 78 EP - 96 PB - Springer International Publishing CY - Cham ER - TY - CHAP A1 - Grum, Marcus A1 - Gronau, Norbert ED - Shishkov, Boris T1 - Quantification of knowledge transfers BT - the design of an experiment setting for the examination of knowledge transfers T2 - Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5–7, 2021, Proceedings N2 - 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. KW - knowledge management KW - knowledge transfer KW - conversion KW - empirical examination KW - experiment Y1 - 2021 SN - 978-3-030-79975-5 SN - 978-3-030-79976-2 U6 - https://doi.org/10.1007/978-3-030-79976-2_13 VL - 422 SP - 224 EP - 242 PB - Springer International Publishing CY - Cham ER - TY - JOUR A1 - Grum, Marcus A1 - Sultanow, Eldar A1 - Friedmann, Daniel A1 - Ulrich, Andre A1 - Gronau, Norbert T1 - Tools des Maschinellen Lernens BT - Marktstudie, Anwendungsbereiche & Lösungen der Künstlichen Intelligenz N2 - Künstliche 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 Überblick über gegenwärtig vorhandene Lösungen zu geben und darüber hinaus konkrete Hilfestellung bei der Auswahl von Algorithmen oder Tools bei spezifischen Problemstellungen zu bieten. Um diesem Anspruch gerecht zu werden, wurden 90 Lösungen mittels einer systematischen Literaturrecherche und Praxissuche identifiziert sowie anschließend klassifiziert. Mit Hilfe dieses Buches gelingt es, schnell die notwendigen Grundlagen zu verstehen, gängige Anwendungsgebiete zu identifizieren und den Prozess zur Auswahl eines passenden ML-Tools für das eigene Projekt systematisch zu meistern. Y1 - 2021 SN - 978-3-95545-380-0 SN - 978-3-95545-318-7 U6 - https://doi.org/10.30844/grum_2020 PB - Gito CY - Berlin ER - TY - JOUR A1 - Grum, Marcus A1 - Hiessl, Werner A1 - Maresch, Karl A1 - Gronau, Norbert T1 - Design of a neuronal training modeling language BT - exemplified with the AI-based dynamic GUI adaption JF - AIS-Transactions on enterprise systems N2 - As the complexity of learning task requirements, computer infrastruc- tures and knowledge acquisition for artificial neuronal networks (ANN) is in- creasing, it is challenging to talk about ANN without creating misunderstandings. An efficient, transparent and failure-free design of learning tasks by models is not supported by any tool at all. For this purpose, particular the consideration of data, information and knowledge on the base of an integration with knowledge- intensive business process models and a process-oriented knowledge manage- ment are attractive. With the aim of making the design of learning tasks express- ible by models, this paper proposes a graphical modeling language called Neu- ronal Training Modeling Language (NTML), which allows the repetitive use of learning designs. An example ANN project of AI-based dynamic GUI adaptation exemplifies its use as a first demonstration. KW - AI and business informatics KW - development of AI-based systems KW - AI-based decision support system KW - cooperative AI (human-in-the-loop) KW - process-oriented knowledge acquisition KW - modeling language Y1 - 2021 UR - https://www.aes-journal.com/index.php/ais-tes/article/view/20/18 U6 - https://doi.org/10.30844/aistes.v5i1.20 SN - 1867-7134 VL - 5 IS - 1 PB - GITO-Publ., Verl. für Industrielle Informationstechnik und Organisation CY - Berlin ER - TY - CHAP A1 - Thim, Christof A1 - Grum, Marcus A1 - Schüffler, Arnulf A1 - Roling, Wiebke A1 - Kluge, Annette A1 - Gronau, Norbert ED - Andersen, Ann-Louise ED - Andersen, Rasmus ED - Brunoe, Thomas Ditlev ED - Larsen, Maria Stoettrup Schioenning ED - Nielsen, Kjeld ED - Napoleone, Alessia ED - Kjeldgaard, Stefan T1 - A concept for a distributed Interchangeable knowledge base in CPPS T2 - Towards sustainable customization: cridging smart products and manufacturing systems N2 - As AI technology is increasingly used in production systems, different approaches have emerged from highly decentralized small-scale AI at the edge level to centralized, cloud-based services used for higher-order optimizations. Each direction has disadvantages ranging from the lack of computational power at the edge level to the reliance on stable network connections with the centralized approach. Thus, a hybrid approach with centralized and decentralized components that possess specific abilities and interact is preferred. However, the distribution of AI capabilities leads to problems in self-adapting learning systems, as knowledgebases can diverge when no central coordination is present. Edge components will specialize in distinctive patterns (overlearn), which hampers their adaptability for different cases. Therefore, this paper aims to present a concept for a distributed interchangeable knowledge base in CPPS. The approach is based on various AI components and concepts for each participating node. A service-oriented infrastructure allows a decentralized, loosely coupled architecture of the CPPS. By exchanging knowledge bases between nodes, the overall system should become more adaptive, as each node can “forget” their present specialization. KW - learning KW - distributed knowledge base KW - artificial intelligence KW - CPPS Y1 - 2021 SN - 978-3-030-90699-3 SN - 978-3-030-90702-0 SN - 978-3-030-90700-6 U6 - https://doi.org/10.1007/978-3-030-90700-6_35 SP - 314 EP - 321 PB - Springer CY - Cham ER - TY - CHAP A1 - Grum, Marcus A1 - Thim, Christof A1 - Gronau, Norbert ED - Andersen, Ann-Louise ED - Andersen, Rasmus ED - Brunoe, Thomas Ditlev ED - Larsen, Maria Stoettrup Schioenning ED - Nielsen, Kjeld ED - Napoleone, Alessia ED - Kjeldgaard, Stefan T1 - Aiming for knowledge-transfer-optimizing intelligent cyber-physical systems T2 - Towards sustainable customization : cridging smart products and manufacturing systems N2 - Since more and more production tasks are enabled by Industry 4.0 techniques, the number of knowledge-intensive production tasks increases as trivial tasks can be automated and only non-trivial tasks demand human-machine interactions. With this, challenges regarding the competence of production workers, the complexity of tasks and stickiness of required knowledge occur [1]. Furthermore, workers experience time pressure which can lead to a decrease in output quality. Cyber-Physical Systems (CPS) have the potential to assist workers in knowledge-intensive work grounded on quantitative insights about knowledge transfer activities [2]. By providing contextual and situational awareness as well as complex classification and selection algorithms, CPS are able to ease knowledge transfer in a way that production time and quality is improved significantly. CPS have only been used for direct production and process optimization, knowledge transfers have only been regarded in assistance systems with little contextual awareness. Embedding production and knowledge transfer optimization thus show potential for further improvements. This contribution outlines the requirements and a framework to design these systems. It accounts for the relevant factors. KW - smart automation KW - smart production KW - human-machine-interaction Y1 - 2021 SN - 978-3-030-90699-3 SN - 978-3-030-90700-6 SN - 978-3-030-90702-0 U6 - https://doi.org/10.1007/978-3-030-90700-6_16 SP - 149 EP - 157 PB - Springer CY - Cham ER - TY - CHAP A1 - Grum, Marcus A1 - Klippert, Monika A1 - Albers, Albert A1 - Gronau, Norbert A1 - Thim, Christof T1 - Examining the quality of knowledge transfers BT - the draft of an empirical research T2 - Proceedings of the Design Society N2 - Already successfully used products or designs, past projects or our own experiences can be the basis for the development of new products. As reference products or existing knowledge, it is reused in the development process and across generations of products. Since further, products are developed in cooperation, the development of new product generations is characterized by knowledge-intensive processes in which information and knowledge are exchanged between different kinds of knowledge carriers. The particular knowledge transfer here describes the identification of knowledge, its transmission from the knowledge carrier to the knowledge receiver, and its application by the knowledge receiver, which includes embodied knowledge of physical products. Initial empirical findings of the quantitative effects regarding the speed of knowledge transfers already have been examined. However, the factors influencing the quality of knowledge transfer to increase the efficiency and effectiveness of knowledge transfer in product development have not yet been examined empirically. Therefore, this paper prepares an experimental setting for the empirical investigation of the quality of knowledge transfers. KW - knowledge management KW - new product development KW - evaluation Y1 - 2021 U6 - https://doi.org/10.1017/pds.2021.404 SN - 2732-527X VL - 1 SP - 1431 EP - 1440 PB - Cambridge University Press CY - Cambridge ER - TY - CHAP A1 - Grum, Marcus A1 - Gronau, Norbert ED - Shishkov, Boris T1 - Adaptable knowledge-driven information systems improving knowledge transfers BT - design of context-sensitive, AR-enabled furniture assemblies T2 - Business modeling and software design : 10th International Symposium, BMSD 2020, Berlin, Germany, July 6-8, 2020, Proceedings N2 - 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. KW - augmented reality KW - knowledge transfers KW - empirical studies KW - context-aware computing KW - adaptable software systems KW - business process improvement Y1 - 2020 SN - 978-3-030-52305-3 SN - 978-3-030-52306-0 U6 - https://doi.org/10.1007/978-3-030-52306-0_13 VL - 391 SP - 202 EP - 220 PB - Springer International Publishing CY - Cham ER - TY - CHAP A1 - Grum, Marcus A1 - Bender, Benedict A1 - Gronau, Norbert A1 - Alfa, Attahiru S. T1 - Efficient task realizations in networked production infrastructures T2 - Proceedings of the Conference on Production Systems and Logistics N2 - 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. KW - Industry 4.0 KW - CPS KW - Decentral Decision Making KW - Industrial Analytics KW - Case Study Y1 - 2020 U6 - https://doi.org/10.15488/9682 SP - 397 EP - 407 PB - publish-Ing. CY - Hannover ER - TY - CHAP A1 - Grum, Marcus A1 - Blunk, Oliver A1 - Rojahn, Marcel A1 - Fettke, Peter A1 - Gronau, Norbert T1 - Research challenges of knowledge modelling and the outline of a research agenda T2 - Knowledge in digital age : IFKAD 2020 KW - knowledge management KW - process modelling KW - research challenges Y1 - 2020 SN - 978-88-96687-13-0 SN - 2280-787X PB - The Arts of Business Institute CY - Matera, Italy ER - TY - GEN A1 - Bender, Benedict A1 - Grum, Marcus A1 - Gronau, Norbert A1 - Alfa, Attahiru A1 - Maharaj, B. T. T1 - Design of a worldwide simulation system for distributed cyber-physical production networks T2 - 2019 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) N2 - 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. KW - production networks KW - geographical distribution KW - task realization strategies KW - Industry 4.0 KW - simulation KW - evaluation Y1 - 2019 SN - 978-1-7281-3401-7 SN - 978-1-7281-3402-4 U6 - https://doi.org/10.1109/ICE.2019.8792609 SN - 2334-315X PB - IEEE CY - New York ER - TY - CHAP A1 - Grum, Marcus A1 - Rapp, Simon A1 - Gronau, Norbert A1 - Albers, Albert ED - Shishkov, Boris T1 - Accelerating knowledge BT - the speed optimization of knowledge transfers T2 - Business modeling and software design N2 - As knowledge-intensive processes are often carried out in teams and demand for knowledge transfers among various knowledge carriers, any optimization in regard to the acceleration of knowledge transfers obtains a great economic potential. Exemplified with product development projects, knowledge transfers focus on knowledge acquired in former situations and product generations. An adjustment in the manifestation of knowledge transfers in its concrete situation, here called intervention, therefore can directly be connected to the adequate speed optimization of knowledge-intensive process steps. This contribution presents the specification of seven concrete interventions following an intervention template. Further, it describes the design and results of a workshop with experts as a descriptive study. The workshop was used to assess the practical relevance of interventions designed as well as the identification of practical success factors and barriers of their implementation. KW - knowledge transfers KW - business process optimization KW - interventions KW - product development KW - product generation engineering KW - empirical evaluation Y1 - 2019 SN - 978-3-030-24853-6 SN - 978-3-030-24854-3 U6 - https://doi.org/10.1007/978-3-030-24854-3_7 VL - 356 SP - 95 EP - 113 PB - Springer CY - Cham ER - TY - GEN A1 - Grum, Marcus A1 - Gronau, Norbert T1 - Process modeling within augmented reality BT - the bidirectional interplay of two worlds T2 - Business Modeling and Software Design, BMSD 2018 N2 - 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. KW - Augmented reality KW - Process modeling KW - Simulation process building KW - Generalized knowledge constructin axiom KW - Meta-model KW - Use cases Morphologic box KW - Industry 4.0 KW - CPS KW - CPPS KW - Internet of things Y1 - 2018 SN - 978-3-319-94214-8 SN - 978-3-319-94213-1 U6 - https://doi.org/10.1007/978-3-319-94214-8_7 SN - 1865-1348 VL - 319 SP - 99 EP - 115 PB - Springer CY - Berlin ER - TY - JOUR A1 - Grum, Marcus A1 - Bender, Benedict A1 - Alfa, A. S. A1 - Gronau, Norbert T1 - A decision maxim for efficient task realization within analytical network infrastructures JF - Decision support systems : DSS ; the international journal N2 - Faced with the increasing needs of companies, optimal dimensioning of IT hardware is becoming challenging for decision makers. In terms of analytical infrastructures, a highly evolutionary environment causes volatile, time dependent workloads in its components, and intelligent, flexible task distribution between local systems and cloud services is attractive. With the aim of developing a flexible and efficient design for analytical infrastructures, this paper proposes a flexible architecture model, which allocates tasks following a machine-specific decision heuristic. A simulation benchmarks this system with existing strategies and identifies the new decision maxim as superior in a first scenario-based simulation. KW - Analytics KW - Architecture concepts KW - Cyber-physical systems KW - Internet of things KW - Task realization strategies KW - Simulation Y1 - 2018 U6 - https://doi.org/10.1016/j.dss.2018.06.005 SN - 0167-9236 SN - 1873-5797 VL - 112 SP - 48 EP - 59 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Gronau, Norbert A1 - Grum, Marcus A1 - Bender, Benedict T1 - Determining the optimal level of autonomy in cyber-physical production systems T2 - IEEE 14th International Conference on Industrial Informatics (INDIN) N2 - 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. KW - cyber-physical systems KW - hybrid simulation KW - Internet of Things KW - manufacturing systems KW - production engineering computing KW - cyber-physical production systems Y1 - 2017 U6 - https://doi.org/10.1109/INDIN.2016.7819367 SP - 1293 EP - 1299 PB - IEEE CY - New York ER -