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 - 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 - CHAP A1 - Bender, Benedict A1 - Gronau, Norbert T1 - Coring on Digital Platforms BT - Fundamentals and Examples from the Mobile Device Sector T2 - Proceedings of the 38th International Conference on Information Systems (ICIS) N2 - Today’s mobile devices are part of powerful business ecosystems, which usually involve digital platforms. To better understand the complex phenomenon of coring and related dynamics, this paper presents a case study comparing iMessage as part of Apple’s iOS and WhatsApp. Specifically, it investigates activities regarding platform coring, as the integration of several functionalities provided by third-party applications in the platform core. The paper makes three contributions. First, a systematization of coring activities is developed. Coring modes are differentiated by the amount of coring and application maintenance. Second, the case study revealed that the phenomenon of platform coring is present on digital platforms for mobile devices. Third, the fundamentals of coring are discussed as a first step towards theoretical development. Even though coring constitutes a potential threat for third-party developers regarding their functional differentiation, an idea of what a beneficial partnership incorporating coring activities could look like is developed here. KW - Coring KW - Digital Platforms KW - Digital Marketplaces KW - Mobile Software Ecosystems Y1 - 2017 UR - https://aisel.aisnet.org/icis2017/DigitalPlatforms/Presentations/6/ SN - 978-0-9966831-5-9 IS - 8 SP - 5256 EP - 5274 PB - Association for Information Systems (AIS) CY - Atlanta ER - TY - CHAP A1 - Glaschke, Christian A1 - Gronau, Norbert A1 - Bender, Benedict T1 - Cross-System Process Mining using RFID Technology T2 - Proceedings of the Sixth International Symposium on Business Modeling and Software Design - BMSD N2 - In times of digitalization, the collection and modeling of business processes is still a challenge for companies. The demand for trustworthy process models that reflect the actual execution steps therefore increases. The respective kinds of processes significantly determine both, business process analysis and the conception of future target processes and they are the starting point for any kind of change initiatives. Existing approaches to model as-is processes, like process mining, are exclusively focused on reconstruction. Therefore, transactional protocols and limited data from a single application system are used. Heterogeneous application landscapes and business processes that are executed across multiple application systems, on the contrary, are one of the main challenges in process mining research. Using RFID technology is hence one approach to close the existing gap between different application systems. This paper focuses on methods for data collection from real world objects via RFID technology and possible combinations with application data (process mining) in order to realize a cross system mining approach. KW - Process Mining KW - RFID KW - Production KW - Cross-System Y1 - 2016 SN - 978-989-758-190-8 U6 - https://doi.org/10.5220/0006223501790186 SP - 179 EP - 186 PB - SCITEPRESS - Science and Technology Publications CY - Setúbal ER - TY - CHAP A1 - Gronau, Norbert A1 - Weber, Edzard A1 - Heinze, Priscilla T1 - Cyclic process model transformation T2 - Proceedings of the 12th European Conference on Knowledge Management N2 - Process analysis usually focuses only on single and selected processes. It is either existent processes that are recorded and analysed or reference processes that are implemented. So far no evident effort has been put into generalising specific process aspects into patterns and comparing those patterns with regard to their efficiency and effectiveness. This article focuses on the combination of dynamic and holistic analytical elements in enterprise architectures. Our goal is to outline an approach to analyse the development of business processes in a cyclical matter and demonstrate this approach based on an existent modelling language. We want to show that organisational learning can derive from the systematic analysis of past and existent processes from which patterns of successful problem solving can be deducted. Y1 - 2011 SN - 978-1-908272-09-6 IS - 2 SP - 349 EP - 359 PB - Academic Conferences Ltd. CY - Reading ER - TY - CHAP A1 - Panzer, Marcel A1 - Bender, Benedict A1 - Gronau, Norbert T1 - Deep reinforcement learning in production planning and control BT - A systematic literature review T2 - Proceedings of the Conference on Production Systems and Logistics N2 - Increasingly fast development cycles and individualized products pose major challenges for today's smart production systems in times of industry 4.0. The systems must be flexible and continuously adapt to changing conditions while still guaranteeing high throughputs and robustness against external disruptions. Deep rein- forcement learning (RL) algorithms, which already reached impressive success with Google DeepMind's AlphaGo, are increasingly transferred to production systems to meet related requirements. Unlike supervised and unsupervised machine learning techniques, deep RL algorithms learn based on recently collected sensor- and process-data in direct interaction with the environment and are able to perform decisions in real-time. As such, deep RL algorithms seem promising given their potential to provide decision support in complex environments, as production systems, and simultaneously adapt to changing circumstances. While different use-cases for deep RL emerged, a structured overview and integration of findings on their application are missing. To address this gap, this contribution provides a systematic literature review of existing deep RL applications in the field of production planning and control as well as production logistics. From a performance perspective, it became evident that deep RL can beat heuristics significantly in their overall performance and provides superior solutions to various industrial use-cases. Nevertheless, safety and reliability concerns must be overcome before the widespread use of deep RL is possible which presumes more intensive testing of deep RL in real world applications besides the already ongoing intensive simulations. KW - deep reinforcement learning KW - machine learning KW - production planning KW - production control KW - systematic literature review Y1 - 2021 U6 - https://doi.org/10.15488/11238 SN - 2701-6277 SP - 535 EP - 545 PB - publish-Ing. CY - Hannover 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 - TY - CHAP A1 - Rojahn, Marcel A1 - Gronau, Norbert T1 - Digital platform concepts for manufacturing companies BT - a review T2 - 10th International Conference on Future Internet of Things and Cloud (FiCloud) N2 - Digital Platforms (DPs) has established themself in recent years as a central concept of the Information Technology Science. Due to the great diversity of digital platform concepts, clear definitions are still required. Furthermore, DPs are subject to dynamic changes from internal and external factors, which pose challenges for digital platform operators, developers and customers. Which current digital platform research directions should be taken to address these challenges remains open so far. The following paper aims to contribute to this by outlining a systematic literature review (SLR) on digital platform concepts in the context of the Industrial Internet of Things (IIoT) for manufacturing companies and provides a basis for (1) a selection of definitions of current digital platform and ecosystem concepts and (2) a selection of current digital platform research directions. These directions are diverted into (a) occurrence of digital platforms, (b) emergence of digital platforms, (c) evaluation of digital platforms, (d) development of digital platforms, and (e) selection of digital platforms. Y1 - 2023 SN - 979-8-3503-1635-3 U6 - https://doi.org/10.1109/FiCloud58648.2023.00030 SP - 149 EP - 158 PB - IEEE CY - [Erscheinungsort nicht ermittelbar] 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 - 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 -