TY - GEN A1 - Berneis, Moritz A1 - Winkler, Herwig T1 - Fallstudie zur Fabrikplanung im Greenfield T2 - WiSt Wirtschaftswissenschaftliches Studium Y1 - 2022 UR - https://rsw.beck.de/zeitschriften/wist/das-aktuelle-heft SN - 0340-1650 VL - 51 IS - 1 SP - 58 EP - 63 ER - TY - GEN A1 - Kuteyi, Damilola A1 - Winkler, Herwig T1 - Logistics Challenges in Sub-Saharan Africa and Opportunities for Digitalization T2 - Sustainability N2 - Improvements in logistics performance are considered to be a significant driver of economic growth. Digitalization in logistics, which includes enhanced tracking systems, digitized flows of information, artificial intelligence and automation, has further enabled globalized trade. However, Sub-Saharan African (SSA) economies, majorly export-commodity-dependent, still lag behind, grappling with inadequate policy frameworks, huge infrastructure deficits and trade barriers which result in a broken supply chain that negatively impacts growth. This study aims to investigate existing challenges in Sub-Saharan Africa, the potentials for the region to leapfrog traditional supply chain practices and adopt digital technologies, by evaluating previous findings in a systematic manner and augmenting these findings through semi-structured interviews. Systematic literature analysis conducted on published academic literature within a specific period and based on predefined criteria resulted in 287 articles being used for the final analysis. The most common logistics challenges and potential solutions have been identified. Semi-structured interviews with logistics service providers in the region have been conducted to establish the findings highlighted in the systematic literature review. The interviews showed that human capital training is a major factor when adopting digital technologies, and a focus on infrastructure investments, regulatory and institutional framework improvements will boost economic growth in Sub-Saharan Africa. KW - digitalization KW - industry 4.0 KW - logistics KW - sub-Saharan Africa Y1 - 2022 UR - https://www.mdpi.com/2071-1050/14/4/2399/htm U6 - https://doi.org/10.3390/su14042399 SN - 2071-1050 VL - 14 IS - 4 ER - TY - GEN A1 - Stockmann, Christian A1 - Winkler, Herwig T1 - Robustness of production systems: Evidence from the German manufacturing industry T2 - Journal of Engineering and Technology Management Y1 - 2022 UR - https://www.sciencedirect.com/science/article/pii/S0923474822000029 U6 - https://doi.org/10.1016/j.jengtecman.2022.101672 SN - 0923-4748 VL - Vol. 63 ER - TY - CHAP A1 - Jeglinsky, Vinzenz A1 - Winkler, Herwig ED - Baumann, Sabine T1 - Improved decisions for digitalization projects in digital business ecosystems T2 - Handbook on Digital Business Ecosystems. Strategies, Platforms, Technologies, Governance and Societal Challenges Y1 - 2022 UR - https://www.elgaronline.com/view/edcoll/9781839107184/9781839107184.00028.xml SN - 978-1-83910-719-1 SN - 978-1-83910-718-4 U6 - https://doi.org/10.4337/9781839107191.00028 SP - 307 EP - 326 PB - Edward Elgar Publishing Ltd (Verlag) CY - Chaltenham ER - TY - CHAP A1 - Winkler, Herwig A1 - Mielke, Jonas ED - Roth, Stefan ED - Corsten, Hans T1 - Adaptive Assistenzsysteme zur Entscheidungsunterstützung im Produktionsumfeld T2 - Handbuch Digitalisierung Y1 - 2022 SN - 978-3-8006-6562-4 SP - 651 EP - 672 PB - Vahlen CY - München ER - TY - GEN A1 - Berneis, Moritz A1 - Winkler, Herwig T1 - Fallstudie zur Fabrikplanung im Greenfield. Teil 2: Lösungshinweise T2 - WiSt Wirtschaftswissenschaftliches Studium Y1 - 2022 U6 - https://doi.org/10.15358/0340-1650-2022-2-3-63 SN - 0340-1650 VL - 51 IS - 2-3 SP - 63 EP - 71 ER - TY - GEN A1 - Küpper, Christoph A1 - Rösch, Janina J. A1 - Winkler, Herwig T1 - Empirical findings for the usage of 5G as a basis for real time locating systems (RTLS) in the automotive industry T2 - Procedia CIRP Y1 - 2022 U6 - https://doi.org/10.1016/j.procir.2022.05.146 SN - 2212-8271 VL - 107 SP - 1287 EP - 1292 ER - TY - GEN A1 - Hayat, Mubashir A1 - Winkler, Herwig T1 - From Traditional Product Lifecycle Management Systems to Blockchain-Based Platforms T2 - Logistics N2 - Abstract: Background: Several product lifecycle management systems (PLMs) have been implemented in the industrial sector for managing the data of the product from the design up to the disposal or recycling stage. However, these PLMs face certain challenges in managing the complex and decentralized product lifecycles. Methods: To this aim, this work investigates the currently implemented PLMs used in industries through the exploration of various software reviews and selection websites. Accordingly, these existing PLMs are quantitatively compared and analyzed. Results: The analysis shows that most of the existing PLMs do not contain all the required features; therefore, industries integrate different software to create a full-fledged PLM system. However, this practice results in reducing the overall system efficiency. In this context, this paper assesses and recommends a blockchain-based innovative solution that overcomes the challenges of existing PLMs, hence increasing the overall system efficiency. Furthermore, this work argues, in a logical way, that the recommended blockchain-based platform provides a secure and connected infrastructure for data handling, processing, and storage at different stages of the product lifecycle. Conclusions: This work can be considered among the first to compare the currently implemented PLMs with a novel blockchainbased method. Thus, the stakeholders can utilize the outputs of this research in their analysis and decision-making processes for implementing the blockchain in their organizations. KW - product lifecycle management KW - blockchain technology KW - product development KW - decentralization KW - production system Y1 - 2022 U6 - https://doi.org/10.3390/logistics6030040 SN - 2305-6290 VL - 6 IS - 3 ER - TY - GEN A1 - Mielke, Jonas A1 - Winkler, Herwig T1 - Machine-Learning-Ansatz zur Produktionsauftragsplanung T2 - Zeitschrift für wirtschaftlichen Fabrikbetrieb N2 - Maschinelle Lernalgorithmen sind leistungsstarke Werkzeuge, um Muster und Wechselwirkungen in Datenstrukturen zu erkennen. Diese Fähigkeiten werden im Rahmen dieses Beitrags zur Optimierung der Auftragsplanung eines Produktionssystems genutzt. Dabei wird ein praxisnahes Vorgehen zur schnellen Evaluierung neuer Auftragssituationen vorgestellt. Die Datengrundlage bildet dabei ein Simulationsmodell eines realen Produktionssystems. Y1 - 2022 U6 - https://doi.org/10.1515/zwf-2022-1077 SN - 0947-0085 SN - 2511-0896 VL - 117 IS - 6 SP - 384 EP - 389 ER - TY - GEN A1 - Eberlein, Simeon A1 - Winkler, Herwig T1 - Die Blockchain Technologie (BCT) zur Verbesserung von Transaktionsprozessen in der Logistik T2 - Logistics Journal N2 - Blockchain-Technologie ist die Zukunft im Bereich sicherer Datenspeicherung und -nachverfolgung. Dabei wird diese Technologie schon lange nicht mehr nur bei Kryptowährungen eingesetzt. Viele Unternehmen arbeiten und entwickeln Lösungen, um mit der Blockchain-Technologie Prozessverbesserung in der Industrie zu realisieren. Vor allem Transaktionsprozesse in der Logistik können durch den Einsatz dieser Technologie optimiert werden. Demnach ist das Ziel dieses Artikels die Grundzüge der Blockchain Technologie darzulegen und aufzuzeigen, wo die Blockchain bereits genutzt wird und wie dadurch Logistikprozesse verbessert werden. Der Fokus dieses Beitrags liegt auf Blockchain-Anwendungen, die sich bereits in der Industrie etabliert haben. Es wird analysiert, wie diese Systeme aufgebaut sind, wie sie in der Praxis eingesetzt werden und welchen Verbesserungen mit ihnen zu realisieren sind. KW - Blockchain KW - Logistics KW - Smart Contracts KW - TradeLens KW - IBM Food Trust Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0009-14-55242 UR - https://www.logistics-journal.de/not-reviewed/2022/05/5524 SN - 1860-5923 SP - 1 EP - 11 ER - TY - GEN A1 - Hayat, Mubashir A1 - Winkler, Herwig T1 - An Analytic Hierarchy Process for Selection of Blockchain-Based Platform for Product Lifecycle Management T2 - Sustainability N2 - Blockchain technology has disrupted traditional business processes and hence gained significant attention and popularity in recent years. Consequently, a number of blockchain-based platforms are available today that offer vast applications across multiple sectors and industries. Implementing these blockchain-based platforms as an alternative to traditional product lifecycle management systems (PLMs) is one of the applications. However, before any platform is adopted, its nature, functionalities, and adaptability need to be clearly defined, evaluated, and verified. In this context, the proposed work explores the available blockchain-based platforms that can be used for the purpose of product lifecycle management. We then apply one of the multi-criteria decision-making techniques, i.e., the analytic hierarchy process (AHP), to select the best possible blockchain-based platform for PLM. As transaction speed, data privacy, and scalability are our prime concerns in PLM, we only considered the permissioned (private) blockchain platforms as available alternatives in the final selection process. Results achieved on the basis of considered criteria show that Hyperledger Fabric is the top-ranked among available alternatives to be used for PLM. Furthermore, as blockchain is a new technology, a clear comparison of the available platforms based on the performancebased metrics and key performance indicators is not completely matured and is still in the development stage. However, our proposed approach can be considered an attempt to create a procedure for evaluating blockchain-based platform implementation in any sector. KW - blockchain platforms KW - product lifecycle management KW - analytic hierarchy process industry 4.0 Y1 - 2022 U6 - https://doi.org/10.3390/su142113703 SN - 2071-1050 VL - 14 IS - 21 SP - 1 EP - 19 ER - TY - GEN A1 - Bartsch, Devis A1 - Winkler, Herwig T1 - Smart Order as a new Instrument for Production Control T2 - Changing Tides: The New Role of Resilience and Sustainability in Logistics and Supply Chain Management : Innovative Approaches for the Shift to a New Era Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:830-882.0200550 SN - 978-3-756541-95-9 SP - 149 EP - 175 PB - epubli GmbH CY - Berlin ER - TY - GEN A1 - Küpper, Christoph A1 - Rösch, Janina J. A1 - Winkler, Herwig T1 - Use of real time localization systems (RTLS) in the automotive production and the prospects of 5G – A literature review T2 - Journal for Production & Manufacturing N2 - Numerous challenges in automotive production lead to an increased need for transparency and optimization. Real-Time-Location-Systems (RTLS) is a key tool for achieving this goal. They enable intelligent and automated production processes. Existing solutions such as Ultra-Wideband, Bluetooth-Low-Energy, or Radio-Frequency-Identification have drawbacks in costs, range, or accuracy. 5G is a newly developing standard, recently including high-accuracy positioning. It is unclear, however, if 5G positioning has reached industrial maturity. This contribution aims to determine the state-of-the-art of 5G positioning by performing a systematic literature review and comparing its findings to industrial positioning requirements. 143 articles were analyzed and categorized as: the fundamentals of radio-frequency positioning, an overview of existing solutions, the state-of-the-art, and the maturity of 5G as an industrial positioning system. Results show that, theoretically, centimeter-level accuracies are pursued. However, practical tests are rarely conducted. Concluding, 5G positioning shows great potential, but industrial pilots are required to validate the theoretical characteristics. Y1 - 2022 U6 - https://doi.org/10.1080/21693277.2022.2144522 SN - 2169-3277 VL - 10 IS - 1 SP - 840 EP - 874 ER - TY - GEN A1 - Hayat, Mubashir A1 - Winkler, Herwig T1 - Exploring the Basic Features and Challenges of Traditional Product Lifecycle Management Systems T2 - 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) N2 - Product lifecycle management (PLM) is the strategic process of managing all the data of the product from the design up to its disposal or recycling stage. In product development, the PLM data is the key source for better decision-making at different stages of the product lifecycle. To this aim, several PLMs have been implemented in industrial sectors. Among these PLMs, each carries some common as well as certain distinct features and characteristics over its counterparts. However, due to the complex and decentralized nature of today’s product lifecycles, all of the currently implemented PLMs face certain challenges. In this context, various software reviews and selection websites have been explored to investigate the core functionalities as well as the challenges associated with the currently implemented PLMs. Moreover, this study analyzed that most of the existing PLMs do not contain all the required features and therefore, industries usually integrate different software to make a full-fledged PLM system. However, this practice results in reducing the overall system efficiency. Therefore, this study emphasizes that it is the need of the day to adopt a novel technology i.e. blockchain to overcome the PLM challenges and its consequences in the production industries. Y1 - 2022 SN - 978-1-6654-8687-3 SN - 978-1-6654-8688-0 U6 - https://doi.org/10.1109/IEEM55944.2022.9989978 SP - 0762 EP - 0766 ER - TY - GEN A1 - Schmid, Stefan A1 - Winkler, Herwig T1 - Hybrid Production Management System in the Context of Industry 4.0 T2 - 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) N2 - In our contribution, we consider how the Digital Twin of the production system can be combined with Artificial Intelligence methods. The result of these considerations should be a hybrid production management system, which can be used for decision support as well as for self-control. On the one hand, the Digital Twin is providing information and simulation for the optimal decision-making to the decision support system (human interaction). On the other hand, Artificial Intelligence is taking over some elements and tasks with automatic control (independent of human interaction). If the result is out of tolerance or if a random sample of the result is audited, the decision support system is validating the Artificial Intelligence result for execution (by human interaction). The hybrid production management system is to be used to control production processes and generates learning effects. The extension by methods of Artificial Intelligence opens possibilities to process and to master certain tasks independently of human interaction. The presented approach should relieve and support decision makers in production. By performing various actions automatically and achieving partial auto-control, the reactivity of linked processes can increase. The provision of decisive information favors decision quality and time-effectiveness. Y1 - 2022 SN - 978-1-6654-8687-3 SN - 978-1-6654-8688-0 U6 - https://doi.org/10.1109/IEEM55944.2022.9990000 SP - 1573 EP - 1577 ER - TY - GEN A1 - Huber, Tobias A1 - Winkler, Herwig T1 - Fehlerprognose in der Automobilmontage. Einsatz eines überwachten Lernalgorithmus zur fahrzeug- und stationsbezogenen Fehlerprognose T2 - Zeitschrift für wirtschaftlichen Fabrikbetrieb N2 - Die Automobilmontage ist aktuell von der Produktvarianz und dem Faktor Mensch geprägt, sodass diese bisher – trotz zahlreicher präventiver Maßnahmen – nicht gänzlich fehlerfrei gestaltet werden konnte. Mit dem Ziel, jene Fehler prognostizieren zu können, wurde ein Klassifikationsmodell aus dem Bereich des überwachten maschinellen Lernens trainiert und über einen dreimonatigen Zeitraum validiert. Während dieses Zeitraums konnten rund 60 Prozent der relevanten Fehler korrekt vorhergesagt werden. KW - Automobilindustrie KW - Automobilmontage KW - Maschinelles Lernen KW - Klassifikation KW - Montagefehler KW - Fehlervorhersage Y1 - 2022 U6 - https://doi.org/10.1515/zwf-2022-1042 SN - 0947-0085 SN - 0932-0482 VL - 117 IS - 4 SP - 192 EP - 199 ER -