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 - RPRT A1 - König, Mathias A1 - Winkler, Herwig T1 - Digitalisierung in der Montage mit dem Schwerpunkt des Einsatzes von Assistenzsystemen Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus4-62121 PB - Brandenburgische Technische Universität, IKMZ, Universitätsbibliothek CY - Cottbus ; Senfteberg ER - TY - GEN A1 - Schmid, Stefan A1 - Winkler, Herwig T1 - Empirical Findings on the Need of Industrial Production Management Systems in the Context of Enhanced Digitalization T2 - 2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) N2 - Since the framework conditions of manufacturing companies change dynamically, production control must react to this and be adaptive and dynamically designed. Our article addresses the need of industrial production management systems in the course of enhanced digitization. The aim is to examine the extent to which traditional systems for controlling and optimizing production systems have been supplemented by Industry 4.0 concepts. In the course of the scarcity of resources and the shortage of labor, the human factor is once again coming to the fore. Against this background, the interaction between users / humans and artificial intelligence applications will be the main focus. The result should give an indicator how this connection must be considered in the future and what should artificial intelligence do in the context of production control. The findings will be the basis for future considerations of a smart production management system, which can be used for decision support as well as for auto-control. KW - Production Management System KW - Production Control KW - Manufacturing Control KW - Production KW - Industry 4.0 Y1 - 2023 SN - 979-8-3503-2315-3 SN - 979-8-3503-2316-0 U6 - https://doi.org/10.1109/IEEM58616.2023.10406474 SP - 1406 EP - 1410 PB - IEEE CY - Piscataway, NJ ER - TY - GEN A1 - Schmid, Stefan A1 - Winkler, Herwig T1 - Empirical Findings on the Current State of Industrial Production Management Systems in the Context of Increasing Digitalization T2 - Industrial Engineering and Applications – Europe : 11th International Conference, ICIEA-EU 2024, Nice, France, January 10–12, 2024 N2 - Increasing digitization and automation are currently bringing a revolution to many processes and sectors of the economy. The industry of production in particular is undergoing a fundamental transformation through the implementation of digital and networked system elements. In production control, traditionally based on established methods and experience, advanced technologies such as artificial intelligence are opening up entirely new options. Artificial intelligence’s ability to analyze large amounts of data in real time and make instant decisions from it brings fresh perspectives to production control. Until now, there have been only a few surveys that examine the current status as well as future requirements for production control. Our research focuses on the current state of IT systems used to support decision-makers in production control. It also sheds light on the requirements needed to improve these systems, especially in light of advancing technological developments and increasing digitalization. The article is dedicated to the results and provides an overview of future expectations. Y1 - 2024 SN - 9783031581120 U6 - https://doi.org/10.1007/978-3-031-58113-7_3 SN - 1865-1348 SP - 23 EP - 34 PB - Springer Nature Switzerland CY - Cham ER - TY - GEN A1 - Berneis, Moritz A1 - Winkler, Herwig A1 - Abdelkafi, Nizar T1 - Insights from interviews with German supply chain managers: a study of supply chain transformations and emerging issues T2 - Procedia Computer Science N2 - To investigate current trends and challenges related to supply chain management in Germany, we conduct an interview-based study involving 16 experts. The study focuses on three areas: supply chain transformations, software challenges, and the adoption of emerging technologies. Supply chain transformations are influenced by increasing supply chain complexity, the growing prevalence of e-commerce, and the increased importance allocated by customers to trust and transparency. Complexity is, in part, due to disruptions caused by policy responses to the pandemic and to software challenges in terms of the adaptability of ERP systems and the cybersecurity of cloud-based solutions. This study is relevant for practitioners, as it sheds light on pressing issues in supply chain management, and for researchers as it uncovers potential topics that require further research in the future. KW - Supply Chain Management (SCM) Interviews KW - Emerging Technologies KW - German Companies KW - Digitalization KW - Trends KW - Interviews Y1 - 2024 UR - https://authors.elsevier.com/sd/article/S1877-0509(24)00106-6 U6 - https://doi.org/10.1016/j.procs.2024.01.106 SN - 1877-0509 VL - Vol. 232 SP - 1079 EP - 1088 ER - TY - CHAP A1 - Schmid, Stefan A1 - Winkler, Herwig T1 - Überlegungen zur Konzeption eines smarten Produktionsleitsystems T2 - Handbuch Industrie 4.0 und Digitale Transformation Y1 - 2024 SN - 978-3-658-36874-6 U6 - https://doi.org/10.1007/978-3-658-36874-6_17-1 PB - Springer Gabler CY - Wiesbaden ER - TY - GEN A1 - Singh, Amita A1 - Hayat, Mubashir A1 - Winkler, Herwig T1 - Deriving principles from participatory modelling to augment circularity and sustainability in EV battery lifecycle T2 - Procedia CIRP N2 - Very often circularity and sustainability go hand-in-hand. This paper is focused on understanding the key criteria of how circularity and hence sustainability can be increased in the field of electric vehicle (EV) batteries. Literature shows that only 2-5% of Lithium Ion Batteries (LIB) are recycled resulting in a huge number of not properly disposed batteries. This is harmful not only to the environment but also has adverse effects on the population of the area where the minerals are mined disturbing the triple bottom line of sustainability. Through this paper, we propose a holistic approach to tackle the problem by not only using technology but also including systematic input from different stakeholders through the lifecycle. We, thus, combine principles from participatory modelling and blockchain technology to bring transparency and accountability in the process. This approach is a step-by-step guideline for including circularity and sustainability through a continuous process of input collection from real life and validation in the real world thereby resulting in a practical and technologically adept solution yielding better results when compared to a solution developed in an isolated laboratory environment. In this paper, we discuss the guidelines for the process together with input data collection, method, process validation, and resulting document in each step. Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1016/j.procir.2024.10.120 SN - 2212-8271 IS - 130 SP - 498 EP - 503 ER - TY - GEN A1 - Schmid, Stefan A1 - Winkler, Herwig T1 - Evaluation of Future Enhancement Requirements in Production Control with Regard to the Deployment of Artificial Intelligence T2 - Proceedings of the 11th International Conference on Industrial Engineering and Applications N2 - In an age when digitization and the integration of artificial intelligence in production processes are becoming increasingly important, the selection and prioritization in the enhancement of production control functions has become a key challenge for companies. This article is dedicated to the application of the Analytic Hierarchy Process for the systematic selection and prioritization of enhancement options in production control, based on findings and data from empirical studies that examined the status quo and future needs. The focus is on structuring and weighting future enhancements. The findings are used for future considerations of a smart production management system that can be used for decision support as well as for auto-control. Y1 - 2024 SN - 978-981-9764-92-1 U6 - https://doi.org/10.1007/978-981-97-6492-1_7 SN - 2195-4356 SP - 79 EP - 91 PB - Springer Nature Singapore CY - Singapore ER - TY - GEN A1 - König, Mathias A1 - Winkler, Herwig T1 - Investigation of assistance systems in assembly in the context of digitalization: a systematic literature review T2 - Journal of Manufacturing Systems N2 - Assistance systems play a crucial role in enhancing working conditions and efficiency in industrial assembly. In the context of Industry 4.0, it is important to determine the types of assistance systems that contribute to assembly goals as well as their economic benefits. First, the significance of the topic will be introduced, and the research questions will be presented. Second, the basic technical terms will be defined, and third, the research methodology of a structured literature review (SLR) will be delineated. The fourth section presents an overview of the ergonomic and information assistance systems used in operational practice and academic test set-ups. It further explains the reasons for using assistance systems in assembly and their economic benefits, particularly in terms of reducing assembly times and errors. In the fifth section, the research perspectives of the respective publications are evaluated and summarized in both a qualitative and quantitative way. The present mixed-methods study is not generalizable due to its limitations such as a small sample size, the geographical scope of the study, type of databanks, time of publication and language of the reviewed articles, and methods of data collection. It does, however, identify potential areas for future research and provide recommendations for further investigation. KW - AssemblyIndustry 4.0Assistance systemsDigitalization Y1 - 2025 U6 - https://doi.org/10.1016/j.jmsy.2024.11.015 SN - 0278-6125 VL - 78 SP - 187 EP - 199 PB - Elsevier BV ER - TY - GEN A1 - Mielke, Jonas A1 - Winkler, Herwig T1 - A reference architecture for the application of machine leaning algorithms in production planning and control T2 - Procedia CIRP N2 - The organization of complex production systems in enterprises poses major challenges for decision-makers in production planning and control (PPC). Especially the volatility of planning scenarios and the associated demand for short-cycle adaptation of decisions requires methods that enable fast and reliable evaluations. This article presents a system architecture that addresses these challenges holistically. The system architecture uses real production data to generate scenario-based value stream simulations, which are subsequently used to train machine learning algorithms. The trained models enable a holistic evaluation of new production scenarios. Finally, decisions can be made for the real production environment based on reliable knowledge. KW - Production planningcontrolmachine learningvalue stream simulationproduction datasystem architecturefast decision-making processes Y1 - 2024 U6 - https://doi.org/10.1016/j.procir.2024.10.059 VL - 130 SP - 79 EP - 85 PB - Elsevier BV ER - TY - GEN A1 - Mielke, Jonas A1 - Winkler, Herwig T1 - Application of machine learning algorithms for the evaluation of employee qualification matrices in complex production systems T2 - Procedia CIRP N2 - Machine learning (ML) methods are powerful tools for the rapid and targeted evaluation of large data volumes. Especially in the area of production planning and control (PPC), there is huge potential for making the operational and strategic organization of production systems more efficient. However, in order to achieve these objectives, practical research contributions are required that focus on the actual boundary conditions of PPC. This article presents the practical evaluation of qualification matrices using the gradient boosting method and cross-validation to determine the influence of data splitting and selection on prediction accuracy to demonstrate the usability of the system. KW - machine learningproduction planningcontrolpersonnel qualificationcontrol systemcomplex production systemsproduction data Y1 - 2024 U6 - https://doi.org/10.1016/j.procir.2024.10.058 SN - 2212-8271 VL - 130 SP - 72 EP - 78 PB - Elsevier BV ER - TY - GEN A1 - Abdulghani, Tamer A1 - Winkler, Herwig A1 - Abdelkafi, Nizar A1 - Lange, Hans-Rüdiger T1 - Paving the Green Path: a Maturity Model for Digital Decarbonization T2 - Procedia CIRP N2 - Digital transformation has been employed across all industries to enhance business operations. Its potential lies in optimising existing business models and creating innovative ones based on various technologies, making them fit for the future by reducing greenhouse gas emissions and fulfilling future decarbonization goals. In order to ensure a successful digital transformation towards decarbonization, managers have to assess the capabilities of their organisation and competently oversee their journey by defining a clear path employing digital maturity models. For this purpose, the analysis focused on published literature concerning digital maturity models developed to guide organisations in navigating their digital transformation journey. A systematic literature review of 36 selected digital maturity models is presented in this paper. These models overlook decarbonisation measures and focus solely on demonstrating digital capabilities, efficiency and profit growth. Therefore, a maturity model for digital decarbonization encompassing dimensions of decarbonization goals, business model innovation, customers and suppliers, products and services, employees, collaboration, processes and activities, data, and IT-infrastructure, -security and -technology has been developed. It assists organisations in reducing their carbon footprint by strategically and effectively leveraging digital technologies. The proposed model has five maturity levels: assess and align, engage and refine, innovate and collaborate, optimize and personalize, and transform and decarbonize. This paper aims to assess the current level of digital decarbonization and determine the position of the organizations. Additionally, it endeavours to identify the specific opportunities for growth that digitalization presents, thereby enabling the creation of a new digital carbon-free existence. KW - Maturity Model KW - Digital Decarbonization KW - Digital Transformation KW - Review Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1016/j.procir.2024.10.201 SN - 2212-8271 VL - 130 SP - 1020 EP - 1027 ER - TY - GEN A1 - Hayat, Mubashir A1 - Winkler, Herwig T1 - Blockchain-based Order Management Process in Decentralized Production Networks: A Conceptual Framework T2 - Procedia CIRP N2 - In today's world of decentralized production, original equipment manufacturers (OEMs) are facing more than ever an increasing complexity in their order management processes. On the one hand, they deal with issues related to data transparency, traceability, and security in constant back-and-forth communication exchange between customers and sub-contractors having to confirm the order status. On the other hand, there are significant obstacles when it comes to integrating their traditional order management systems with the partner organization's information systems within the production network. These complexities tend to decrease the ability of OEMs to remain flexible and profitable. Therefore, they are constantly looking for feasible solutions to overcome these issues and improve their order management processes. To this aim, this work proposes a novel blockchain-based conceptual framework for order management that intends to establish a secure environment for efficient communication and data integrity. The decentralized and immutable nature of blockchain makes it capable of providing real-time data as well as ensuring data security and transparency. Furthermore, this work also set up basic level SQLite3 databases and design prototypical on-chain and off-chain smart contract logics that can be used to automate various operations and decisions making in order management, thereby, improving the overall order processing efficiency. According to the authors knowledge, there is no such framework available in research. Therefore, it is suggested that organization can utilize the proposed framework to develop their own blockchain based order management systems. Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1016/j.procir.2024.10.218 SN - 2212-8271 IS - 130 SP - 1137 EP - 1143 ER - TY - GEN A1 - Eichenseer, Patrick A1 - Winkler, Herwig T1 - Predicting picking and workforce planning for internal shopfloor material logistics – a simulative, data-driven forecasting model T2 - Journal of Modelling in Management N2 - Purpose With increasing demands for competitiveness, demand fulfilment and cost efficiency, the need to optimise workforce planning in logistics has become crucial. This applies not only to external customer demands, but also to internal customers, i.e. production. For this reason, the purpose of this paper is to develop a simulative, data-driven model that predicts the internal shopfloor material logistics demands. Design/methodology/approach It is a hybrid approach that includes both deterministic and probabilistic components and is an alternative to advanced but data and knowledge-dependent machine learning algorithms. Inductive, self-developed procedures, heuristic calculation rules and consideration of real-world factors form the basis of the prediction of the number of picks. The number of picks predicted in the first step forms the basis for deriving the number of employees required in the second step, and thus the basis for optimised workforce planning. The developed approach was then validated in a case study in a real company. Findings The results show that the model significantly optimises not only the planning efficiency, but also the forecasting effectiveness through better decision making in demand prediction and workforce planning in internal shopfloor material logistics compared to the status quo on a weekly basis (95.5% accuracy in the case study). This improved decision making leads to increased efficiency throughout the intralogistics/production system. Originality/value A structured approach is described for systematically predicting the number of internal picks, which is highly relevant in practice and cannot be found in the existing literature (from the data model to the calculation rules, including statistical influencing factors, to the prediction). In terms of future research, the model has the potential to be used and validated in additional companies. KW - Data KW - Logistics KW - Workforce planning KW - Forecast KW - Picking KW - Shopfloor Y1 - 2024 U6 - https://doi.org/10.1108/jm2-09-2024-0288 SN - 1746-5664 VL - 2024 PB - Emerald ER - TY - GEN A1 - Eichenseer, Patrick A1 - Hans, Lukas A1 - Winkler, Herwig T1 - A data-driven machine learning model for forecasting delivery positions in logistics for workforce planning T2 - Supply Chain Analytics N2 - Workforce planning in logistics is a major challenge due to increasing demands and a dynamic environment. The number of delivery positions is a key factor in determining staffing requirements. This is often predicted subjectively based on employee assessments. To improve decision making and increase both the efficiency of this important forecasting process and the use of resources in the production system, i.e. shopfloor logistics, a data-driven machine learning model with a forecasting horizon of 5 working days was developed and validated in a practical case study in a company. The results show that the novel and specifically developed model outperforms both the manual forecasting approach in practice and auto machine learning models in terms of accuracy. The outperformance is particularly strong in the short term. Based on the predicted delivery positions, an optimised workforce planning was subsequently carried out in the case study company. Limitations of the model include the fact that it was validated in only one company and that the number of picks may need to be derived for more accurate scheduling. These two aspects also represent potential for future research. KW - Delivery Positions KW - Forecasting KW - Logistics KW - Workforce Planning KW - Machine Learning KW - Picks Y1 - 2025 U6 - https://doi.org/10.1016/j.sca.2024.100099 SN - 2949-8635 VL - 9 (2025) PB - Elsevier BV ER - TY - GEN A1 - Lamann, Arian A1 - Winkler, Herwig A1 - Lange, Hans-Rüdiger A1 - Abdelkafi, Nizar T1 - Identification and analysis of barriers in co-innovation projects in the manufacturing and energy industry T2 - Procedia CIRP N2 - Innovation is critical to a company’s growth in today’s competitive environment. Co-innovation projects, where companies pool resources, represent a promising way to achieve this goal. This study, based on a focus group of industry experts from the manufacturing and energy sectors, aims to identify the challenges of such collaborations and propose suitable solutions. The study relies on the innovation phase model and identifies several obstacles such as short technology lifecycles, data management overload, and organizational shortcomings in co-innovation projects. Effective management, communication and intellectual property (IP) can be a significant challenge in co-innovation projects, particularly due to uncertainties surrounding the release of sensitive data and inadequate contractual support. Uncoordinated communication can also result in project delays or failure. Furthermore, the absence of access protocols for online applications presents risks such as unauthorized access and document change tracking issues, further complicating complex engineering projects. Despite the availability of digital providers, many of them still require physical platforms for efficient innovation projects due to insufficient support for e.g., data management and caused by communication limitations. In conclusion, this study emphasizes the urgent need for research on co-innovation projects and recommends a collaborative framework to address the complexities triggered by this kind of projects and increase collaboration effectiveness. Y1 - 2024 U6 - https://doi.org/10.1016/j.procir.2024.10.248 SN - 2212-8271 VL - 130 (2024) SP - 1334 EP - 1339 PB - Elsevier BV ER -