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 - TY - CHAP A1 - Winkler, Herwig A1 - Tomenendal, Matthias A1 - Abdelkafi, Nizar A1 - Lange, Hans-Rüdiger ED - Zhang, Michael T1 - Managing the coal exit in a mining region - Strategic landscape design and niche management for a sustainable socio-technical regime in Lusatia T2 - Strategic management and sustainability transitions - Theory and Practice N2 - In the course of the European Green Deal, which defines the goal of “no net emissions of greenhouse gases in 2050” (European Commission, 2019, p. 2) and a supply of “clean, affordable and secure energy” (p. 3), the European energy sector needs a rapid and significant sustainability transition. In Germany, the federal government has accordingly decided to phase out energy production from lignite-fired power plants by 2038 at the latest. Most affected regional ecosystems in Germany are the Lusatia mining region (Lausitzer Revier), the Rhenish mining region (Rheinisches Revier) and the Middle German mining region (Mitteldeutsches Revier). In these regions, fundamental sustainability transitions have to take place. Y1 - 2024 SN - 9781003329909 U6 - https://doi.org/10.4324/9781003329909-7 SP - 83 EP - 118 PB - Routledge CY - New York ET - 1st Edition ER - TY - GEN A1 - Abdulghani, Tamer A1 - Winkler, Herwig T1 - Unlocking the power of digital technology for fostering sustainability T2 - IEEE Student Conference on Research and Development N2 - In the pursuit of economic, social and environmental sustainability, digital technologies have the potential to become powerful tools that provide innovative solutions for the reduction of greenhouse gas emissions and the mitigation of the effects of climate change. Therefore, it is essential to understand how digital technologies can drive or inhibit the sustainability endeavors of firms. For this purpose, we systematically analyze the literature published during the last five years to feature how digitalization could contribute to the achievement of a more sustainable future. The study provides key findings on the potential applications of digital technologies in promoting and contributing to sustainability and presents a roadmap for adopting digital technologies. It also highlights the challenges and barriers faced by different industries in realizing the full potential of these technologies as drivers of sustainable development as well as identifies future directions. KW - Sustainability KW - Digitalization KW - Digital Technology KW - Industry 4.0 KW - A Systematic Literature Review Y1 - 2024 UR - https://ieeexplore.ieee.org/document/10563721 SN - 979-8-3503-1882-1 U6 - https://doi.org/https://doi.org/10.1109/SCOReD60679.2023.10563721 SP - 57 EP - 64 ER - TY - GEN A1 - Abdulghani, Tamer A1 - Winkler, Herwig T1 - Managing the decarbonization of coal-based energy value chain: empirical findings from Lusatia, Germany T2 - IEEE Student Conference on Research and Development N2 - Decarbonization is causing structural changes that exert pressure not only on the companies and their employees in the lignite industry, but also on other companies along the value chain across multiple sectors. As a result, companies have an urgent need for change. They are challenged to take action to build a sustainable new existence as the markets collapse. For this purpose, we investigate the structural changes due to decarbonization and what factors can be part of a successful business transformation to manage the decarbonization of the coal-based energy value chain. This paper presents empirical insights from several semi-structured interviews with the key actors i.e., energy companies, service providers, logistics companies, suppliers, etc. in the Lusatia mining region as well as a conceptual model for a successful business transformation in the region. We used the Gioia method to analyze the interview data in order to obtain a meaningful data structure. This resulted in a conceptual model that shows how success factors have to be combined to manage the decarbonization measures within a region. KW - Decarbonization KW - Coal Phase-out KW - Energy Value Chain KW - Structural Change KW - Case Study KW - Digitalization Y1 - 2024 UR - https://ieeexplore.ieee.org/document/10563936 U6 - https://doi.org/https://doi.org/10.1109/SCOReD60679.2023.10563936 SP - 307 EP - 315 ER - TY - GEN A1 - Eichenseer, Patrick A1 - Winkler, Herwig T1 - A data-oriented shopfloor management in the production context: a systematic literature review T2 - The International Journal of Advanced Manufacturing Technology N2 - AbstractData not only plays an essential role in traditional shopfloor management, but it is also becoming even more important in Industry 4.0, particularly due to the increasing possibilities offered by new digital and data technologies and developments. In this context, the literature often refers to digital shopfloor management, the next generation shopfloor or other evolutionary synonyms. This raises the question of how to differentiate the content of data-oriented shopfloor management from digital shopfloor management. This paper discusses the state of the art — in terms of both data and digital perspectives — using a systematic literature review. Due to the complexity of the topic, three different levels of consideration — technology, organisation and people — are examined and discussed. Existing conceptual approaches are analysed in terms of conclusions and research gaps. It was found that the area of technology, including dedicated applications, is very well represented and researched in the literatur Y1 - 2024 U6 - https://doi.org/10.1007/s00170-024-14238-8 SN - 0268-3768 VL - 134 SP - 4071 EP - 4097 PB - Springer Science and Business Media LLC ER - TY - RPRT A1 - Berneis, Moritz A1 - Winkler, Herwig T1 - Untersuchung von aktuellen Trends und Herausforderungen im Supply Chain Management in Deutschland N2 - ▪Im Zeitraum von November 2022 bis Januar 2023 wurde am Lehrstuhl für Produktionswirtschaft der BTU Cottbus-Senftenberg die Studie mit dem Titel „Untersuchung von aktuellen Trends und Herausforderungen im SCM deutscher Unternehmen“ durchgeführt. Es wurden dazu Interviews mit über 20 Experten durchgeführt. 16 Interviews wurden ausgewertet. ▪ Im ersten Themenkomplex wurden aktuelle Ereignisse untersucht. Die häufigsten Herausforderungen sind demnach die Ressourcenverfügbarkeit, lange Lieferzeiten, der Fachkräftemangel und preisgetriebene Themen. Experten wurden zu aktuellen Ereignissen befragt, wie Rohstoffpreisschwankungen, der Zero-Covid-Strategie von China und Knappheiten in der Logistik. Preisschwankungen betrafen die meisten Unternehmen, während Krieg und Sanktionen nur indirekt wirkten. Die Zero-Covid-Strategie führte zu Vertrauensverlust in China und Knappheiten in der Logistik zu höheren Transportkosten. Der Fachkräftemangel wurde auch als aktuelles Ereignis genannt. ▪ Der zweite Themenkomplex ist Veränderungen im SCM gewidmet. Dabei wurde insbesondere der Wandel in komplexere Netzwerke, der Aufstieg des E-Commerce und die Bedeutung von Vertrauen und Transparenz angeführt als wesentliche Veränderungen. Supply Chains sind gewachsen und komplexer geworden, aber auch die Qualität der Vernetzung hat sich verbessert. E-Commerce für Standardprodukte wird weiter an Relevanz gewinnen. Kein Trend zur Deglobalisierung wurde festgestellt. Die Experten sind sich einig, dass Supply Chains "too lean" geworden sind und das Risikomanagement wichtiger ist. Das oberste Ziel sollte sein, die Produktion und Lieferfähigkeit sicherzustellen. ▪ Der dritte Themenkomplex befasst sich mit Nachhaltigkeit, einschließlich des Lieferkettengesetzes (LkSG), Dekarbonisierung und Nachhaltigkeitsnachweisen. Die meisten Unternehmen erfüllen die Anforderungen des LkSG und nehmen das Gesetz als Klausel in Verträgen auf. Bürokratie in Deutschland führt zu hohen Kosten, besonders für kleine Unternehmen. Kosteneinsparungen sind der größte Treiber bei der Dekarbonisierung. Nachhaltigkeit wird immer wichtiger und kann über Zertifikate nachgewiesen werden. ▪ Im vierten Themenkomplex Software wurde die Anpassungsfähigkeit von ERP-Systemen bemängelt und automatische Bestellungen wurden kontrovers betrachtet. Die meisten Experten verzichten aus Sicherheitsgründen auf Cloud-basierte Lösungen aufgrund des hohen Risikos für Cyberangriffe und möglicher Gefahren für die Supply Chain. ▪ In Themenkomplex fünf wurden neue Technologien im SCM besprochen. AR wird für die Wartung von Maschinen eingesetzt und VR für Simulation und Planung. IoT wird für predictive Maintenance genutzt, Treiber der Automatisierung ist der Fachkräftemangel. Big Data & AI sollen die Vorhersage der Nachfrage verbessern. Additive Fertigung hat Potenzial für Automatisierung und sinkende Kosten. Die Blockchain gilt als komplex und kann als geteilte Datenbank, Zahlungssystem und zur Rechnungsteilung dienen. ▪ Im sechsten Themenkomplex Sichtbarkeit halten Experten Transparenz für wichtig, sie ist noch zu gering aber bietet Potenzial zur Erkennung von Risiken. Tracking und Tracing ist intern verbreitet, international gibt es noch Probleme. Eine höhere Zahlungsbereitschaft der Kunden für Sichtbarkeit besteht nicht, diese entsteht nur für einen besseren Service. KW - Herausforderungen KW - Logistik KW - Logistics KW - Supply Chain Management KW - Trends KW - Challenges KW - Interview Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus4-62950 PB - Brandenburgische Technische Universität, IKMZ, Universitätsbibliothek CY - Cottbus ; Senftenberg 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 - TY - GEN A1 - Stockmann, Christian A1 - Winkler, Herwig T1 - Robustness in production systems – findings from a systematic literature review T2 - International Journal of Services and Operations Management N2 - In production systems, the concept of robustness is widespread due to various applications, purposes, and interpretations. However, little is known about the conceptual characteristics of robustness in production settings and even less about its distinctions from other related terms – such as flexibility or agility. That is why this paper systematically reviews production literature to identify the contexts in which robustness is mostly applied and to analyse existing concepts. It is found that despite a vast literature body addressing robustness in varying forms, few studies cover it on a conceptual basis. Within this small sample, a rather general agreement on the meaning of robustness in production systems is found. However, details, interpretations, mentioned interlinkages to related terms differ across the identified literature. Based on the findings of the review and with the help of established literature on adjacent concepts, a comprehensive understanding of robustness in production systems is developed. KW - robustness KW - production management KW - production systems KW - operations management KW - flexibility KW - resilience KW - resistance KW - changeability Y1 - 2023 UR - https://www.inderscienceonline.com/doi/10.1504/IJSOM.2023.129462 U6 - https://doi.org/10.1504/IJSOM.2023.129462 SN - 1744-2370 SN - 1744-2389 VL - 44 IS - 3 SP - 368 EP - 389 ER -