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Auflistung nach Schlagwort "Production"

1 - 10 von 11
  • Textdokument
    Application Fields and Research Gaps of Process Mining in Manufacturing Companies
    (INFORMATIK 2020, 2021) Dreher, Simon; Reimann, Peter; Gröger, Christoph
    To survive in global competition with increasing cost pressure, manufacturing companies must continuously optimize their manufacturing-related processes. Thereby, process mining constitutes an important data-driven approach to gain a profound understanding of the actual processes and to identify optimization potentials by applying data mining and machine learning techniques on event data. However, there is little knowledge about the feasibility and usefulness of process mining specifically in manufacturing companies. Hence, this paper provides an overview of potential applications of process mining for the analysis of manufacturing-related processes. We conduct a systematic literature review, classify relevant articles according to the Supply-Chain-Operations-Reference-Model (SCOR-model), identify research gaps, such as domain-specific challenges regarding unstructured, cascaded and non-linear processes or heterogeneous data sources, and give practitioners inspiration which manufacturing-related processes can be analyzed by process mining techniques.
  • Konferenzbeitrag
    Assessing the performance of Neural Networks in Recognizing Manual Labor Actions in a Production Environment
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Höfinghoff, Maximilian; Buschermöhle, Ralf; Korn, Goy-Hinrich; Schumacher, Marcel; Seipolt, Arne
    Action recognition technology has gained significant traction in recent years. This paper focuses on evaluating neural network architectures for action recognition in the production industry. By utilizing datasets tailored for production or assembly tasks, various architectures are assessed for their accuracy and performance. The findings of this study provide some insights and guidance for researchers and practitioners to select an appropriate architecture or pretrained models for action recognition in the production industry.
  • Zeitschriftenartikel
    Die Industrie 4.0 aus ethischer Sicht
    (HMD Praxis der Wirtschaftsinformatik: Vol. 52, No. 5, 2015) Bendel, Oliver
    Der vorliegende Beitrag arbeitet die wesentlichen Merkmale der Industrie 4.0 heraus und setzt sie ins Verhältnis zur Ethik. Es interessieren vor allem Bereichsethiken wie Informations-, Technik- und Wirtschaftsethik. Am Rande wird auf die Maschinenethik eingegangen, im Zusammenhang mit der sozialen Robotik. Es zeigt sich, dass die Industrie 4.0 neben ihren Chancen, die u. a. ökonomische und technische Aspekte betreffen, auch Risiken beinhaltet, denen rechtzeitig in Wort und Tat begegnet werden muss.AbstractThis article highlights the essential features of the industry 4.0 and puts them in relation to ethics. Of special interest are the fields of applied ethics such as information, technology and business ethics. Machine ethics is mentioned in passing in connection with social robotics. It is evident that the industry 4.0 in addition to opportunities, affecting among other things economic and technical aspects, includes also risks which must be addressed in word and deed in a timely manner.
  • Konferenzbeitrag
    Enhancing Digital Shadows with Workflows
    (Modellierung 2022 Satellite Events, 2022) Heithoff, Malte; Michael, Judith; Rumpe, Berhard
    The vast amount of data in modern manufacturing demands acquisition of contextualized data to enable fast decision making where domain expertise must be provided at run-time. Within this paper, we investigate the research question how to handle human-machine-interactions for engineering of digital shadows and still ensure the traceability of computation and simulation results. Current research for digital shadows concentrates on modeling key elements such as data sharing or metadata, but does not incorporate human-machine-interaction or the traceability of data aggregation. In this paper, we present a conceptual model which covers the base concepts for digital shadows integrating human-machine-interaction by utilizing workflows. We extend the conceptual digital shadow model defined within the “Internet of Production” excellence cluster and showcase our approach on an example. This contribution presents an applicable modeling approach for designing digital shadows which provide contextual information of the underlying human integrated process.
  • Konferenzbeitrag
    Enhancing Digital Twins for Production through Process Mining Techniques: A Literature Review
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Schumacher, Marcel; Buschermöhle, Ralf; Haak, Liane; Höfinghoff, Max; Seipolt, Arne; Korn, Goy-Hinrich
    A digital twin (DT) plays a vital role in the advancement of manufacturers towards Industry 4.0. However, the creation and maintenance of DTs can be time-consuming. One approach to streamline this process is the utilization of process mining (PM) methods and techniques, which can automatically generate valuable information for DTs. Therefore, this paper aims to examine different approaches that augment DTs with PM and explore their effects. The review categorizes these approaches into three groups: theoretical approaches, approaches with laboratory case studies, and approaches with real-world case studies conducted by manufacturers. The review reveals that the use of PM can enhance the flexibility and sustainability of DTs. However, this improvement comes at the cost of requiring high-quality data and more data preparation efforts.
  • Zeitschriftenartikel
    Industrie 4.0 in kleinen und mittleren Unternehmen – Welche Potenziale lassen sich mit smarten Geräten in der Produktion heben?
    (HMD Praxis der Wirtschaftsinformatik: Vol. 56, No. 6, 2019) König, Ulrich Matthias; Röglinger, Maximilian; Urbach, Nils
    Die hohe Anzahl genutzter smarter Geräte führt zu deren weiter Verbreitung und engen Integration im Alltag. Mit der Erweiterung von Alltagsgegenständen um Netzwerkkonnektivität, dem Internet der Dinge (Internet of Things, IoT), ist ein neuer Trend beobachtbar. Das Internet der Dinge bietet zahlreiche Einsatzgebiete und katalysiert die Verschmelzung von physischer und digitaler Welt. Dadurch lassen sich insbesondere Kommunikation und Interaktion zwischen Individuen, Gegenständen und Unternehmen verbessern. In der Industrie muss zur Integration und Potenzialnutzung des Internets der Dinge der Kontext gewissenhaft analysiert werden. Plant ein Unternehmen eine Transformation hin zu Industrie 4.0, so muss es Abhängigkeiten zu Produktionsanlagen und Anwendungssystemen berücksichtigen. Motiviert durch Effizienzpotenziale, haben große Unternehmen bereits mit der Transformation begonnen. In kleinen und mittleren Unternehmen (KMU) wird die Umstellung oft noch defensiv betrachtet. Jedoch bietet sich auch für KMU großes Kostenreduktions- und Prozessverbesserungspotenzial. Diese Problemstellung adressiert das von der Bayerischen Forschungsstiftung geförderte Forschungsprojekt „SmarDes@Work – Smart Devices in der Produktion“ . Ziel war es, handelsübliche smarte Geräte einfach in die Produktionsprozesse von KMU zu integrieren. Im Rahmen des Forschungsprojekts erarbeitete ein Konsortium aus Wissenschaftlern, Produktionsbetrieben und Softwareherstellern eine Startlösung für Industrie 4.0 in KMU. In diesem Beitrag werden die zentralen Erkenntnisse vorgestellt und Handlungsempfehlungen abgeleitet. The growing number of smart devices leads to widespread use and close integration to daily life. A new trend, the Internet of Things (IoT), has emerged as a result of technology being closely integrated with everyday objects. The IoT offers numerous areas of application and catalysis the fusion of the physical and digital worlds. In this way, communication and interaction between individuals, objects, and organizations can be improved. In manufacturing, the integration and exploitation of the potential of IoT requires a careful analysis of the context. If a company plans for a transformation to industry 4.0, they must consider dependencies on production facilities and application systems. Motivated by efficiency potentials, large companies have already started the transformation. In small and medium-sized enterprises (SMEs), the changeover is often still viewed defensively. However, there is also a great potential for cost reduction and process improvement for SMEs. This problem is addressed by the research project “SmarDes@Work – Smart Devices in Production” funded by the Bavarian Research Foundation . The aim of the research project was to easily integrate commercially available smart devices into the production processes of SMEs. As part of this research project, a consortium of academics, production companies, and software developers developed a starting solution for Industry 4.0 in SMEs. In this paper, the central findings are presented and recommendations for action are derived.
  • Konferenzbeitrag
    Message from the Modellierung’22 Workshop Chairs
    (Modellierung 2022 Satellite Events, 2022) Michael, Judith; Pfeiffer ,Jérôme; Wortmann, Andreas
    Preface of the Modellierung’22 Workshop, Tools und Demos Proceedings
  • Zeitschriftenartikel
    Nutzung von unterschiedlich strukturierten Daten zur Fehleranalyse in Produktionsbetrieben: Eine prototypische Beispielimplementierung
    (HMD Praxis der Wirtschaftsinformatik: Vol. 61, No. 5, 2024) Möhring, Michael; Keller, Barbara
    Der Einsatz von Daten mit unterschiedlicher Struktur zur Fehleranalyse in der Produktion ist eine große Herausforderung für Industrieunternehmen. Dieser Artikel zeigt einen prototypischen Lösungsweg auf, wie die Integration von unterschiedlich strukturierten Daten zur Fehleranalyse gelingen kann. Anhand eines Fallbeispiels wird ein Prototyp konzipiert und umgesetzt, der verschiedene Verfahren zur Analyse von Daten unterschiedlicher Struktur kombiniert und die spezifischen Anforderungen in der datengetriebenen Produktionsfehleranalyse adressieren kann. Das Ergebnis zeigt eine innovative Möglichkeit zur datengetriebenen Fehleranalyse für die Produktion, in der unterschiedlich strukturierte Daten eingesetzt und verschiedene Analyseverfahren miteinander nutzendstiftend verbunden sind. Die Evaluation durch Experten zeigt ferner, dass der vorgeschlagene prototypische Lösungsweg für den Einsatz in der Praxis geeignet ist und einen Mehrwert für Unternehmen stiften kann. Aufbauend auf diesen Erkenntnissen werden Implikationen benannt, Limitationen aufgezeigt und zukünftiger Forschungsbedarf abgeleitet. The integration of data with varying structures for error analysis in production poses a significant challenge for production enterprises. This article presents a prototype solution for successfully integrating differently structured data for error analysis. A sample case is used to design and implement a prototype that combines various methods for analysing data with different structures, addressing the specific requirements of a data-driven production error analysis. The results demonstrate an innovative approach to implement data-driven error analysis in production. This approach combines differently structured data and analysis methods in a beneficial way. Experts evaluated the proposed prototype solution and found it suitable for practical use, creating added value for enterprises. Based on these findings, implications were identified, limitations were described, and future research needs were derived.
  • Konferenzbeitrag
    Saving energy in production using mobile services
    (Software Engineering 2013 - Workshopband, 2013) Ruff, Christopher; Laufs, Uwe; Müller, Moritz; Zibuschka, Jan
    High energy costs have led to an increasing relevance of energyefficiency over the last few years. While new equipment is mostly designed to be energy-efficient, feasible action is needed to decrease energy consumption of existing equipment on the shop-floor level. As interventions there rely on dependable information and its use at the right time and place, involvement of ICT systems and particularly mobile devices becomes evident. In our approach, a system based on a SOA back end and a mobile device-based front end was implemented as a prototype. The system uses data provided by sensors, production orders and additional metadata describing specific properties of the production systems to provide decision support and to generate recommendations for the stakeholders to realize immediate as well as longer-term energy savings.
  • Konferenzbeitrag
    A Vision Towards Generated Assistive Systems for Supporting Human Interactions in Production
    (Modellierung 2022 Satellite Events, 2022) Michael, Judith
    Human workers need to cope with complex production settings when handling and monitoring cyber-physical production systems. Assistive systems can provide situational step-by-step support for human behavior, e.g., when interacting with a machine or for manual assembly. These systems need to take personal knowledge, workers skills or personal restrictions into account and are therefore subject to privacy concerns. However, the engineering of such interactive assistive systems within the production domain is a complex task as they might support critical functionality in dangerous environments and have a high need for safety and privacy considerations due to processing personal data. We want to investigate how the software engineering process of assistive systems in production can be improved to achieve higher reusability. Current research focuses on specific use cases and implements systems specifically for those needs without reusability in mind. We suggest using behavior and context models in a generative approach, to create a reusable method to engineer assistive systems for production environments, either as own applications or as services integrated within digital twins. We have already applied model-driven methods for assistive systems in the smart home domain and discuss the opportunities and challenges of an application of these methods for the production domain. These methods can facilitate the engineering of assistive functionalities within applications in production while meeting privacy, adaptability, and context-sensitivity requirements.