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Teaching resource efficiency in learning factories — systematic approach for choosing measures

Weyand, Astrid ; Seyfried, Stefan ; Bardy, Sebastian ; Laghai, Bijan ; Metternich, Joachim ; Weigold, Matthias (2023)
Teaching resource efficiency in learning factories — systematic approach for choosing measures.
13th Conference on Learning Factories (CLF 2023). Reutlingen (09.-11.05.2023)
doi: 10.2139/ssrn.4469318
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

Due to the rising demand towards industry to reduce greenhouse gas emissions and resource consumption, the topic of resource efficiency is gaining interest amongst the learning factory community. To show visitors and workshop participants possible resource efficiency measures, learning factory operators need to choose, which measures to implement in their learning factory. Several lists with resource efficiency measures exist already, rating the measures in terms of criteria like financial invest or payback period. Although these are important criteria for industry, learning factory operators also need to consider additional criteria regarding the learning factory environment, like visibility of the measure, to achieve the best teaching experience. To support learning factory operators in this process, an approach based on an interactive catalogue for resource efficiency measures is developed, focusing on the requirements of learning factories. Therefore, literature is first analysed regarding resource efficiency measures and conventional criteria, followed by the development of learning factory specific criteria as well as the rating of the measures according to these. The result is transferred into a tool for learning factory operators and exemplarily demonstrated in a use case.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2023
Autor(en): Weyand, Astrid ; Seyfried, Stefan ; Bardy, Sebastian ; Laghai, Bijan ; Metternich, Joachim ; Weigold, Matthias
Art des Eintrags: Bibliographie
Titel: Teaching resource efficiency in learning factories — systematic approach for choosing measures
Sprache: Englisch
Publikationsjahr: 2023
Verlag: Elsevier B.V.
Reihe: SSRN elibrary
Kollation: 6 Seiten
Veranstaltungstitel: 13th Conference on Learning Factories (CLF 2023)
Veranstaltungsort: Reutlingen
Veranstaltungsdatum: 09.-11.05.2023
DOI: 10.2139/ssrn.4469318
URL / URN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4469318
Kurzbeschreibung (Abstract):

Due to the rising demand towards industry to reduce greenhouse gas emissions and resource consumption, the topic of resource efficiency is gaining interest amongst the learning factory community. To show visitors and workshop participants possible resource efficiency measures, learning factory operators need to choose, which measures to implement in their learning factory. Several lists with resource efficiency measures exist already, rating the measures in terms of criteria like financial invest or payback period. Although these are important criteria for industry, learning factory operators also need to consider additional criteria regarding the learning factory environment, like visibility of the measure, to achieve the best teaching experience. To support learning factory operators in this process, an approach based on an interactive catalogue for resource efficiency measures is developed, focusing on the requirements of learning factories. Therefore, literature is first analysed regarding resource efficiency measures and conventional criteria, followed by the development of learning factory specific criteria as well as the rating of the measures according to these. The result is transferred into a tool for learning factory operators and exemplarily demonstrated in a use case.

Freie Schlagworte: competencies, learning factory environments, sustainability, sustainable production
Fachbereich(e)/-gebiet(e): 16 Fachbereich Maschinenbau
16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW)
16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) > CiP Center für industrielle Produktivität
16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) > ETA Energietechnologien und Anwendungen in der Produktion
Hinterlegungsdatum: 28 Jun 2023 06:29
Letzte Änderung: 28 Jun 2023 06:29
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