Kreß, Antonio ; Metternich, Joachim (2022)
Evaluation of Factory Elements for the Configuration of Learning Factories.
11th Conference on Learning Factories (CLF). Online (01.-02.07.2021)
doi: 10.26083/tuprints-00021312
Konferenzveröffentlichung, Zweitveröffentlichung, Verlagsversion
Kurzbeschreibung (Abstract)
Learning factories for lean production represent the majority of the learning factories worldwide. Learning factories for lean production represent the majority of them. For the design of learning factories, the organisational framework, the organisational targets, the target groups and the intended competencies should be clarified, so factory elements can be preselected. Examples of factory elements in learning factories are machines, equipment, assembly lines or logistics systems. The selection of factory elements is a complex task since various restrictions must be considered such as the budget or layout constraints. Configuration systems can simplify the selection process. Since the learning factory developer wants to choose the best factory elements, it is necessary to define how to evaluate the utility of the preselected factory elements. Both competency-based criteria based on the learning targets, and general evaluation criteria for learning factories such as the degree of changeability play a role. In this paper, an evaluation method is presented which allows an individual evaluation of factory elements for learning factories with the focus on lean production. The method is based on a utility value analysis with previously researched evaluation criteria. For each evaluation criterion, a fixed classification is made into strong, medium, weak and non-evaluable. Since the evaluation criteria are different for each learning factory, they can be weighted individually according to the specific use case. This evaluation scheme can also be used to evaluate existing learning factory configurations. As a case study, factory elements of the process learning factory CiP of the TU Darmstadt are examined. However, new learning factories to be developed can also be configured on this basis.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Kreß, Antonio ; Metternich, Joachim |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Evaluation of Factory Elements for the Configuration of Learning Factories |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Ort: | Darmstadt |
| Verlag: | Elsevier |
| Buchtitel: | Proceedings of the Conference on Learning Factories (CLF) 2021 |
| Kollation: | 6 Seiten |
| Veranstaltungstitel: | 11th Conference on Learning Factories (CLF) |
| Veranstaltungsort: | Online |
| Veranstaltungsdatum: | 01.-02.07.2021 |
| DOI: | 10.26083/tuprints-00021312 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/21312 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichungsservice |
| Kurzbeschreibung (Abstract): | Learning factories for lean production represent the majority of the learning factories worldwide. Learning factories for lean production represent the majority of them. For the design of learning factories, the organisational framework, the organisational targets, the target groups and the intended competencies should be clarified, so factory elements can be preselected. Examples of factory elements in learning factories are machines, equipment, assembly lines or logistics systems. The selection of factory elements is a complex task since various restrictions must be considered such as the budget or layout constraints. Configuration systems can simplify the selection process. Since the learning factory developer wants to choose the best factory elements, it is necessary to define how to evaluate the utility of the preselected factory elements. Both competency-based criteria based on the learning targets, and general evaluation criteria for learning factories such as the degree of changeability play a role. In this paper, an evaluation method is presented which allows an individual evaluation of factory elements for learning factories with the focus on lean production. The method is based on a utility value analysis with previously researched evaluation criteria. For each evaluation criterion, a fixed classification is made into strong, medium, weak and non-evaluable. Since the evaluation criteria are different for each learning factory, they can be weighted individually according to the specific use case. This evaluation scheme can also be used to evaluate existing learning factory configurations. As a case study, factory elements of the process learning factory CiP of the TU Darmstadt are examined. However, new learning factories to be developed can also be configured on this basis. |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-213120 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau 600 Technik, Medizin, angewandte Wissenschaften > 670 Industrielle und handwerkliche Fertigung |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) |
| Hinterlegungsdatum: | 06 Mai 2022 10:02 |
| Letzte Änderung: | 09 Mai 2022 06:24 |
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