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Evaluation of Factory Elements for the Configuration of Learning Factories

Kreß, Antonio ; Metternich, Joachim (2022)
Evaluation of Factory Elements for the Configuration of Learning Factories.
11th Conference on Learning Factories (CLF). Online (01.07.2021-02.07.2021)
doi: 10.26083/tuprints-00021312
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Item Type: Conference or Workshop Item
Type of entry: Secondary publication
Title: Evaluation of Factory Elements for the Configuration of Learning Factories
Language: English
Date: 2022
Place of Publication: Darmstadt
Year of primary publication: 2021
Publisher: Elsevier
Book Title: Proceedings of the Conference on Learning Factories (CLF) 2021
Collation: 6 Seiten
Event Title: 11th Conference on Learning Factories (CLF)
Event Location: Online
Event Dates: 01.07.2021-02.07.2021
DOI: 10.26083/tuprints-00021312
Corresponding Links:
Origin: Secondary publication service
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: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-213120
Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
600 Technology, medicine, applied sciences > 670 Manufacturing
Divisions: 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW)
Date Deposited: 06 May 2022 10:02
Last Modified: 03 Apr 2023 09:57
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21312
PPN: 495512192
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