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METHOD FOR REFERENCE-BASED MANUFACTURING COST ESTIMATION – EVALUATION STUDY USING A PROTOTYPE

Published online by Cambridge University Press:  19 June 2023

Fynn Hellweg*
Affiliation:
Robert Bosch GmbH, Robert-Bosch-Campus 1, 71272 Renningen, Germany; IPEK - Insitute of Product Engineering at Karlsruhe Institute of Technology (KIT), Kaiserstraße 10, 76131 Karlsruhe, Germany
Ardian Cacaj
Affiliation:
Robert Bosch GmbH, Robert-Bosch-Campus 1, 71272 Renningen, Germany;
Simon Haneke
Affiliation:
Robert Bosch GmbH, Robert-Bosch-Campus 1, 71272 Renningen, Germany;
Albert Albers
Affiliation:
IPEK - Insitute of Product Engineering at Karlsruhe Institute of Technology (KIT), Kaiserstraße 10, 76131 Karlsruhe, Germany
*
Hellweg, Fynn, Robert Bosch GmbH, Germany, fynn.hellweg@de.bosch.com

Abstract

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Within product development, manufacturing cost estimation provides a sound basis for design and management decisions. This secures companies profitability, but the effort is high and deep knowledge at the interface of design, manufacturing and costs is needed. These issues can be eased with automation enabled by semantic technologies. Therefore, the authors developed a method for reference-based manufacturing cost estimation and created a prototype. This research evaluates the method and the prototype. Observation, interview and questionnaire were conducted with ten experienced cost engineers at a large German manufacturing company.

Based on its results, the study shows the methods contribution to lower estimation effort, while the impact on transparency and the knowledge base was only partly verified. The method steps show different automation potential, so an incremental automation should be considered. Even though semantic technologies show high potential for identifying reference system elements in this study, the limiting factor for automation in manufacturing cost estimation remains the low availability of product and manufacturing information and missing knowledge of its connection within product development.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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