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Enabling Knowledge Transfer in Product Development and Production – Methods and Techniques from Artificial Intelligence

Authors:

Peter Funk

Publication Type:

Conference/Workshop Paper

Venue:

The 1st Nordic Conference on Product Lifecycle Management

Abstract

A strong trend in production industry is to move production abroad to low-wage countries. Incidental evidence indicates that there is a large risk that product development follows, thus loss of important knowledge, the only meaningful resource today. We suggest that a company may increase its efficiency by enabling knowledge transfer within and between the product development and production department using AI methods and techniques. Thus, the objective discusses how the AI methods and techniques could facilitate knowledge transfer in product development and production. Knowledge creation in a product development and production does in many aspects agree with the SECI-model addressing transfer of tacit and explicit knowledge in an organization. Knowing how knowledge is created facilitates the understanding of how to solve problems with knowledge transfer. Three cases illustrate current needs of AI methods and techniques within manufacturing industry. Problem areas are information overload, past project experience and tacit knowledge. These could be facilitated by case-based reasoning, ontology and intelligent agent technology. Findings indicate that cased-based reasoning may be a way to transfer tacit knowledge between humans and to transform tacit knowledge into explicit knowledge. Thus, it may be used together with Nonaka’s SECI-model as a part of the S- and E-section.

Bibtex

@inproceedings{Funk892,
author = {Peter Funk},
title = {Enabling Knowledge Transfer in Product Development and Production – Methods and Techniques from Artificial Intelligence},
month = {January},
year = {2006},
booktitle = {The 1st Nordic Conference on Product Lifecycle Management},
url = {http://www.es.mdu.se/publications/892-}
}