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A methodology for KEE systems target cascading
(Product Development Research Lab)ORCID iD: 0000-0002-5076-3300
Luleå Technical University. (Product Development Research Lab)ORCID iD: 0000-0003-4875-391X
Luleå Technical University. (Product Development Research Lab)ORCID iD: 0000-0002-9662-4576
2008 (English)In: Proceedings of the 7th International Symposium on Tools and Methods of Competitive Engineering - TMCE 2008 / [ed] I. Horváth and Z. Rusák, Delft University of Technology , 2008, Vol. 2Conference paper, (Refereed)
Abstract [en]

The main aim of this paper is to present a methodology developed within the European Project VIVACE to guide the design and implementation of a Knowledge Enabled Engineering (KEE) system in a Virtual Enterprise. The proposed methodology tries to overcome some of the limitations which characterise traditional methods for Target Cascading, promoting a more collaborative and iterative approach to derive system specifications (in terms of advanced knowledge functionalities) from initial high-level targets. Social and behavioural aspects of Knowledge Management play a crucial role when many different users, knowledge experts, and process owners are involved in the Knowledge Management System (KMS) development. A well designed methodology is needed, therefore, to enhance communication and information sharing among design teams, to promote requirements merging and to take care both of the technological and behavioural aspects of the implementation. Initial business targets have been step-by-step decomposed into a set of sub-problems (Service Requirements, Knowledge Issues, and Knowledge Challenges) in the form of simple sentences in natural language. Then Quality Function Deployment (QFD) matrixes have been used to identify the set of functionalities to be implemented in the system, addressing the most important knowledge-related problems outlined in the cascading activity.

Place, publisher, year, edition, pages
Delft University of Technology , 2008. Vol. 2
Keyword [en]
Knowledge Enabled Engineering, QFD, Target Cascading, VIVACE project
National Category
Other Mechanical Engineering
Identifiers
URN: urn:nbn:se:bth-11273ISBN: 978-90-5155-044-3 (print)OAI: oai:DiVA.org:bth-11273DiVA: diva2:889474
Conference
The 7th International Symposium on Tools and Methods for Concurrent Engineering, TMCE, Izmir, Turkey
Projects
Faste Laboratory
Funder
VINNOVA
Available from: 2015-12-26 Created: 2015-12-18 Last updated: 2016-09-30Bibliographically approved
In thesis
1. Knowledge engineering in the virtual enterprise: exploring a maturity-based decision support
Open this publication in new window or tab >>Knowledge engineering in the virtual enterprise: exploring a maturity-based decision support
2007 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

In product development, lead-time reduction, cost reduction, and quality improvement are issues that companies want to improve on to increase competitiveness. One recent approach to reach this - particularly in the aerospace industry where the complexity of product offers is steadily increasing - is to manage risk by forming virtual enterprises. A virtual enterprise is a network of partner companies that join on equal terms when an opportunity arises to develop a product offer, e.g. a jet engine offer, in a more agile manner than if any of the partners would realise it by themselves. They therefore team up to share risk, investment and resources - to in return also share revenue and profit. A driver for the formation of the virtual enterprise is the ability to effectively utilise partner knowledge assets. However, when sharing and managing knowledge effectively across the virtual enterprise, current practices have yet to evolve to meet the needs of knowledge workers, who may come from different aerospace companies, have different roles, belong to different disciplines and that may also be situated in geographically dispersed locations.

Improving product development includes allowing developers from all disciplines to know - as early as possible in the product development process - more about the customer needs, the desired product properties, and the downstream impact of the decisions they choose to make throughout the process. Knowing about the impact in downstream phases would allow for significant time and cost savings due to the avoidance of unnecessary and expensive rework that would otherwise occur much further on in the product's life cycle.

Among other things, a virtual enterprise can start organising and mapping the knowledge assets available in their teams, and information overload can be managed by assuring that the right knowledge ends up with the right person, to mention but a few things that can facilitate the everyday work of engineers and their colleagues. When working in a product development project, the virtual enterprise needs to assess the quality of the created knowledge as early as possible to devise the correct actions early. In this thesis, a Gated Maturity Assessment technique including the concept of knowledge maturity has been developed as an example of an improved stage-gate decision-making process. With this approach development teams are able to assess the knowledge maturity level in the content and rationale that is put forward as a basis for a decision - as opposed to only assessing the raw data of the results (i.e. thrust, weight, fuel burn, etc.). Knowledge maturity is used to support decision makers when in the process of assessing a decision base to make a decision whether to go ahead, abort the process, or order rework to be done. Naturally, if the decision base is poor, a decision to go ahead should probably not be taken, as the consequences might be negative. In assessing maturity, decision makers can determine at decision points if the knowledge base is good enough to move forward to the next step in the jet engine component design, if there is need for rework, and what specific areas need to be improved. Decision makers can divert and focus resources to areas of importance due to, for instance, too low maturity levels.

Knowledge maturity is a way to - using a criteria scale that prescribes the knowledge needed at each level - help development teams assess and visualise how well they know what they know, and subsequently, what they need to know. This thesis explores the feasibility of using knowledge maturity as a way of supporting knowledge engineering in the context of a development process in aeronautics.

Place, publisher, year, edition, pages
Luleå: Luleå tekniska universitet, 2007. 39 p.
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757 ; 2007:64
Keyword
Knowledge Engineering, Knowledge Maturity, Functional Product Development, Decision Making
National Category
Other Mechanical Engineering
Identifiers
urn:nbn:se:bth-12101 (URN)
Opponent
Supervisors
Projects
Faste Laboratory
Funder
VINNOVAEU, FP7, Seventh Framework Programme
Available from: 2016-06-17 Created: 2016-06-16 Last updated: 2016-06-17Bibliographically approved

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