We study the product development process in an artificial firm using two different incentive schemes (market share/production costs vs. life cycle return). In the product development process, we compare a trial and error search to the House of Quality approach. In our study, we focus on tactical decision making within a stable environment, given resources (production function) and knowledge base. The knowledge base is represented by neural networks which are trained on the basis of prototype data. This knowledge is then used in the product development process. We demonstrate, how production and marketing agents coordinate their actions in order to produce optimal products with respect to their incentive schemes. Our simulation shows that coordinating incentive schemes increase the performance of the firm. For a given incentive scheme, the House of Quality approach always outperformed the trial and error search. An interesting feature of the HoQ approach lies in the fact that product improvement is considerably faster compared to the alternative search strategy. (author's abstract) / Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:epub-wu-01_1a9 |
| Date | January 1999 |
| Creators | Mild, Andreas, Natter, Martin, Trcka, Michael, Feurstein, Markus, Merz, Christian, Taudes, Alfred, Dorffner, Georg |
| Publisher | SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Paper, NonPeerReviewed |
| Format | application/pdf |
| Relation | http://epub.wu.ac.at/176/ |
Page generated in 0.0024 seconds