Learning in the artificial factory

We study the effects of various incentive schemes on the learning behavior of teams in an artificial factory. Modeling the new product development process, we demonstrate, how production and marketing agents learn to coordinate their actions in order to produce the optimal product with respect to their incentive schemes. As a coordinating mechanism between marketing and production, we use the House of Quality Framework of H.J.R. Hauser and D. Clausing (1988). The House of Quality methodology, which is used by real firms, contains important information from marketing and production. It is a procedure that facilitates the search for new, promising (from market perspective), and feasible products (from a production/design perspective). We found that the House of Quality approach yields higher life cycle returns than the traditional search for new products, especially for a low number of search steps. This is an important finding recommending the application of the House of Quality since the number of search steps directly influences time to market. Thus, minimizing the number of steps could be an important competitive advantage in today's fast moving consumer markets.