An agent-based investigation into the new product development capability View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2007-09

AUTHORS

Andreas Mild, Alfred Taudes

ABSTRACT

The capability to bring products to market which comply with quality, cost and development time goals is vital to the survival of firms in a competitve environment. New product development comprises knowledge creation and search and can be organized in different ways. In this paper, we study the performance of several alternative organizational models for new product development using a model of distributed, self-adapting (learning) agents. The agents (a marketing and a production agent) are modelled via neural networks. The artificial new product development process analyzed starts with learning on the basis of an initial set of production and marketing data about possible products and their evaluation. Subsequently, in each step of the process, the agents search for a better product with their current models of the environment and, then, refine their representations based on additional prototypes generated (new learning data). Within this framework, we investigate the influence of different types of new product search methods and generating prototypes/learning according to the performance of individual agents and the organization as a whole. In particular, sequential, team-based Trial & Error and House of Quality guided search are combined with prototype sampling methods of different intensity and breadth; also, the complexity of the agents (number of hidden units) is varied. It turns out that both the knowledge base and the search procedure have a significant impact on the agents' generalization ability and success in new product development. More... »

PAGES

315-331

References to SciGraph publications

  • 1995-10. Computational and mathematical organization theory: Perspective and directions in COMPUTATIONAL AND MATHEMATICAL ORGANIZATION THEORY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10588-006-9012-5

    DOI

    http://dx.doi.org/10.1007/s10588-006-9012-5

    DIMENSIONS

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