An agent-based investigation into the new product development capability

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.Keywords New product development . Knowledge management . Agent-based model A. Mild ( ) . A. Taudes

Knowledge-based New Product Development: fostering innovation through knowledge co-creation

“…Similarly, Leonard (1998, p.211) contends that "[o]ne of the most critical engines of renewal for companies is NPD". Indeed, the capability to bring products to market that comply with quality, cost and development time goals is vital to the survival of firms in a competitive environment (Mild and Taudes, 2007). Some authors have shown that the resource-based view is well suited to explain a firm's success in NPD (Dutta et al, 1999;Natter et al, 2001;Verona, 1999); the knowledge-based view is equally adequate.…”

“…Some authors have shown that the resource-based view is well suited to explain a firm's success in NPD (Dutta et al, 1999;Natter et al, 2001;Verona, 1999); the knowledge-based view is equally adequate. NPD requires knowledge creation and searching, and can be organised in different ways (Mild and Taudes, 2007). According to Bell et al (2002, p.82), product development is "a particularly salient area for organisational learning inquiry for a number of reasons": it is often a team-based pursuit, it requires a high degree of interfunctional coordination, and it is frequently project based.…”

Usage of Agent-Based Modeling and Simulation in Marketing

“…Reference [47] one of the papers in this category examined network externalities and network structure for new product development. Reference [48] covers the capability of firm for new product development. Reference [49] investigated the effects of incentives and organizational structure on new product development.…”

Putting the organization back into computational organization theory: a complex Perrowian model of organizational action

“…In addition, Aldrich (1979) incorporated the strategic choice perspective in his modified population ecological perspective, and the idea of 'adaptation by choice' has been addressed by COT studies (Carley 1995(Carley , 2002Carley and Svoboda 1996;Handley and Levis 2001;Lin and Hui 1997;Mild and Taudes 2007;Perdu and Levis 1998;Turner et al 2002). Since Porter's perspective appears to be both so pervasive and accommodating, it was adopted for the framework of our simulation of organizations: a multi-agent simulation, with each firm represented by an agent (simulating the characteristics of a top management team) and located on a two-dimensional landscape (simulating an industry), not unlike Epstein and Axtell's (1996) individual-level landscape-based simulation of human societies, where agents were allowed to "move" incrementally across their landscapes in each simulation iteration.…”