G. Burchill et Ch. Fine, TIME VERSUS MARKET ORIENTATION IN PRODUCT CONCEPT DEVELOPMENT - EMPIRICALLY-BASED THEORY GENERATION, Management science, 43(4), 1997, pp. 465-478
Citations number
44
Categorie Soggetti
Management,"Operatione Research & Management Science","Operatione Research & Management Science
In collaboration with industry partners, a normative model of the prod
uct concept decision process was developed, supported with tools and t
echniques, and codified as a decision support process for product deve
lopment teams. This process (Concept Engineering) was then introduced
into a number of product development teams in different companies. A c
omparative analysis of actual product concept development activities,
with and without the use of Concept Engineering, was conducted. All of
the observed teams viewed time to market as a critical measure of the
ir success. However, the development processes differed significantly
depending on whether relatively more emphasis was placed on time or ma
rket considerations. Key variables associated with the product concept
development decision process and time-to-market dynamics were identif
ied and a theory of the concept development process was developed usin
g the inductive system diagram technique, a research methodology devel
oped in the course of this work. We believe this work contributes to t
he operations management literature in three ways. First, it introduce
s a very detailed, structured decision process for product concept dev
elopment, enhancing the literature on Quality Function Deployment (QFD
). Second, it presents a theory of product concept development that ca
n improve understanding of success and failure in product concept deve
lopment. Third, this work develops new methodology (Inductive Systems
Diagrams) for field work in operations management. This methodology ma
rries the grounded theory methods, familiar to sociologists with causa
l-loop modeling familiar to systems dynamicists, yielding a rigorous t
ool for systematically collecting, organizing, and distilling large am
ounts of field-based data.