Innovation: A data-driven approach

author = "Andrew Kusiak",

title = "Innovation: A data-driven approach",

journal = "International Journal of Production Economics",

volume = "122",

number = "1",

pages = "440--448",

year = "2009",

note = "Transport Logistics and Physical Distribution; Interlocking of Information Systems for International Supply and Demand Chains Management; ICPR19",

ISSN = "0925-5273",

DOI = "

doi:10.1016/j.ijpe.2009.06.025",

URL = "

http://www.sciencedirect.com/science/article/B6VF8-4WKTWVR-2/2/73fdc80f743b54ffb2f1449b44d434cd",

keywords = "genetic algorithms, genetic programming, Innovation science, Data mining, Innovation rules, Innovation framework, Evolutionary computation",

abstract = "A newly introduced product or service becomes an innovation after it has been proven in the market. No one likes the fact that market failures of products and services are much more common than commercial successes. A data-driven approach to innovation is proposed. It is a natural extension of the system of customer requirements in terms of their number and type and the ways of collecting and processing them. The ideas introduced in this paper are applicable to the evaluation of the innovativeness of planned introductions of design changes and design of new products and services. In fact, blends of products and services could be the most promising way of bringing innovations to the market. The most important toll gates of innovation are the generation of new ideas and their evaluation. People have limited ability to generate and evaluate a large number of potential innovation alternatives. The proposed approach is intended to evaluate many alternatives from a market perspective.",