A systematic way of identifying and forecasting technological reverse salients using QFD, bibliometrics, and trend impact analysis: A carbon nanotube biosensor case

Abstract

Experts have more difficulty identifying reverse salients in R&D because of increasing technological complexity and a shortened technology lifecycle. As an alternative, we suggest a new and systematic method of identifying and forecasting reverse salients using QFD (quality function deployment), bibliometric analysis, and TIA (trend impact analysis). QFD allows users to systematically identify and prioritize reverse salients. An integration of QFD, bibliometric analysis, and TIA makes it possible to specify key performance indicators of reverse salient in order to identify the performance gap between current and market-required performance and to make a probabilistic forecast about when reverse salients will be corrected. Our method will help managers identify a top priority reverse salient, forecast its future, and thus make better R&D decisions with regard to market requirements. A carbon nanotube biosensor technology is used as an example.