Decision support for technology transfer using fuzzy quality function deployment and a fuzzy inference system

Abstract

Technology transfer plays an essential role in developing an organization’s capabilities to perform better in the market. Several protocols are defined for technology transfer. One of the main techniques in technology transfer is licensing, which significantly impacts profit and income. This study intends to develop a decision framework that integrates both a Fuzzy Inference System (FIS) and a two steps Fuzzy Quality Function Deployment (F-QFD) to assist an organization in selecting a licensor. To illustrate the decision framework’s performance, it has been implemented in an Iranian lubricant producer to select the best licensor among the 13 targeted companies. A complete product portfolio, brand image enhancement, increasing the market share of the high-value products, and improving the technical knowledge of manufacturing products were identified as the most important expectations of the licensees. A sensitivity analysis for the recommended framework has been conducted. For doing so, 27 rules of the FIS were categorized into four group and then changed. The results are compared using the Pearson correlation coefficient. Inference rules detect unconventional changes, while logical changes are appropriately considered.