Knowledge-based decision making for the technology competency analysis of manufacturing enterprises

Bölükbaş U., Güneri A. F.

APPLIED SOFT COMPUTING, vol.67, pp.781-799, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 67
  • Publication Date: 2018
  • Doi Number: 10.1016/j.asoc.2017.11.023
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.781-799
  • Keywords: Technology management, Technology competency, Decision making based on knowledge, Cluster analysis, Small and medium-sized enterprises, INNOVATION ADOPTION, MANAGEMENT, PERFORMANCE, SERVICE, MODEL, CAPABILITIES, INDUSTRIES, FRAMEWORK, DYNAMICS, QUALITY
  • Yıldız Technical University Affiliated: Yes


Innovation, technology and management competency are vital for determining capabilities of enterprises to compete in the sector. These dimensions are very critical and important for innovative researches and development of enterprises. In this study, a new decision making method called decision making based on knowledge (DeBK) and cluster analysis are used to analyse the decision making process and evaluate the performances of manufacturing enterprises in Istanbul, Turkey. The performance evaluation model is structured based on the six main criteria and knowledge of information which is defined by expert evaluations and literature review. Small and medium-sized enterprises are analysed with respect to technology evaluation surveys. This paper suggests an analytical approach for managerial decision making. DeBK method is used to evaluate importance of the information and decision criteria. Multivariate statistical clustering analysis is applied on the appropriate model data to evaluate the enterprises. As a result, technology competency levels of the enterprises are determined in five different groups by the cluster analysis. Different clustering ways represent approximately same results for technology evaluations of the enterprises, depending on the performance clusters. (C) 2017 Elsevier B.V. All rights reserved.