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Open AccessArticle

A Smartness Assessment Framework for Smart Factories Using Analytic Network Process

Department of Industrial Engineering, Seoul National University, Seoul 08826, Korea
School of Industrial Engineering, Purdue University, West Lafayette, IN 47907, USA
Department of Industrial & Management Engineering, Kyonggi University, Suwon 16227, Korea
Author to whom correspondence should be addressed.
Academic Editors: Wei Dei Solvang, Kesheng Wang, Bjørn Solvang, Peter Korondi and Gabor Sziebig
Sustainability 2017, 9(5), 794;
Received: 19 March 2017 / Revised: 25 April 2017 / Accepted: 6 May 2017 / Published: 10 May 2017
(This article belongs to the Special Issue Sustainability in Manufacturing)
The so-called smart factory is a novel paradigm that is rapidly gaining ground in scenarios for factories of the future. Many manufacturing companies try to raise the level of smartness by considering a number of aspects related to the smart factory. However, there is a lack of field-oriented systematic research to help them fit the interest of industry for promoting interest and diffusion of smart factory. Moreover, it is still difficult to assess whether the vision of the future factory that incorporates information and communication technologies is implemented. Therefore, in this study, we propose a smartness assessment framework for smart factories which is based on the concept of operation management so as to be easy to make manufacturing companies to understand and apply. The framework is composed of evaluation criteria and sets the weightings of the criteria using analytic network processes. From a case study based on 20 small and medium-sized manufacturing enterprises, the effectiveness of the proposed framework has been verified. View Full-Text
Keywords: smartness assessment; smart factory; analytic network process smartness assessment; smart factory; analytic network process
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Lee, J.; Jun, S.; Chang, T.-W.; Park, J. A Smartness Assessment Framework for Smart Factories Using Analytic Network Process. Sustainability 2017, 9, 794.

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