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A Smartness Assessment Framework for Smart Factories Using Analytic Network Process

Author

Listed:
  • Jeongcheol Lee

    (Department of Industrial Engineering, Seoul National University, Seoul 08826, Korea)

  • Sungbum Jun

    (School of Industrial Engineering, Purdue University, West Lafayette, IN 47907, USA)

  • Tai-Woo Chang

    (Department of Industrial & Management Engineering, Kyonggi University, Suwon 16227, Korea)

  • Jinwoo Park

    (Department of Industrial Engineering, Seoul National University, Seoul 08826, Korea)

Abstract

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.

Suggested Citation

  • Jeongcheol Lee & Sungbum Jun & Tai-Woo Chang & Jinwoo Park, 2017. "A Smartness Assessment Framework for Smart Factories Using Analytic Network Process," Sustainability, MDPI, vol. 9(5), pages 1-15, May.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:5:p:794-:d:98112
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    References listed on IDEAS

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    1. Saaty, Thomas L., 1990. "How to make a decision: The analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 48(1), pages 9-26, September.
    2. Gyusun Hwang & Jeongcheol Lee & Jinwoo Park & Tai-Woo Chang, 2017. "Developing performance measurement system for Internet of Things and smart factory environment," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2590-2602, May.
    3. Gunasekaran, A. & Patel, C. & McGaughey, Ronald E., 2004. "A framework for supply chain performance measurement," International Journal of Production Economics, Elsevier, vol. 87(3), pages 333-347, February.
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    Cited by:

    1. Jian Xu & Jae-Woo Sim, 2018. "Characteristics of Corporate R&D Investment in Emerging Markets: Evidence from Manufacturing Industry in China and South Korea," Sustainability, MDPI, vol. 10(9), pages 1-18, August.
    2. Jong Hun Woo & Haoyu Zhu & Dong Kun Lee & Hyun Chung & Yongkuk Jeong, 2021. "Assessment Framework of Smart Shipyard Maturity Level via Data Envelopment Analysis," Sustainability, MDPI, vol. 13(4), pages 1-27, February.
    3. Elibal, Kerem & Özceylan, Eren, 2022. "Comparing industry 4.0 maturity models in the perspective of TQM principles using Fuzzy MCDM methods," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    4. Melissa Liborio Zapata & Lamia Berrah & Laurent Tabourot, 2019. "Is a digital transformation framework enough for manufacturing smart products? The case of Small and Medium Enterprises," Post-Print hal-02389603, HAL.
    5. Rafael, Lizarralde Dorronsoro & Jaione, Ganzarain Epelde & Cristina, López & Ibon, Serrano Lasa, 2020. "An Industry 4.0 maturity model for machine tool companies," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    6. Brodny, Jarosław & Tutak, Magdalena, 2023. "Assessing the level of digital maturity in the Three Seas Initiative countries," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    7. Zhexuan Zhou & Yajie Dou & Jianbin Sun & Jiang Jiang & Yuejin Tan, 2017. "Sustainable Production Line Evaluation Based on Evidential Reasoning," Sustainability, MDPI, vol. 9(10), pages 1-14, October.
    8. Rimalini Gadekar & Bijan Sarkar & Ashish Gadekar, 2022. "Key performance indicator based dynamic decision-making framework for sustainable Industry 4.0 implementation risks evaluation: reference to the Indian manufacturing industries," Annals of Operations Research, Springer, vol. 318(1), pages 189-249, November.
    9. Diogo Rodrigues & Radu Godina & Pedro Espadinha da Cruz, 2021. "Key Performance Indicators Selection through an Analytic Network Process Model for Tooling and Die Industry," Sustainability, MDPI, vol. 13(24), pages 1-20, December.
    10. Bhatia, Purvee & Diaz-Elsayed, Nancy, 2023. "Facilitating decision-making for the adoption of smart manufacturing technologies by SMEs via fuzzy TOPSIS," International Journal of Production Economics, Elsevier, vol. 257(C).

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