A Maturity Model for Diagnosing the Capabilities of Smart Factory Solution Suppliers and Its Pilot Applicationopen access
- Authors
- Cha, Cheol-Won; Lee, Jeongcheol; Jun, Sungbum; Kang, Keumseok; Chang, Tai-Woo
- Issue Date
- Dec-2023
- Publisher
- MDPI
- Keywords
- capability diagnosis model; institutionalization; maturity model; smart factory; supplier
- Citation
- Systems, v.11, no.12, pp 1 - 20
- Pages
- 20
- Indexed
- SSCI
SCOPUS
- Journal Title
- Systems
- Volume
- 11
- Number
- 12
- Start Page
- 1
- End Page
- 20
- URI
- https://scholarworks.dongguk.edu/handle/sw.dongguk/20822
- DOI
- 10.3390/systems11120569
- ISSN
- 2079-8954
2079-8954
- Abstract
- For the successful and sustainable deployment and diffusion of smart factories, both the capabilities of the adopters who operate the factories and the capabilities of the suppliers who supply information technology and equipment play very important roles. However, since the existing models for diagnosing the capabilities of smart factories are mainly focused on evaluating the capabilities of the manufacturing companies themselves, such as technological capabilities and digital transformation, there are not many models that diagnose the supply capabilities of suppliers from the perspective of demand companies. Unlike models that diagnose the level of smart factories, when diagnosing the capabilities of suppliers, various factors such as supply experience and management capabilities must be comprehensively evaluated in addition to the capabilities of the company itself. Therefore, this study proposes a new model to diagnose the capabilities of suppliers from the perspective of adopters who want to build smart factories and verifies the validity of the model by applying the model for a pilot diagnosis for 32 suppliers. In addition, based on the survey results obtained from both adopters and suppliers participating in the pilot diagnoses, this study proposes an institutionalization plan for capability diagnosis. © 2023 by the authors.
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- Appears in
Collections - College of Engineering > Department of Industrial and Systems Engineering > 1. Journal Articles

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