Jeongcheol Lee

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.

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.