Recently, many manufacturers have recalled their products owing to quality issues. It is increasingly difficult to determine the cause of quality issues because of the complexity of the supply chain. Thus, it is essential to share manufacturing information throughout the product life cycle. However, small and medium-sized enterprises (SMEs) often lack the necessary infrastructure and information systems. This research proposes an open-source system allowing the 3D visualization of production history and simulation results. The production history includes products' time stamps and inspection results, defect information, and a status of each facility. This information is then used to construct a product workflow and simulation model. Further, it is possible to compare simulation results for up to three alternative scenarios. The system is developed using open-source libraries for easy diffusion and application to SMEs in the automobile industry. A method for the implementation of this system to Korean auto parts companies is introduced.

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In recent years, studies on smart manufacturing using ICT have increased significantly, and much attention has been paid to CPSs, IoT, sensors, industrial data analytics and artificial intelligence as core technologies. In particular, CPSs are one of the core technical elements in smart manufacturing, and a variety of studies on CPSs are underway. Accordingly, a large number of technical developments and applications related to intelligent and autonomous facilities, the prediction of factory operation, machinery factories and quality issues and proactive responses have been made. The new paradigm in smart manufacturing can be defined as customisation, connectivity and collaboration. The goal of smart manufacturing is to perform right decision making autonomously by connecting intelligent design, the efficient manufacture of customized products in accordance with the analysis of market and customer demand, sales, the user's use and the service sector. The paper presents the concept, framework, configuration and implementation method of a CPS that is performed intelligently based on the IoT, smart sensors and industrial data analytics from manufacturing preparation and execution.