Purpose The purpose of this paper is to examine the strategic response to Industry 4.0 for Chinese automotive industry and to identify the critical factors for its successful implementation. Design/methodology/approach A technological, organizational, and environmental framework is used to build the structural models, and statistical tools are used to validate the model. The data analysis helps to determine which factors have impact on the strategic response and whether their relationships are positive or negative. Interpretive structural modeling method is applied to further analyze these derived factors for depicting the relationship. Findings The result shows that company size and nature do not increase the use of advanced production technologies, while other factors have positive impacts on improving the technology adoption among the companies surveyed. Practical implications A strategic response to Industry 4.0 not only helps in improving organizational competitiveness, but it also has social and economic implications. For this purpose, empirical data are collected to measure the understanding of Industry 4.0 in the Chinese automotive industry. Originality/value Despite the fact that the Chinese Government has proposed the “Made in China 2025” approach as a way to promote smart manufacturing, little empirical evidence exists in the literature validating company’s perspective toward Industry 4.0. This paper is to fill the research gap.

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Purpose With the new generation Industry 4.0 coming, as well as globalization and outsourcing, products are fabricated by different parties in the distributed manufacturing network and enterprises face the challenge of consistent planning of semi-finished product in each manufacturing process in different geographical locations. The purpose of this paper is to propose a real-time operation planning system in the distributed manufacturing network to intelligently control/plan the manufacturing networks. Design/methodology/approach The feature of the proposed system is to model and simulate large distributed manufacturing networks to streamline the mechanical and production engineering processes with radio frequency identification (RFID) technology, which can keep track of process variants. To deal with concurrency and synchronization, the hierarchical timed colored Petri net (HTCPN) formalism for modeling is selected in this study. This method can help to model graphically and test the discrete events of concurrent operations. Fuzzy inference system can help for knowledge representation, so as to provide knowledge-based decision assistance in distributed manufacturing environment. Findings In this proposed system, there are two main sub-systems: one is the real-time modeling system, and the other one is intelligent operation planning system. These two systems are not parallel in the whole systems while the intelligent operation planning system should be embedded in any stage of the real-time modeling system as needed. That means real time modeling system provides the holistic structure of the studied distributed manufacturing system and realize real-time data transfer and information exchange. At the same time the embedded intelligent operation planning system fulfill operation plan function. Originality/value This new intelligent real-time operation system realizes real-time modeling with RFID-based HTCPN and smart fuzzy engine to fulfill intelligent operation planning which is highly desirable in the environment of Industry 4.0. The new intelligent manufacturing architecture will highly reduce the traditional planning workload and improve the planning results without manual error interference. The new system has been applied in a practical case to demonstrate its feasibility.

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