An implementation for Smart Manufacturing Information System for an industrial practice survey

Recently, countries around the world have put forward the architecture of Smart Manufacturing System (SMS), such as Reference Architecture Model Industry (RAMI), Smart Manufacturing Ecosystem, Industrial Internet Reference Architecture (IIRA), Cyber Physical Systems Frame, and Industrial Value Chain Reference Architecture (IVRA). A review of published literature in this field shows that scholars research on smart manufacturing centres on a part of the production system. However, there is little research on top-level design from the perspective of the overall system to outline most of the elements covered by enterprises in the implementation of smart manufacturing process and the relationship between them. Even though, there is not much research on more specific and further in-depth application scenarios for Smart Manufacturing Information System (SMIS).

To be specific, there is little research on the detailed step-by-step implementation steps of top-level planning and design for the implementation of smart manufacturing system in enterprises. To address this, Shanghai Jiao Tong University scientists, Dr. Xianyu Zhang and Professor Xinguo Ming proposed a general reference architecture for SMIS through a detailed survey of several enterprises in the information dimension of smart manufacturing. There work is currently published in the research journal Computer & Industrial Engineering.

In their approach, the framework, application scenario and implementation path of the smart manufacturing information system were studied by combining the system engineering idea of top-level design with the method of enterprise specific case validation, which combines theory with practice. Then, the implementation path of SMIS was deduced by connecting of the integrated subsystems of SMIS. Finally, a comprehensive case study for SMIS in automobile manufacturing industry was undertaken. The authors reported that an implementation path for SMIS from case studies was proposed to integrate various subsystems. The team noted that the proposed approach could provide some guidance for enterprises to implement smart manufacturing from each modular of SMIS.

In summary, the study presented a novel approach coined with the aim of developing an implementation path for SMIS from case studies which were proposed to integrate various subsystems. In other words, by analysing the hierarchical structure of smart manufacturing system (SMS), the researchers here put forward the concept and connotation of SMIS. Remarkably, the SMIS’ reference general architecture, related reference subsystems, and implementation developed herein, can be used as a guidance for Industry and government in designing, planning, setting up and implementing SMIS. In a statement to Advances in Engineering, Dr. Xianyu Zhang and Professor Xinguo Ming further highlighted that based on the existing smart manufacturing system framework, their approach has some complementary value. The authors also added that their approach provides some guidance for enterprises to gradually carry out smart manufacturing system from the perspective of information technology step by step.

The research group of Dr. Xianyu Zhang and Professor Xinguo Ming has established good cooperative relations with large domestic manufacturing enterprises in Shanghai, such as aerospace, shipbuilding, marine equipment, automobile, household appliances, new energy and other industries. Their team has been committed to the research in the fields of smart manufacturing, industrial big data, industrial Internet, and industrial artificial intelligence in manufacturing industry, and will continue to contribute to the digital transformation of China’s manufacturing enterprises. At the same time, their research achievements in smart manufacturing and industrial digital innovation have been highly praised by their peers.

Acknowledgment

The authors would like to thank Producer Service Development Innovation Center of Shanghai Jiao Tong University, Shanghai Research Center for industrial Informatics, and Shanghai Key Lab of Advanced manufacturing Environment for the funding support to this research.

Funding

This work was supported by the National Natural Science Foundation of China [grant number 71632008]; Major Project for Aero engines and Gas turbines [grant number 2017-I-0007-0008, grant number 2017-I-0011-0012]; and Transformation and Upgrading of Industry in 2017 (China Manufacturing 2025) [grant number ZL35060009002].