Influencing Factors of Enterprise Intelligent Manufacturing Based on the Three Stages of Intelligent Manufacturing Ecosystems

Introduction

With the deep integration of information technology, manufacturing technology, and intelligent technology, as an advanced manufacturing process, intelligent manufacturing has become a novel path of intelligent transformation and upgradation of the global manufacturing industry. The level of intelligent manufacturing is regarded as an important indicator for measuring the core competitiveness of enterprises. The level of enterprise intelligent manufacturing is related to the stage of intelligent manufacturing development. The strategic objectives of different development stages have different requirements for the intelligent manufacturing level. Therefore, the main factors influencing enterprise intelligent manufacturing in different development stages must be explored, the degree of influence must be clarified, and an action path of different factors affecting the development of enterprise intelligent manufacturing needs to be developed. In addition, decision-making suggestions and reference for the development of enterprise intelligent manufacturing must be provided, which is important to improve the level of enterprise intelligent development and the national strategies for manufacturing power.

Recently, manufacturing industries of various countries have suggested national strategies and plans to improve the development of the traditional manufacturing industry using information technology, for example, “industry 4.0” in Germany, “national strategic plan for advanced manufacturing industry” in the United States, “industry 2050 strategy” in the United Kingdom, and “made in China 2025” in China. These strategic plans are advanced manufacturing development strategies with intelligent manufacturing technology as the core. Meanwhile, researchers are also paying close attention to the research and development of intelligent manufacturing in manufacturing enterprises. Wik et al. illustrated that the ability of manufacturing enterprises to develop intelligent factories is related to the production system, application field, and application technology of enterprises based on the intelligent factory solutions of South Korea and Sweden and their digital-related strategies (Wiktorsson et al., 2018). Zhou et al. showed that Chinese manufacturing enterprises can develop their own intelligent manufacturing capability upgrading path, according to their own capabilities and industry characteristics (Zhou et al., 2019). He et al. proposed a multi-scale integrated intelligent manufacturing model for the chemical industry and discussed the key technologies associated with the interconnected chemical industry (He et al., 2020). Ma et al. proposed a data-driven intelligent manufacturing framework based on the demand response of energy-intensive industries (Ma et al., 2020).

In terms of influencing factors of intelligent manufacturing, Su and Yang studied the influencing factors of intelligent transformation and the upgrading of manufacturing enterprises based on grounded theory (Su and Yang, 2018). Meng and Zhao focused on the factors that affect the development of traditional manufacturing to intelligent manufacturing (Meng and Zhao, 2018). Liu et al. found that external factors such as industrial chain factors, development factors, and capital factors have an important impact on enterprise intelligent manufacturing based on the SVR model (Liu et al., 2017). Based on knowledge acquisition and motivation, Stadnicka et al. studied human factors in an intelligent manufacturing system (Stadnicka et al., 2019). Oliff et al. proposed a human–computer interaction and collaboration framework for intelligent manufacturing that integrates human knowledge to study the relationship between robots and operators to better understand human impact on the production process (Oliff et al., 2018). Owusu A. explored the determinants of Cloud BI adoption among Ghanaian small-medium enterprises (Owusu, 2020).Yu et al. studied the impact of government subsidies on the intelligent transformation of new energy vehicle enterprises (Yu et al., 2020).