Research on influencing factors and multiple driving paths of intelligent transformation in China’s manufacturing industry

Abstract

As the internal requirement of the development of China’s traditional manufacturing industry, intelligent transformation has gradually become the main goal of China’s regional manufacturing transformation. From the perspective of manufacturing value chain, this paper proposes a conceptual model of intelligent transformation of manufacturing industry. Taking 22 sample cities as cases, the paper uses fuzzy set qualitative comparative analysis method to identify the necessary factors of intelligent transformation of manufacturing industry and the configuration analysis of each factor combination, and then obtains three realization paths to promote intelligent transformation. The results show that intelligent technology innovation and intelligent industry investment are the necessary conditions for the intelligent transformation of manufacturing industry, and there are three configuration paths: collaborative R&D and processing, external factor driven and value chain climbing, which promote the intelligent transformation of manufacturing industry. This paper enriches the research on intelligent transformation of manufacturing industry from the perspective of value chain, and uses the qualitative comparative analysis method of fuzzy set to provide countermeasures and suggestions for the direction and path of intelligent transformation of manufacturing industry.

Keywords

Manufacturing industry value chain intelligent transformation QCA method

1. Introduction

China’s manufacturing industry as the main force to promote the development of industrial economy, its positive and rapid development not only promotes the steady development of the national economy, but also provides a favorable basic guarantee for China’s economy in the middle of the industrialization process. With the continuous development of China’s manufacturing industry, many problems are increasingly exposed. In order to achieve the strategic goal of manufacturing power, and combined with China’s national conditions, the Chinese government put forward the first ten-year action program “made in China 2025” to implement the strategy of manufacturing power. As the main direction of the transformation and upgrading of manufacturing industry, intelligent manufacturing actively promotes the development of traditional manufacturing to intelligent manufacturing, which has become an important strategic task for China to build a manufacturing power [1]. From the micro perspective, the development of manufacturing industry is inseparable from the development of manufacturing enterprises. According to Porter’s value chain theory, not every link in enterprise activities can create value. Only those strategic links can really bring value-added to enterprises. Based on this logic, the strategic key links in the value chain of manufacturing industry that can truly create value are often concentrated in some parts. Therefore, it is of great significance to analyze the relevant factors of intelligent transformation of manufacturing industry from the perspective of value chain, identify the leading factors and the configuration relationship and implementation path of each factor to the intelligent transformation, so as to implement the strategy of manufacturing power and improve the intelligent development level of manufacturing industry in China.

Since the concept of “made in China 2025” was put forward for the first time in 2014, a series of policies and measures to promote the development of intelligent manufacturing have been put forward one after another, such as “guidelines for the construction of national intelligent manufacturing standard system (2018 Edition)”, “Intelligent manufacturing development plan (2016–2020)”, and “guiding suggestions on promoting the deep integration of artificial intelligence and real economy”. China’s investment in intelligent manufacturing has been rising steadily since 2014, and the average annual investment from 2017 to 2019 is more than 30 billion. According to the prediction of China Commercial Industry Research Institute, the output value of China’s intelligent manufacturing equipment will reach 2090 billion in 2020. According to their own regional characteristics and economic advantages, China’s provinces and cities also vigorously carry out the intelligent transformation of manufacturing industry; Guangdong province takes manufacturing as the development goal, and plans to enter the comprehensive intelligent stage of manufacturing industry by 2025. Shanghai takes the national policy as the guidance, and takes intelligent manufacturing as the main direction of the transformation from “made in Shanghai” to “intelligent in Shanghai”; Jiangsu Province actively promotes the development of intelligent manufacturing with 10 provincial intelligent manufacturing demonstration zones, 50 upgraded intelligent manufacturing demonstration plants and 1000 intelligent workshops as the target points; Chongqing strives to make the output value of intelligent manufacturing related industries exceed 40 billion yuan in 2022. All provinces are actively carrying out the transformation of intelligent manufacturing industry, which fully reflects the far-reaching significance of China’s intelligent development.

2. Research background

In web of science database, there are about 231 articles with the keywords of intelligent manufacturing and manufacturing transformation and upgrading. From the analysis of the existing literature, it can be found that intelligent manufacturing and intelligent transformation have received extensive attention in various research fields. Intelligent manufacturing has experienced the transition from digitization as the origin point, networking as the advanced level, and finally to the era of intelligent manufacturing [2]. Therefore, the theoretical expansion of intelligent concept is gradually carried out in academic circles [3, 4]. As the application of advanced intelligent technology, intelligent manufacturing can accelerate the manufacturing speed of new products, the dynamic response of personalized product demand and the real-time optimization of production and supply chain network [5, 6]. Intelligent manufacturing covers the latest information and communication technology and intelligent facilities, which provides positive significance for the construction and development of organizations [6]. At the same time, data modeling and analysis are also an important part of intelligent manufacturing. While processing real-time data, intelligent manufacturing provides convenience for large capacity data processing [7]. In the whole world, while actively seizing the strategic commanding point of intelligent manufacturing, countries also build intelligent manufacturing systems in line with their own development. Relevant scholars also build China’s intelligent manufacturing architecture system from three dimensions of value dimension, technology dimension and organization dimension [8]. The intelligent manufacturing architecture of Japan, the United States and other developed countries focuses more on high-end links and high-end industries, while China’s architecture is inclined to the overall planning of the transformation and upgrading of manufacturing industry [9], and the research on the measurement of influencing factors and intelligent level in the process of intelligent transformation has also become the research focus of domestic scholars [10, 11, 12]. In the guide to the construction of national intelligent manufacturing standard system, the architecture of intelligent manufacturing system is sorted out from three dimensions, namely life cycle, system level and intelligent characteristics. The life cycle includes a series of interrelated value creation activities such as design, production, logistics, sales and service. At present, the literature on the value chain and division of labor is also increasing with the transformation of manufacturing industry. The intelligence is the further development of “Internet $+$ ”, which can penetrate into the various links of the enterprise value chain to achieve efficiency improvement [13]. Through using artificial intelligence industrial robot’s in-depth analysis, finds that artificial intelligence reduces trade costs and promotes technology. Innovation and optimization of resource allocation can improve a country’s participation and division status in the global value chain [14]. At the same time, intellectualization can also increase the division status of the global value chain by improving the export value-added rate and technical complexity [15].

By combing the existing literature, we can see that domestic and foreign scholars have carried out a wealth of research on the intelligent transformation of manufacturing industry from different angles, and some scholars have carried out specific analysis on the influencing factors of intelligent transformation, but both qualitative research and quantitative research are mostly independent analysis of the impact of various factors, rarely based on the combination of factors. Therefore, based on the perspective of value chain, this paper analyzes the relevant factors affecting the transformation by constructing the conceptual model of intelligent transformation of manufacturing industry, and uses the qualitative comparative analysis method to identify the leading factors influencing the transformation and the configuration relationship between the factors with small case sample data, so as to provide theoretical basis and policy suggestions for the intelligent transformation of China’s manufacturing industry.

3. The influencing factors of intelligent transformation based on value chain

In this study, the connotation of intelligent transformation is defined as the transformation of sustainable development manufacturing mode based on digital and intelligent technology for value-added of industrial value chain. At this stage, the development of intelligent manufacturing industry pays more attention to the systematic intelligence of the whole industrial value chain, which is embodied in intelligent R&D and design, intelligent manufacturing, intelligent technical service and intelligent management. From the perspective of industrial value chain, the purpose of intelligent transformation and upgrading is to improve the value creation ability of each link of enterprise production and operation activities. From the perspective of manufacturing value chain, this paper analyzes the influencing factors of intelligent transformation of manufacturing industry.

3.1 The proposal of influencing factors

From the perspective of global value chain, the transformation and upgrading of manufacturing industry includes four modes: process upgrading, product upgrading, function upgrading and chain upgrading. The upgrading path follows the general evolution law, and finally ends in the upgrading of value chain. The industrial value chain generally includes R&D link, production and processing link and marketing link, and the value-added of each link Capability reflects the overall competitiveness of the industry. In the process of intelligent transformation, the new generation of intelligent manufacturing will reshape the design, manufacturing, service and other aspects of the product life cycle and their integration, giving birth to new technologies, new products, new formats and new models [17]. From the perspective of value chain, the factors that affect the intelligent transformation of manufacturing industry are to realize value-added and promote the rise of value chain through the three dimensions of intelligent products, intelligent production and intelligent services, and through local optimization and overall promotion. Therefore, this study divides the driving factors into three categories: R&D driving factors, production driving factors and marketing service driving factors.

3.1.1 Driving factors of R&D

Intelligent technology innovation. The core of intelligent manufacturing technology is the deep integration of new technologies such as artificial intelligence and manufacturing technology. In the micro aspect, it is the self-organization and self-processing ability of manufacturing enterprises in actual production [18]. The core of intelligent manufacturing technology is the deep integration of new technologies such as artificial intelligence and manufacturing technology. In the micro aspect, it is the self-organization and self-processing ability of manufacturing enterprises in actual production. Intelligent technology innovation is a kind of technology innovation activity in manufacturing industry, which includes the development and application of intelligent manufacturing technology. Increasing the investment of intelligent technology innovation in manufacturing enterprises can effectively reduce the production cost, increase the technical content of products, and improve the added value of products, so as to expand the brand awareness and increase the market competitiveness of enterprises. At the same time, the development of new intelligent technology or the use of mature intelligent manufacturing technology in the product R&D and design stage can improve the intelligent level of product design, shorten the product R&D cycle, and improve the success rate of product R&D. When the industrial strength of manufacturing enterprises reaches a certain scale, the innovation ability changes from introduction and imitation to independent innovation, and promotes the enterprise value chain to the high end, so as to obtain technical and economic benefits. Therefore, as the driving factor of R&D, intelligent technology innovation is an indispensable step in the development of manufacturing industry.

Investment in intelligent industry. From the perspective of value-added, enterprises increase the investment in the intelligent industry, which arouses the high-level desire to dominate and control the value-added links such as technological innovation, brand promotion and after-sales service. At the same time, increasing the investment in each value-added link also effectively promotes the rise of the enterprise value chain [19]. The investment in the intelligent industry is not only reflected in the financial support of equipment and technology, but also includes the investment of personnel capital, which is beneficial to attract high-end talents, optimize the structure of human resources, improve the level and level of human resources, and provide a favorable boost for the intelligent transformation of manufacturing industry.

3.1.2 Driving factors of production

Intelligent infrastructure. It includes all devices related and auxiliary to intelligent manufacturing. As the carrier of intelligent manufacturing, intelligent infrastructure affects the optimization and upgrading of intelligent production process. The goal is to improve production efficiency and product quality, optimize production processes and processes, and reduce the waste of resources in the production process. At the same time, the intelligent infrastructure in production links is increased to replace the traditional labor force with intelligent production, which reduces the allocation of production line personnel and reduces the cost Low production cost, and with the network can link machines and employees, improve the accuracy of production operation.

Efficiency of intelligent industry. In the theory of intelligent industry efficiency and industry life cycle, it is mentioned that when an industry enters the mature stage of development, with the market demand for products tends to be saturated, the market demand for products decreases. In order to achieve sustainable development, enterprises will take measures to improve production efficiency and reduce costs. When the productivity of the leading industry in the industry decreases, the new high-efficiency industry will replace the original industry, resulting in the change of the leading industry; conversely, if the leading industry improves the industrial efficiency to adapt to the market environment, the leading industry will have the potential of sustainable development, and by absorbing new technology and scientific and technological achievements and applying them in the development, it can effectively promote the development of the leading industry Sustainable development.

3.1.3 Driving factors of marketing service

Intelligent market scale. Expanding the market scale can reduce the competitive pressure of manufacturing enterprises in the market environment. In the process of intelligent transformation of manufacturing enterprises, by improving customer satisfaction and market share, the competitiveness of enterprises in the product market and the whole industry can be effectively improved. The manufacturing industry with large market scale has more suppliers, and the customers are more sensitive to the products. As a result, the market competitiveness of the products increases, and then the enterprises will get more customer demand and market trend information, so as to enhance the overall competitiveness of the enterprises.

Intelligent social benefits. The investment of social benefits is conducive to enhance the brand identity and enterprise visibility of manufacturing enterprises, which is mainly reflected in the market environment. While accelerating the intelligent transformation of manufacturing industry, it promotes the market profitability of manufacturing enterprises. At the same time, the increase of social benefits is also the guarantee to promote social stability and expand the overall social and economic benefits.

The above six factors have a certain impact on the intelligent development of manufacturing industry in different links of the value chain, in addition to the promotion of the external factor of national support, which includes not only the input of special funds, but also the guidance of various policies. According to the relevant policies of intelligent manufacturing released in China, in order to solve the problems of lack and lag of intelligent manufacturing standards, the Ministry of industry and information technology and the National Standard Committee jointly issued the “guidelines for the construction of national intelligent manufacturing standard system”, which effectively promoted the development of interconnection, cross industry and cross field standardization. At the same time, the recently released 14th five year plan also clearly puts forward that we should pay attention to high-end manufacturing and intelligent manufacturing, and accelerate the transformation and upgrading of the manufacturing industry around the theme of digitalization and intellectualization.

Table 1
Driving factors and existing literature

Value chain	Factor	Existing research
R&D	Intelligent technology innovation	Zheng Jiliang [20]
	Investment in intelligent industry	Thomas F [3]
Production	Intelligent infrastructure	Liu liang [15]
	Efficiency of intelligent industry
Marketing service	Intelligent market scale	Li Jianxuan [12]
	Intelligent social benefits	Subei [11]
	State support	Zheng Jiliang [20]

3.2 Conceptual model of intelligent transformation

Based on the above analysis, from the perspective of value chain, this paper puts forward seven hypothetical factors affecting the intelligent transformation of manufacturing industry, which are: intelligent technology innovation, intelligent industry investment, intelligent infrastructure, intelligent industry efficiency, intelligent social benefit, intelligent market scale and state support. And this paper builds a conceptual model of intelligent transformation of manufacturing industry based on the perspective of value chain.

Figure 1.

Intelligent transformation of manufacturing industry from the value chain perspective conceptual model.

4. Method and design

4.1 Method

In the 1980s, Charles Larkin, an American sociologist, proposed a qualitative comparative analysis method for small sample case studies. Through the continuous exploration of scholars, qualitative comparative analysis method is widely used in various fields. It mainly studies the complex causal relationship between a certain result and the corresponding factors, and obtains the combination of configuration conditions leading to the result. It has obvious advantages in the small sample of 10–60. At present, clear set analysis, fuzzy set analysis and multi set value analysis are the three main methods of qualitative comparative analysis, and fuzzy set qualitative comparative analysis is the most widely used. Therefore, according to the research objectives and case selection, this paper adopts the fuzzy set qualitative comparative analysis method, and sets the research results and influencing factors as “result variable” and “condition variable” respectively. Then the collected data is calibrated to a number between 0–1 to determine its membership degree. Finally, the fsqca3.0 software is used for analysis, and the corresponding condition combination and corresponding results are obtained.

4.2 Case selection

Based on the case selection of manufacturing center cities, this study takes 22 cities in China from 50 sample cities in the “world intelligent manufacturing center development trend report (2019)” as sample cases, including Shanghai, Shenzhen, Suzhou, Tianjin, Beijing, Chongqing, Foshan, Ningbo, Guangzhou, Nanjing, Wuhan, Hefei, Dongguan, Wuxi, Changsha, Xi’an, Hangzhou and Chengdu Qingdao, Zhengzhou, Yantai, Dalian. The selected cities cover 11 provinces and 3 municipalities directly under the central government in China, which basically reflects the five economic regions of Yangtze River Delta, Pearl River Delta, central region, Bohai rim and southwest. Meanwhile, the sample cities include not only traditional manufacturing centers such as Xi’an, Nanjing and Qingdao, but also new intelligent manufacturing cities such as Suzhou, Tianjin and Foshan. The characteristics and prominence of the selected cases provide sufficient materials for data collection and collation, and 22 sample cities meet the small sample cases specified in the research method of this paper. Therefore, the selected cases are representative for the research of intelligent transformation of manufacturing industry.

4.3 Variable selection and assignment

Since China proposed “made in China 2025”, the development of manufacturing industry to intelligent has experienced continuous exploration and innovation. Not only China has explored the development of intelligent, but also other developed countries in the world have issued a series of guidelines and policies to guide the development of manufacturing industry. Therefore, the result variable selection of this paper is based on the level of Intelligent Manufacturing in the “world intelligent manufacturing center development trend report (2019)”. According to the above analysis, 7 condition variables and 1 result variable are selected for specific analysis. The selection of variables and specific observation values of variables are shown in Table 2 below. The index data in the study mainly come from “China Statistical Yearbook”, “China Industrial statistical yearbook”, “China Science and technology statistical yearbook” and statistical yearbooks of provinces and cities. Other data come from the collection and statistics of network data such as government work report, science and Technology Association report, popular science statistical report and Baidu Index.

Table 2
Influencing factors of intelligent transformation variable description

Variable type	Variable name	Variable observation	Variable label
Result variable	Intelligent Manufacturing	Intelligent manufacturing index	ZZ
Condition variables	Intelligent technology innovation	Number of technicians	ZC
		Number of research institutions
	Intelligent industry investment	Number of intelligent manufacturing industrial parks	ZT
	Intelligent infrastructure	Number of manufacturing enterprises in Digital Workshop	ZJ
	Intelligent industry efficiency	Proportion of intelligent manufacturing output value in traditional manufacturing output value	ZX
	Intelligent industry scale	Number of intelligent products held	ZG
	Intelligent social benefit	Number of employees in intelligent manufacturing	ZS
	State support	Special bonus for intelligent manufacturing	GF
		Government fund on science and technology

Table 3

Fuzzy set calibration data

Sample city name	Result variable	Conditional variables
	ZZ	ZC	ZT	ZJ	ZX	ZG	ZS	GF
Shanghai	1	0.991	0.751	0.421	0.821	0.991	0.961	0.941
Shenzhen	0.981	0.811	0.681	0.421	0.991	0.781	0.961	0.981
Suzhou	0.961	0.781	0.681	0.971	0.861	0.051	0.891	0.131
Tianjin	0.951	0.951	0.751	0.731	0.051	0.441	0.521	0.961
Beijing	0.921	1	0.951	0.531	0.051	0.971	0.311	0.921
Chongqing	0.731	0.951	0.991	0.741	0.821	0.891	0.871	0.171
Foshan	0.871	0.211	0.501	0.421	0.051	0.051	0.801	0.021
Ningbo	0.461	0.271	0.111	0.531	0.051	0.051	0.671	0.101
Guangzhou	0.351	0.941	0.681	0.421	0.051	0.741	0.611	0.481
Nanjing	0.281	0.691	0.871	0.081	0.861	0.081	0.471	0.131
Wuhan	0.251	0.451	0.591	0.161	0.051	0.501	0.181	0.081
Hefei	0.241	0.041	0.391	0.961	0.931	0.061	0.141	0.351
Dongwan	0.221	0.001	0.111	0.161	0.711	0.091	0.971	0.141
Wuxi	0.211	0.051	0.513	0.341	0.601	0.061	0.481	0.061
Changsha	0.201	0.171	0.51	0.161	0.051	0.061	0.061	0.171
Xi’an	0.141	0.481	0.31	0.081	0.891	0.051	0.061	0.521
Hangzhou	0.141	0.421	0.751	0.121	0.051	0.051	0.231	0.091
Chengdu	0.131	0.231	0.681	0.081	0.581	0.251	0.151	0.181
Qingdao	0.111	0.031	0.071	0.081	0.051	0.051	0.071	0.531
Zhengzhou	0.111	0.011	0.391	0.051	0.051	0.051	0.111	0.071
Yantai	0.051	0.121	0.111	0.041	0.491	0.051	0.051	0.081
Dalian	0.031	0.051	0.031	0.041	0.051	0.041	0.061	0.031

4.4 Measurement and calibration of variables

This study analyzes the combination of multiple factors in the intelligent transformation of manufacturing industry, combined with the case distribution of the score of intelligent manufacturing level in the result variable. At the same time, the qualitative comparative analysis method adopted in this study to study the evaluation method of influencing factors of intelligent transformation of manufacturing industry is rare in previous studies, so the existing studies can not effectively provide reference for the corresponding anchor membership. Based on the selected manufacturing city case, the relevant text analysis of intelligent transformation and the distribution of the selected sample city case on each conditional variable, this study chooses to take the following measures, the direct calibration method provided by Ragin [21]. It determines that the three anchor points of the result variable and conditional variable of intelligent transformation of manufacturing industry are “full membership $=$ 0.95”, “intersection $=$ 0.5” and “full non Membership $=$ 0.05”. The condition variables and result variables are anchored, and the calibration is continued through fsqca3.0 software to form the calibration data available for subsequent analysis. Table 3 shows the data of result variables and conditional variables after calibration.

5. Qualitative comparative analysis

Qualitative comparative analysis includes two steps, one is the necessary condition analysis of single condition to explain the results, the other is the sufficient condition analysis of combination of conditional variables to explain the results. At the same time, the analysis results are measured by the consistency of the interpretation of the result variables and the coverage of the number of cases. The above results are completed by fsqca 3.0 software.

5.1 Necessity analysis

Table 4 presents the results of the necessary condition analysis. The conditional variables with consistency greater than 0.9 are regarded as necessary conditions, indicating that the indicator can independently explain the result variables. On the other hand, the conditional variables with consistency less than 0.9 indicates that the index can explain the result variable only when it interacts with other variables. Among the seven conditional variables of necessity analysis, the consistency between intelligent technology innovation and intelligent industry investment is 0.78 and 0.79, which indicates that intelligent technology innovation and intelligent industry investment have strong explanatory power. Although they do not reach the index of necessary condition 0.9, the consistency is much higher than other variables, which may be a necessary condition for the realization of intelligent transformation.

Table 4
Result Analysis of necessary conditions

Conditional variable	Uniformity	Coverage
Intelligent technology innovation (ZC)	0.7856	0.7604
Intelligent industry investment (ZT)	0.7964	0.6529
Intelligent infrastructure (ZJ)	0.6977	0.8645
Intelligent industry efficiency (ZX)	0.5562	0.5993
Intelligent industry scale (ZG)	0.5756	0.8457
Intelligent social benefit (ZS)	0.7210	0.7963
State support (GZ)	0.5991	0.3759

5.2 Conditional combination analysis

For the necessity analysis of each condition variable and the judgment standard of antecedent condition necessity, each antecedent condition does not meet the requirement of 0.9 consistency threshold for the results of high intelligent manufacturing level. Therefore, all antecedent variables can not be used as the necessary conditions for achieving intelligent transformation. Because the independent explanatory power of the antecedent variables to the interpreted results is not strong, the antecedent variables are further configuration analyze. According to Du Yunzhou [22, 23] and other scholars, this paper sets the consistency threshold to 0.8, the number of cases threshold to 1, and the PRI threshold to 0.75 in order to reduce the potential contradiction configuration. According to the simple solution and the intermediate solution, the core condition and the edge condition are distinguished, and the conditional configuration of the corresponding result variable is obtained.

Table 5
Condition configuration for intelligent transformation

Conditional variable	Configuration path
	1	2a	2b	3
Intelligent technology innovation (ZC)	$\bullet$	$\bullet$	$\bullet$	$\bullet$
Intelligent industry investment (ZT)	$\bullet$	$\bullet$	$\bullet$	$\bullet$
Intelligent infrastructure (ZJ)	$\bullet$	$\bullet$	$\bullet$	$\otimes$
Intelligent industry efficiency (ZX)	$\circ$	$\otimes$	$\otimes$	$\circ$
Intelligent industry scale (ZG)		$\circ$	$\otimes$	$\circ$
Intelligent social benefit (ZS)	$\circ$	$\otimes$	$\circ$	$\circ$
State support (GZ)	$\otimes$	$\bullet$	$\bullet$	$\bullet$
Consistency	0.9957	0.9823	1	0.9909
Original coverage	0.2465	0.2379	0.1972	0.2357
Unique coverage	0.1221	0.0611	0.0182	0.1221
Result consistency	0.9857
Result coverage	0.5176

Explain: $\bullet$ or $\bigcirc$ indicates that the condition exists, $\otimes$ indicates that the condition does not exist, $\bullet$ indicates the core condition, $\circ$ or $\otimes$ indicates the edge condition, and blank indicates that the condition has no effect on the result.

Fsqca method can produce three results: complex solution, reduced solution and intermediate solution. The intermediate solution is usually considered to best reflect the research results, and if the antecedent condition appears in both the reduced solution and the intermediate solution, it is the core condition; if only the intermediate solution appears, it is considered to be the edge condition. According to Table 4, it can be found that there are four kinds of conditional configurations that meet the high intelligent level, with the consistency of 0.9957, 0.9823, 1 and 0.9909 respectively, and the overall consistency of 0.9857, which indicates that the above four configurations can be regarded as the sufficient conditions for the intelligent transformation of manufacturing industry; meanwhile, the overall coverage is 0.5176, which indicates that the four configurations explain about 51.76% of the reasons for the intelligent transformation of manufacturing industry. It can be concluded from the table that the four configuration results all include the input of intelligent technology innovation and intelligent industry, which indicates that the two indispensable conditions of intelligent technology innovation and intelligent industry investment in the process of intelligent transformation of manufacturing industry. Although the two factors do not exceed 0.9 under the condition variable consistency calculation, they are higher consistency than other factors. This is consistent with the results of combination conditions, which shows that intelligent technology innovation and intelligent industry investment are the leading factors of intelligent transformation of manufacturing industry. According to Table 4, four groups of conditional configurations 1, 2a, 2b and 3 of the intelligent transformation of manufacturing industry, and comparing the antecedents of the four groups of conditional configurations, this paper summarizes the intelligent transformation of manufacturing industry into the following three paths:

5.2.1 Collaborative R&D and processing

Path 1: ZC * ZT * ZJ * ZX * ZS * $\sim$ GF indicates that under the lack of state support, the combination of intelligent technology innovation, intelligent industry investment and intelligent infrastructure as the core conditions, and intelligent industry efficiency and intelligent social benefits as the marginal conditions can effectively promote the transformation of manufacturing industry to intelligent. From the perspective of value chain, manufacturing enterprises increase the investment in intelligent technology innovation, intelligent industry and intelligent infrastructure, which can greatly enhance the value-added of R&D design and production and processing, increase the product added value of manufacturing enterprises, and enhance the market competitiveness of enterprises. The path also shows that despite the lack of relevant support from the state, manufacturing enterprises can still improve the level of intelligent transformation of manufacturing industry through the transformation of their own scientific and technological achievements, the improvement of innovative technology and the efficient investment of intelligent devices. With the promotion of industrial efficiency and social benefits, the size of the industry will not have an impact on the intelligent transformation of manufacturing industry.

The representative city of path 1 is Suzhou. As one of the most powerful cities in China, Suzhou has been far ahead in the development of intelligent manufacturing. In 2017, the output value of intelligent manufacturing enterprises in Suzhou has reached 170 billion, and the output value of four leading industries, namely new generation information technology, biomedicine, nanotechnology and artificial intelligence, has accounted for 15.7% of the industrial output value above designated scale. According to the data, Suzhou has 262 provincial intelligent workshops and 6 intelligent manufacturing pilot demonstration projects. Suzhou has increased investment in intelligent technology innovation, intelligent industry investment and infrastructure, and its intelligent index ranks third in the sample cities, which also shows the influence of this path.

5.2.2 External factor driven

This type includes two paths: path 2a and path 2B. These two paths emphasize the importance of national policy support in the intelligent transformation of manufacturing industry on the basis of intelligent technology innovation, intelligent industry investment and intelligent infrastructure. Path 2A: ZC * ZT * ZJ * $\sim$ ZX * ZG * $\sim$ ZS * GZ shows that in the absence of industrial efficiency and social benefits, we should take intelligent technology innovation, intelligent industry investment and intelligent infrastructure as the basis The combination of industrial investment, intelligent infrastructure and national policy support as the core conditions and industrial scale as the edge conditions can effectively promote the transformation of manufacturing industry intelligence. Path 2B: ZC * ZT * ZJ * $\sim$ ZX * $\sim$ ZG * ZS * GZ shows that in the absence of industrial efficiency and industrial scale, the combined path with the core conditions of intelligent technology innovation, intelligent industry investment, intelligent infrastructure and national policy support and the marginal conditions of social benefits can effectively promote the intelligent transformation of manufacturing industry. From the perspective of core conditions, different from path 1, path 2 mainly increases the support of national policies, which indicates that the manufacturing industry in each region will increase its competitive advantage, and if there are external factors such as national support, it will effectively promote the intelligent transformation of manufacturing industry. The state support mainly focuses on the capital investment in scientific and technological innovation. Through the capital investment in regional intelligent infrastructure and intelligent innovation ability, it reflects the government’s industrial awareness and macro-control ability. With the deepening of intelligent transformation in the manufacturing industry, the national policy guidance is also gradually carried out. The path also shows that for manufacturing regions with small scale and low industrial efficiency, the effective transformation of manufacturing intelligence can be achieved only through the investment of innovation and intelligent industry and the support of national capital policy.

The representative case cities of Route 2 are Beijing and Tianjin. As a national science and technology innovation center, Beijing has incomparable advantages in the development of intelligent manufacturing industry compared with other cities. With the blessing of national policies, Beijing has absolute advantages in talent and science and technology. At the same time, various districts in Beijing have also established perfect industrial parks, forming a complete intelligent manufacturing industry system. According to the data of Tianjin government network, there is one intelligent manufacturing enterprise in every five industrial enterprises above Designated Size in Tianjin. At the same time, the intelligent industrial chain with Binhai New Area as the industrial cluster has also taken shape. A series of government policy guidance, R&D subsidies and the introduction of talents contribute to the development of Intelligent Manufacturing in Tianjin.

5.2.3 Value chain overall climbing

Path 3: ZC * ZT * $\sim$ ZJ * ZX * ZG * ZS * GZ indicates that in the absence of intelligent infrastructure, the configuration path with the core conditions of intelligent technology innovation, intelligent industry investment and national support, and the marginal conditions of intelligent industry efficiency, intelligent industry scale and intelligent social benefits can also effectively promote the intelligent transformation of manufacturing industry. This path promotes the intelligent transformation of manufacturing industry through the overall factor input of each link of the value chain. The path shows that a single factor can not effectively promote the intelligent transformation, and the coordinated promotion of various factors can more effectively promote the intelligent transformation of manufacturing industry.

The representative cities of route 3 are Shanghai and Shenzhen. As China’s financial center, the scale of Shanghai’s intelligent manufacturing industry will exceed 90 billion in 2019. The first set of intelligent manufacturing key equipment and core components will break through more than 40 items. It has undertaken 37 items of national intelligent manufacturing comprehensive standardization and new mode application, and a relatively complete manufacturing system of R&D, manufacturing and service, which is conducive to promoting the intelligent transformation of Shanghai’s manufacturing industry. Shenzhen has a unique location advantage in the intelligent development of manufacturing industry. A large number of intelligent industrial projects with Guangdong, Hong Kong and Macao as the industrial cluster have been carried out one after another. In addition to the inflow of policies and capital, the intelligent development of Shenzhen is far ahead. To sum up, in the process of intelligent development of manufacturing industry, the joint investment of all elements in the value chain can effectively promote the intelligent development.

6. Conclusion

From the perspective of R&D design, production and marketing services in the value chain, this paper analyzes the influencing factors of the intelligent transformation of manufacturing industry: intelligent technology innovation, intelligent industry investment, intelligent infrastructure, intelligent industry efficiency, intelligent industry scale, intelligent social benefit, and state support, and puts forward the hypothesis analysis of each factor on the intelligent transformation of manufacturing industry. Taking 22 manufacturing center cities as examples, this paper uses fuzzy set qualitative comparative analysis method to verify the hypothesis, and according to the condition combination of different factors, obtains three configuration paths that affect the intelligent transformation of manufacturing industry. The specific conclusions are as follows, (1) intelligent technology innovation and intelligent industry investment are the necessary conditions for the intelligent transformation of manufacturing industry. Any configuration path contains these two necessary conditions. As the basis of intelligent transformation, intelligent technology innovation mainly takes independent innovation as the core, reduces the production and processing costs of enterprises by technology evolution, and promotes the intelligent process of manufacturing industry. With the continuous progress of intelligent innovation, the investment of intelligent industry has become an indispensable factor in the intelligent transformation; (2) there are three configuration paths in the intelligent transformation of manufacturing industry: collaborative R&D and processing, external factor driven and value chain climbing. Different industrial structure, economic characteristics and government policies in different regions lead to three different development paths in intelligent transformation. In the process of intellectualization, each region can choose different development paths to promote the process of intellectualization according to its own manufacturing development characteristics and the configuration of transformation factors; (3) through the overall analysis of influencing factors and configuration path, a single factor can not promote the intelligent transformation. The necessary condition of intelligent transformation is the collaborative combination of multiple factors. At the same time, in addition to the two factors of intelligent technology innovation and industrial investment, the collaborative combination of other factors can still be effective on the road of intelligent transformation, even if there is no one factor Realize the intelligent transformation of manufacturing industry.

In this paper, there are also some shortcomings in the research process. In the case selection, although the 22 sample cities cover some provinces in the country, there are some limitations. In the data collection, the combination of statistical yearbook and network data can ensure the integrity to a certain extent, but there are some limitations compared with the questionnaire. In the future research, we can expand the capacity of case samples, and use social surveys and interviews to make the analysis results more scientific.

References

Yan

et al., Structural transformation and time-varying characteristics of the evolution of China’s new industrialization road, Soft Science 33(4) (2019), 11–15.

S.L.

et al., 40 years of Chinese manufacturing industry: intelligent process and prospect, China Soft Science 1 (2019), 1–9+30.

Thomas

et al., Manufacturing and energy systems, Computers and Chemical Engineering 27(10) (2017), 25–31.

Sarah

H.E.

et al., Towards Identification of the Hierarchical Link between Industry 4.0, Smart Manufacturing, International Journal of Supply and Operations Management 3(6) (2019), 231–224.

Wang

et al., Smart manufacturing and intelligent manufacturing: A comparative review, Engineering 7(17) (2020), 171–182.

Arnesh

et al., Industry 4.0 implementation for multinationals, Process Safety and Environmental Protection 118 (2018), 316–329.

Andrew

, Smart Manufacturing, International Journal of Production Research 56(12) (2017), 508–517.

Liu

et al., Analysis of the overall architecture of intelligent manufacturing, China Engineering Science 20(4) (2018), 23–28.

Shi

L.Y.

and Yan

, Technological capability of high quality development of Intelligent Manufacturing: Framework and verification – Based on two-dimensional vision of CPS theory and practice, Economist 9 (2019), 83–92.

10.

Meng

S.F.

and Zhao

, Research on the influencing factors of the development from traditional manufacturing to intelligent manufacturing, Scientific and Technological Progress and Countermeasures 35(1) (2018), 66–72.

11.

and Yang

L.S.

, Research on Influencing Factors of intelligent transformation and upgrading of manufacturing enterprises based on grounded theory, Research on Science and Technology Management 38(8) (2018), 115–123.

12.

X.J.

, Research on the evaluation of the intelligence degree of China’s manufacturing industry and its influencing factors, China soft Science 1 (2020), 154–163.

13.

Shao

T.J.

, Research on the remolding of enterprise value chain by digital and intelligent technology, Economics 9 (2019), 95–102.

14.

Liu

and Pan

, Research on the effect of artificial intelligence on the division of labor in manufacturing value chain, Research on Quantitative Economy and Technical Economy 37(10) (2020), 24–44.

15.

Liu

et al., Can intelligent development promote the rise of China’s global value chain? Scientific Research 39(4) (2021), 604–613.

16.

Han

B.J.

, Intelligent Industrialization: a new perspective of industrial development paradigm, Economist 10 (2017), 21–30.

17.

Zhou

et al., Towards a new generation of intelligent manufacturing, Engineering 4(1) (2018), 11–20.

18.

Chen

L.K.

et al., Design and Implementation of Intelligent Creation Platform Based on Artificial Intelligence Technology, Journal of Computational Methods in Sciences and Engineering 20(4) (2020), 1109–1126.

19.

Chen

L.K.

et al., Design and implementation of bilingual digital reader based on artificial intelligence and big data technology, Journal of Computational Methods in Sciences and Engineering 20(3) (2020), 889–907.

20.

Zheng

J.L.

and Chen

B.X.

, Comparative study on the condition configuration path of the transformation and upgrading of manufacturing enterprises—Enterprise Resource Perspective, Scientific and technological progress and Countermeasures 6(37) (2020), 1–10.

21.

Ragin

C.C.

, Redesigning social inquiry: Fuzzy Sets and Beyond[M]. Chicago: University of Chicago Press, 2008.

22.

Y.Z.

and Jia

L.D.

, Configuration perspective and qualitative comparative analysis (QCA): a new way of management research, Management World 6 (2017), 155–167.

23.

Fiss

P.C.

, Building better causal theories: A fuzzy set approach to typologies in organization, Academy of Management Journal 54 (2011): 393–420.