The improvement path and evaluation of science and technology innovation capability in Shaanxi equipment manufacturing industry under the background of intelligence

Abstract

Taking data from eight sub-industries of equipment manufacturing industry in Shaanxi province from 2012 to 2020 as samples, an evaluation index system of science and technology innovation capability in Shaanxi equipment manufacturing industry is constructed, to evaluate the innovation capability of each sub-industry by principal component analysis method and entropy method, and analyze their dynamic evolution trends combined with kernel density. The results show that the innovation capability of railway, shipbuilding, aerospace, and other transportation equipment manufacturing industries is far ahead of other seven sub-industries in Shaanxi. The contribution of the two indexes, including R&D expenditure and Proportion of sales revenue from new products, is higher than other indexes, and the gap in the capability is expanding over time. Therefore, intelligent transformation in Shaanxi equipment manufacturing industry is an inevitable choice. It is necessary to strengthen investment in new equipment and technologies, focus on research and development of new products, effectively expand channels for obtaining funds, and give full of the driving effect of dominant industries to enhance the innovation capability in Shaanxi equipment manufacturing industry.

Keywords

equipment manufacturing industry science and technology innovation capability evaluation index system improvement path principal component analysis method entropy method

Introduction

“Report of the 20th National Congress of the Communist Party of China” clearly states “Promote the development of high-end, intelligent and green manufacturing industry.” “The 14th Five Year Plan for the Development of Intelligent Manufacturing” proposes that the essence of intelligent manufacturing is the manufacturing, and technology and equipment should be the core and cornerstone of manufacturing. As a key sector of industrial production, the equipment manufacturing industry plays a crucial role in the construction of modern industrial systems. In the equipment manufacturing industry, to enhance the science and technology innovation capability and strengthen strategic scientific and technological support capability are inevitable choice to cope with the profound changes that have not been seen in a century, so to adapt to the development trend of the new generation of industrial revolution and move towards a strong manufacturing province.

Related research achievements at home and abroad mainly focus on three aspects. First, research on the influencing factors of the equipment manufacturing industry’s transformation and upgrading. The main factors affecting the transformation and upgrading of the equipment manufacturing industry include supply chain relationship capital, enterprise green management and financial performance.^1–3 Li and Ma used fuzzy set qualitative and comparative analysis methods and found that the equipment manufacturing industry’s high-end and intelligent transformation are mainly influenced by six factors, namely market customization, intelligent manufacturing technology, interconnectivity, intelligent management platforms, research and development investment, and innovative marketing.⁴ Second, research on estimating the equipment manufacturing industry’s innovation capability. Using 10 provinces in eastern region of China as sample data, Pan et al. found that there is a significant gap in the development level of high-end equipment manufacturing industry in every province.⁵ By designing an evaluation index system, Xu and Zhai estimated high-quality development level of the equipment manufacturing industry from 2002 to 2018 in China. The results showed significant differences in different stages of research and development design, market matching, and production manufacturing.⁶ Third, research on evaluating science and technology innovation capability of the equipment manufacturing industry. Sun and Xu designed a comprehensive dynamic evaluation model named by “VHSD-EM” and evaluated the science and technology innovation capability of the high-end equipment manufacturing industry from 2011 to 2018 in China. The results showed that there were differences in the capability of each sub-industry over time, but showing a decreasing trend. The capability of each sub-industry had a clustering effect, and empirically tested the promoting effect of the technological progress on the capability.⁷ From the two perspectives of input indexes and output indexes, Wu and Zhang evaluated the science and technology innovation capability of high-end equipment manufacturing by four stage DEA and Bootstrap model, and found a significant U-shaped change trend.⁸ Reynolds and Uygun and Akman et al. evaluated the science and technology innovation efficiency in the equipment manufacturing industry.^9,10

The above research has certain reference value for the evaluation of the science and technology innovation capability in Shaanxi equipment manufacturing industry and the development differences analysis of its sub-industries. So, following main principles of scientificity, systematicity, and objectivity, by using principal component analysis method and entropy method, design an evaluation index system from four dimensions: innovation foundation, innovation input, innovation support, and innovation output. It evaluates the innovation capability of eight sub-industries, and seeks effective ways to enter the high-end value chain of the manufacturing industry.

General situation of research area

The equipment manufacturing industry is a fundamental and strategic industry, which is known as the backbone of a country’s industrial development. In 2022, the added value of Shaanxi equipment manufacturing industry exceeded 900 billion yuan, achieving a growth rate of 12.7%, which plays an important driving role in the high-quality economic development. In recent years, Shaanxi has taken multiple measures to vigorously promote the equipment manufacturing industry transformation and upgrading through intelligent and digital means, accelerating its move towards the high-end. For example, the intelligent workshop of Fast company has basically achieved unmanned production, and industrial robots and CNC machine tools work together efficiently. Each production link is implemented with intelligent testing, presenting a dual improvement in quality and efficiency.

Although Shaanxi equipment manufacturing industry has been developing rapidly, it only ranks at the middle level in terms of economic operation quality and asset utilization rate, has not shown cluster advantages in terms of enterprise economic scale. The supporting rate of most equipment products is less than 60% within the province in terms of supporting capacity. Therefore, Shaanxi equipment manufacturing industry still has great development potential and space, the development of intelligent manufacturing is the essential path for the industry transformation and upgrading in the digital economy era. The “2022 China Regional Innovation Capability Evaluation Report” shows that the comprehensive index of science and technology innovation capability in Shaanxi enterprise ranks 18th in the country, which does not match the increase speed and magnitude of added value. This indicates that there is great potential for the improvement of innovation capability. Therefore, by evaluating the innovation capability of Shaanxi equipment manufacturing industry and exploring the key influencing factors, so to break through development bottlenecks and take targeted countermeasures to achieve high-end and intelligent development of Shaanxi equipment manufacturing industry.

Evaluation of the innovation capability

Design of an evaluation index system

Scholars have conducted extensive research on the measurement indicators of technological innovation capability. This article believes that various innovative resources within the industry are innovative potential, and they are a potential form of innovation capability. Only when these resources are used to serve the industry and achieve enterprise goals, can these capabilities truly be technological innovation capability. Therefore, this article selects four secondary indicators that can reflect the degree of resource conversion, such as innovation foundation, bed performance investment, innovation support, and innovation output, and selects 14 tertiary indicators to measure scientific and technological innovation capabilities based on their meanings.

Additionally, an evaluation index system of science and technology innovation capability in Shaanxi equipment manufacturing industry is designed by following two basic principles: (1) The system should be scientific and reasonable. The indexes are concise and independent of each other, including scale and efficiency indexes, which can systematically reflect the characteristics and changes of innovation capability. (2) The indexes data sources should be authority and consistency, to ensure the authenticity, reliability, coherence, and extensibility of data. Referring to the related research achievements, combined with the innovation capability characteristics in Shaanxi equipment manufacturing industry, four second-level indexes are selected named by innovation foundation, innovation investment, innovation support, and innovation output, and 14 three-level indexes, mainly including Proportion of R&D personnel, Development investment intensity of new products, R&D expenditure, Numbers of technological papers published, and Input-output coefficient of technological innovation, etc., to design the system.

First, innovation foundation is the fundamental condition for equipment manufacturing industry to carry out innovation work, and the cornerstone for enhancing science and technology innovation capability. Innovation foundation index includes four three-level indexes, namely Numbers of enterprise, Proportion of R&D personnel, Total labor productivity of industry, and Newly added fixed assets, respectively. Numbers of enterprises is an important reflection of industry scale, which is a representation of the innovation capability. Proportion of R&D personnel reflects human capital element foundation of science and technology innovation, which is a prerequisite for improving production efficiency. Total labor productivity of industry reflects the input–output efficiency of labor factors, the higher it, the more the output. Newly added fixed assets is an important foundation for improving enterprise output. Second, innovation investment is an inexhaustible driving force of enterprise innovation development, and the higher the level of investment, the better the innovation effect. Innovation investment index includes four three-level indexes, namely R&D personnel investment, Development investment intensity of new products, Newly-added investment and R&D expenditure, respectively. R&D personnel investment refers to personnel investment of a company in the research and development field. Development investment intensity of new products refers to proportion of R&D investment to total investment of the enterprises, which can reflect degree of importance that enterprise attaches to science and technology innovation. Newly-added investment is foundation of enterprise science and technology innovation. R&D expenditure refers to expenditure incurred in research and development activities. Third, innovation support is an important factor in enhancing science and technology innovation capability. Innovation support index includes three three-level indexes, namely Investment intensity of government R&D, Numbers of valid invention patents and Numbers of scientific papers published, respectively. Investment intensity of government R&D reflects government’s encouragement and support for science and technology innovation. Numbers of valid invention patents and Numbers of scientific papers published reflect depth and professional level of research on science and technology innovation. Finally, innovation output reflects transformation of innovation achievements and demonstrates effectiveness of new technology investment. Innovation output index includes the three three-level indexes, namely Profit margin, Input–output coefficient of science and technology innovation, and Proportion of sales revenue from new products, respectively. Profit margin measures a company’s profit level and reflects its operational capability. Input–output coefficient of science and technology innovation measures output efficiency. Proportion of sales revenue from new products reflects the revenue achieved by the company’s new product sales. The specific meanings of each dimension index are detailed in Table 1.

Table 1.

Evaluation index system.

The first-level Index	The second-level Indexes	The third-level Indexes	Unit	Indexes description	Positive and negative
Evaluation index system	Innovation foundation	Numbers of enterprises	Number	Total numbers of enterprises in segmented industries	+
		Proportion of R&D personnel	%	Numbers of research and development personnel/Numbers of researchers	+
		Total labor productivity of industry	Yuan per person	Output values of total industry/Annual average numbers of employees	+
		Newly-added fixed assets	10,000 yuan	Newly added fixed assets this year	+
	Innovation investment	R&D personnel investment	Person	Numbers of research and development personnel	+
		Development investment intensity of new products	%	Development expenditures of new products/Sales revenue of new products	+
		Newly-added investment	10,000 yuan	Newly added investment this year	+
		R&D expenditure	10,000 yuan	Research and development expenditure	+
	Innovation support	Investment intensity of government R&D	%	Investment funds of government research and development /Expenditure of research and development	+
		Numbers of valid invention patents	Piece	Numbers of invention patents approved by the China national intellectual property administration and still in validity during the statistical period	+
		Number of publications in scientific and technological journals	Piece	Number of publications in scientific and technological journals	+
	Innovation output	Profit margin	%	Total profits/Main business revenue	+
		Input-output coefficient of science and technology innovation		Sales revenue of new products/Development expenses of new products	+
		Proportion of sales revenue from new products	%	Sales revenue of new products/Gross industrial output value	+

Evaluation methods and source of research data

Evaluation methods

Referring to relevant research results,^11,12 the principal component analysis method is used to reduce dimensionality of evaluation indexes and select main indexes, then calculate objective weights of each index by the entropy method. Finally, the innovation capability is comprehensive evaluated.

(1) Main principles of principal component analysis method

Assumed the number of samples is $m$ , and there are $n$ indexes in each sample, $r_{i j}$ representing the $j$ th index in the $i$ th sample, the sample data matrix $R$ can be represented as equation (1)

R = [\begin{array}{l} r_{11} & r_{12} & \dots & r_{1 n} \\ r_{21} & r_{22} & \dots & r_{2 n} \\ \dots & \dots & \dots & \dots \\ r_{m 1} & r_{m 2} & \dots & r_{m n} \end{array}]

(1)

After dimensionality reduction, $n$ indexes can be reduced to $p$ indexes. The specific steps include the following three steps.

Step 1: Standardization of data processing.

To make the data comparable and eliminate the impact of different dimensions, standardize the data. Using Z-score method for standardization transformation by equation (2)

r_{i j}^{*} = (r_{i j} - \bar{r_{j}}) / S_{j}

(2)

Among them, $\bar{r_{j}} = \frac{1}{n} \sum_{i = 1}^{n} r_{i j}$ is the average value of the $j$ th index; $S_{j} = \sqrt{\sum_{i = 1}^{n} {(r_{i j} - \bar{r_{j}})}^{2} / (m - 1)}$ is the standard deviation of the $j$ th index. The new matrix $R^{*}$ obtained after transformation is equation (3)

R^{*} = [\begin{array}{l} r_{11}^{*} & r_{12}^{*} & \dots & r_{1 n}^{*} \\ r_{12}^{*} & r_{12}^{*} & \dots & r_{1 n}^{*} \\ \dots & \dots & \dots & \dots \\ r_{m 1}^{*} & r_{m 2}^{*} & \dots & r_{m n}^{*} \end{array}]

(3)

Step 2: Calculate correlation coefficient between standardized matrix indexes.

Construct a correlation matrix $Q^{*}$ , among them, $q_{s t}^{*}$ represent the correlation coefficient between $r_{s}$ and $r_{t}$ , its calculation by equation (4)

q_{s t}^{*} = q_{ts}^{*} = \frac{\sum_{i = 1}^{m} (r_{i s}^{*} - \bar{r_{s}^{*}}) (r_{i t}^{*} - \bar{r_{t}^{*}})}{\sqrt{\sum_{i = 1}^{m} {(r_{i s}^{*} - \bar{r_{s}^{*}})}^{2} {(r_{i t}^{*} - \bar{r_{t}^{*}})}^{2}}}

(4)

Step 3: Calculate characteristic roots $λ_{k}$ .

Among them, k = 1, 2, … p, and $λ_{1} \geq λ_{2} \geq \dots \geq λ_{p}$ , $λ_{k}$ is the variance of each component. Select principal components based on cumulative variance contribution rate $α$ ( $α \geq$ 85%), p is numbers of principal components, as equation (5)

α = \sum_{i = 1}^{k} λ_{i} / \sum_{i = 1}^{p} λ_{i}

(5)

(2) Main principles of entropy method

The entropy method mainly uses entropy as a measure of uncertainty to determine degree of dispersion of indexes. The size of entropy represents disorder degree, that is, the larger the entropy value, the lower the disorder degree. The steps to determine indexes weights using the entropy method are as follows.

Step 1: Standardization of data processing.

Select corresponding calculation formulas according to different types of indexes for standardization processing:

If positive indexes, calculate using equation (6)

Y_{i j} = \frac{X_{i j} - \min (X_{i j})}{\max (X_{i j}) - \min (X_{i j})}

(6)

If negative indexes, calculate using equation (7)

Y_{i j} = \frac{\max (X_{i j}) - X_{i j}}{\max (X_{i j}) - \min (X_{i j})}

(7)

Step 2: Calculate proportion $P_{i j}$ of the $j$ th item in the $i$ th year.

Considering that calculation of entropy value requires logarithmic operations on the data, the indexes value must be greater than 0. Therefore, a translation processing of 0.01 is applied to the data, namely $X_{i j}^{*} = X_{i j} + d$ , among them, $d$ is a positive number, to make the processed value greater than 0, therefore take $d =$ 0.01. Calculate $P_{i j}$ by equation (8)

P_{i j} = \frac{X_{i j}^{*}}{\sum_{i = 1}^{n} X_{i j}^{*}}

(8)

Step 3: Calculate information entropy $e_{j}$ of the $j$ th index by equation (9)

e_{j} = - k_{i = 1}^{n} (P_{i j} \times \ln P_{i j})

(9)

Among them, the constant $k$ is related to the number of samples $n$ , $k > 0$ , generally take $k = 1 / l n n$ .

Step 4: Calculate difference coefficient $d_{j}$ for the $j$ th index.

Difference coefficient represents degree of data dispersion, and it affects weight of the indexes. Subtract the entropy value $e_{j}$ from 1 by equation (10)

d_{j} = 1 - e_{j}

(10)

Step 5: Calculate weight $w_{j}$ of the $j$ th index.

The larger the difference coefficient of an index, the greater its weight. Based on the difference coefficient, the weight of each index is calculated by equation (11)

w_{j} = \frac{d_{j}}{\sum_{j = 1}^{n} d_{j}}

(11)

(3) Calculate comprehensive score ${s c o r e}_{i}$ .

Calculate comprehensive score based on the scores and entropy values of each principal component by equation (12)

s c o r e_{i} = \sum_{j = 1}^{n} (w_{j} * F_{i p})

(12)

Among them, $F_{i p}$ is the score of the $i$ th sample on the $p$ th principal component. The larger the comprehensive score, the more favorable the evaluation result.

Source of research data

According to relevant national documents, the equipment manufacturing industry includes eight sub-industries, namely metal product industry, general equipment manufacturing industry, instrument and meter manufacturing industry, electrical machinery and equipment manufacturing industry, automobile industry, railway, shipbuilding, aerospace and other transportation equipment manufacturing industries, special equipment manufacturing industry, and computer, communication and other electronic equipment manufacturing industries, respectively. Therefore, select panel data of eight sub-industries in Shaanxi equipment manufacturing industry from 2012 to 2020 (Mainly from the “Shaanxi Statistical Yearbook (2013-2021),” Portal of Department of Industry and Information Technology in Shaanxi, etc.). Because index data of gross industrial output value in 2020 is missing, so linear interpolation method is used to supplement the data-set.

Results analysis

Evaluation results analysis

The specific evaluation processes of the innovation capability are as follows.

Step 1: Calculate evaluation indexes weights by principal component analysis method.

First, correlation analysis is conducted on all the third-level indexes, and it is found that correlation coefficients are mostly greater than 0.4, with some values being small and not affecting the test. Then, KMO sample measures are used to perform correlation tests on each variable. After measurement, the KMO value is 0.711, with a significant p-value. It is generally believed that if the KMO value is greater than 0.6, principal component analysis can be performed. It is appropriate to determine number of principal components and eigenvalues greater than 1. Therefore, three principal components with eigenvalues greater than 1 are selected, and their cumulative contribution rate reached 77.31%, and after dimensionality reduction, 13 indexes were selected, as shown in Table 2.

Table 2.

Selection of principal component and contribution rate.

The third-level Indexes	The first principal component F1	The second principal component F2	The third principal component F3
Numbers of enterprises	0.5446	0.7402	0.0164
Total labor productivity of industry	0.5237	0.6358	0.1342
Newly added fixed assets	0.3839	0.4533	0.5534
R&D personnel investment	0.8184	−0.3867	−0.0880
Development investment intensity of new products	0.0588	−0.4167	0.7934
Newly added investment	0.3442	0.6920	0.4340
R&D expenditure	0.9336	−0.4006	0.0386
Investment intensity of government R&D	0.5597	−0.1037	−0.2008
Numbers of valid invention patents	0.7367	−0.6569	−0.2021
Numbers of scientific papers published	0.8600	0.0235	0.5799
Profit margin	0.3081	−0.1748	−0.6325
Input–output coefficient of science and technology innovation	0.0359	−0.2649	−0.4567
Proportion of sales revenue from new products	0.7782	−0.2858	−0.2645
Contribution rate	34.16	24.41	16.74
Accumulated contribution rate	34.16	60.57	77.31

Second, based on the principal component score matrix and standardized observations of each variable, three factor scores can be calculated, and then weighted and summarized with the corresponding variance contribution rate as weights to calculate the final comprehensive score. The above operation is completed through Stata software, and the weight values of the obtained indexes are shown in Table 3. From the second-level indexes, it is found that innovation investment index has the highest weight at 0.293. Then are index of innovation support and innovation output, indicating that the level of innovation investment has a significant impact on the science and technology innovation capacity in Shaanxi equipment manufacturing industry. From the third-level indexes, it can be seen that some indexes, such as R&D expenditure index, index of Proportion of sales revenue from new products, etc., have a relatively high weight, which once again confirms that level of investment and realization of the value of new products are key factors in improving innovation capability. Investment in intelligent equipment and technology has laid the foundation for the equipment manufacturing industry transformation and upgrading, greatly promoting the innovation capability improvement.

Table 3.

Weights of evaluation indexes.

The first-level Index	The second-level Indexes	The third-level Indexes	Weights
Evaluation index system (1.000)	Innovation foundation (0.181)	Numbers of enterprises (number)	0.054
		Total labor productivity of industry (yuan per person)	0.055
		Newly added fixed assets (10,000 yuan)	0.072
	Innovation investment (0.293)	R&D personnel investment (person)	0.078
		Development investment intensity of new products (%)	0.049
		Newly added investment (10,000 yuan)	0.053
		R&D expenditure (10,000 yuan)	0.114
	Innovation support (0.262)	Investment intensity of government R&D (%)	0.095
		Numbers of valid invention patents (piece)	0.098
		Numbers of scientific papers published (piece)	0.069
	Innovation output (0.264)	Profit margin (%)	0.052
		Input–output coefficient of science and technology innovation	0.090
		Proportion of sales revenue from new products (%)	0.122

Step 2: Calculate science and technology innovation capability by entropy method.

After using the entropy method to standardize the collected and organized data, the science and technology innovation capability level and mean value of the eight sub-industries in Shaanxi equipment manufacturing industry are evaluated, and their innovation capabilities are ranked, as shown in Table 4.

Table 4.

Evaluation of innovation capability of each sub-industry.

Sub-industry	2012	2013	2014	2015	2016	2017	2018	2019	2020	Mean value	Sort
Railway, shipbuilding, aerospace, and other transportation equipment manufacturing industries	0.334	0.363	0.492	0.537	0.579	0.604	0.541	0.601	0.622	0.519	1
Computer, communication, and other electronic equipment manufacturing industries	0.102	0.146	0.232	0.274	0.296	0.321	0.410	0.360	0.500	0.294	2
Automobile industry	0.141	0.136	0.172	0.175	0.146	0.296	0.335	0.353	0.368	0.236	3
Special equipment manufacturing industry	0.161	0.177	0.208	0.198	0.203	0.217	0.231	0.252	0.304	0.217	4
Electrical machinery and equipment manufacturing industry	0.177	0.155	0.181	0.195	0.197	0.209	0.219	0.248	0.289	0.208	5
Instrument and meter manufacturing industry	0.156	0.142	0.186	0.144	0.222	0.166	0.263	0.220	0.226	0.192	6
General equipment manufacturing industry	0.116	0.123	0.139	0.160	0.156	0.158	0.164	0.149	0.184	0.150	7
Metal product industry	0.115	0.117	0.144	0.124	0.154	0.149	0.132	0.151	0.159	0.138	8

From the evaluation results, the sub-industry with the highest innovation capability is transportation equipment manufacturing industry, whose index values have been higher than 0.5 since 2015. The reason is that as a representative of the high-end level of manufacturing, the aerospace industry in Shaanxi province has gathered 30% of the country’s scientific research capabilities, 1/4 of aerospace professionals and high-precision equipment. The civil aerospace industry, mainly focused on satellite applications, special aerospace technology applications and information technology, is developing rapidly. At the same time, in order to accelerate construction of “industrial aerospace,” Shaanxi has adopted a series of plans and measures, such as “14th Five Year Plan” for aerospace industry bases and “2035 Long Range Objectives Outline,” which greatly promote innovation and iteration in the aerospace industry. The innovation capability of computer, communication and other electronic equipment manufacturing industries ranks second. Under leadership of industry leaders such as Samsung Semiconductor and LONGi, the electronic equipment manufacturing industry, as a pillar industry in Shaanxi province, has shown steady development. In addition, the innovation capability of the automotive industry ranks third. Under the leadership of Shaanxi Automobile Group, BYD (Xi’an) and Geely (Baoji), the production and sales of the automotive industry are higher than national average. Since 2021, the automobile industry has achieved an output value of over 100 billion yuan. The innovation capabilities of general equipment manufacturing industry and metal product industry in Shaanxi are relatively insufficient compared with other industries, and there is a lot of space for improvement.

Change trend analysis

In Figure 1, the innovation capabilities of eight sub-industries in Shaanxi equipment manufacturing industry shows a non-synchronous change trend from 2012 to 2020, and the degree of differentiation is basically stable. Specifically, in recent years, the overall innovation capability has shown an upward trend. Among them, the innovation capabilities of railway, shipbuilding, aerospace, and other transportation equipment manufacturing industries, as well as computer, communication, and other electronic equipment manufacturing industries, have increased significantly, about twice as much. Other six sub-industries have also a slight improvement, especially metal product industry has a relatively lower innovation level. Mainly due to the constraints of most sub-industries on imports and technology, encountering bottlenecks in intelligent manufacturing, failing to master core technologies and key equipment, resulting in limited production and science and technology innovation capability. As one of the pillar industries in Shaanxi, metal product industry has a large number of enterprises, with representative enterprises including Xi’an Foundry Factory, Xi’an Electric Motor Group, Baoji Iron, and Steel Company, covering many fields such as forging, casting, metallurgy, and processing. However, due to the small scale of metal product enterprises, slow technological updates and iterations, as well as the higher time cost and capital costs required to meet environmental protection standards, the upgrading of intelligent devices and the application of cloud computing technology are relatively weak. Therefore, its capability has been hovering at a low level.

Figure 1.

Change trends in the innovation capability from 2012 to 2020.

The innovation capability of railway, shipbuilding, aerospace, and other transportation equipment manufacturing industries in Shaanxi has always been in a leading position. In the past decade, Shaanxi’s railway manufacturing industry has transitioned towards digitization and intelligence, building a “rice shaped” high-speed rail network. Public transportation and bulk transportation capabilities and services have been continuously upgraded and upgraded, achieving green and intelligent development of railway transportation equipment and core component enterprises. Thanks to the continuous issuance of several policy documents by national and local governments to encourage and support the development of the industry, Shaanxi has a relatively complete aerospace equipment industry chain, and two national level industrial clusters, namely the Xi’an National Civil Aerospace Industry Base and National Aviation High-tech Industry Base, has already been formed. It is expected to form an industrial cluster and Xi’an International Aviation Hub by 2025, which will leverage economies of scale to promote comprehensive innovation capability improvement.

The innovation capability of computer, communication, and other electronic equipment manufacturing industries in Shaanxi is relatively higher, but the speed of industrial transformation has slowed down in recent years, mainly due to the strong innovation and high-speed growth of enterprises such as Samsung Semiconductor and Rainbow Optoelectronics Technology, which have continuously consumed the technical knowledge and experience advantages accumulated by the industry itself. Affected by the industry cycle and the decline in product prices, some electronic equipment enterprises represented by companies such as Xian’yang Rainbow Optoelectronics Technology and Guanjie Display Technology lack sustained innovation momentum, and their intelligent transformation is difficult to break through.

In sum, the significant feature of innovation capability changes in Shaanxi equipment manufacturing industry is that the industry has invested a considerable amount of funds and manpower in research and development activities. The science and technology innovation advantages are mainly reflected in two sub-industries, namely railway, shipbuilding, aerospace, and other transportation equipment manufacturing industries, as well as computer, communication, and other electronic equipment manufacturing industries. The innovation level of other sub-industries still needs to be improved.

Dynamic evolution trend analysis

The kernel density map of the innovation capability in Shaanxi equipment manufacturing industry from 2012 to 2020 is shown in Figure 2, which intuitively displays the dynamic evolution trend of the capability. From 2012 to 2020, the overall kernel density map has gradually shifted to the right, and the innovation capability index corresponding to the peak has gradually expanded, which also confirms the increasing trend. From 2012 to 2017, there was a smaller peak on the right side of the kernel density curve. From 2018 to 2020, the kernel density curves had only one peak, with a longer tail on the right side, exhibiting an asymmetric distribution, indicating a gradual reduction in the polarization. The right tail has gradually lengthened and the peak value has gradually decreased for nine consecutive years, indicating an expanding trend in the innovation capacity gap.

Figure 2.

The kernel density map of the innovation capability from 2012 to 2020.

Improvement path

Based on the above analysis, main improvement path of the innovation capability is proposed in Shaanxi equipment manufacturing industry.

First, add invest in new technologies and equipment, to promote the quality and efficiency improvement. The investment in intelligent manufacturing technology and equipment is a main support for manufacturing enterprises to improve production efficiency, and an important guarantee for improving product quality and competitiveness. The lack of core technology is a common problem that restricts innovation in various sub-industries of the equipment manufacturing industry. Accelerating the transformation of the metal product industry towards digitalization and intelligence can timely respond to changes in market and consumer demand. At the same time, strengthening investment in intelligent technology and equipment could promote the improvement of the enterprises’ quality and efficiency in the metal product industry and general equipment manufacturing industry, optimize product design and process steps, achieve enterprise process management, and so reduce manufacturing costs and enhance market competitiveness.

Second, broaden financing channels, enhance research, and innovation capabilities. There is a positive correlation between the investment scale of R&D funds and the innovation capability improvement. R&D funds are the necessary blood for innovation and the key to promoting the integration of financial, industry and innovation chains. Intensify government policy guidance, introduce diversified financing channels such as private capital and venture capital, and build a multi-dimensional financing model for the government, financial institutions and industry enterprises, etc. Through institutional innovation, science and technology innovation, financial innovation, and other mechanisms, it could allocate and optimize resources reasonably, reduce the risk of innovation failure, and ensure the sustainable development of the science and technology innovation from the perspective of R&D funds.

Third, give full of dominant industrial effect and lead industry development. In Shaanxi, the construction of the “Mi” shaped high-speed railway network, the continuous increase in the number and operating scale of China-Europe freight trains and the thriving aerospace manufacturing industry, all benefit from the industrial effects of the railway, shipbuilding, aerospace and other transportation equipment manufacturing industries. As a knowledge and technology intensive industry, the aerospace industry is the “crown” of the equipment manufacturing industry. The industry has a wider range of technological coverage and higher industrial correlation, which is an important symbol of national technological progress and industrial development level. Shaanxi should orderly promote the industrial clusters’ construction in the transportation equipment manufacturing industry, design effective adjustment and improvement mechanisms, effectively play the demonstration driving effect of innovation driven, and so narrow the gap in innovation capabilities among various industries.

Fourth, emphasize to new products development, stabilize industry chain and supply chain. In the evaluation index system, index weight of Proportion of sales revenue from new products is the highest. Realizing product value is an inexhaustible driving force for sustainable science and technology innovation to continue and a main link in stabilizing the value chain. The instrument and meter manufacturing industry should broaden the system development channels of upstream and downstream enterprises, the electronic equipment manufacturing industry should focus on customized product demand to stabilize the supply chain, and the automotive industry should improve the value chains of personalized customization research and development, production and sales, and continuously strengthen the deep combination of digital technology and intelligent technology in the equipment manufacturing industry, so to improve the industry profitability.

Fifth, promote the development of industrial clusters and eliminate outdated production capacity. On the basis of the existing five key intelligent equipment industrial parks, including Xi’an Intelligent Manufacturing Industrial Park, Sany Intelligent Manufacturing Industrial Park, Western Intelligent Equipment Industrial Park, and Xi’an Gaoling Intelligent Manufacturing Industrial Park, we will expand and strengthen them by increasing investment and increasing attention. The government can directly restrict the operation of enterprises that cause substandard products, waste resources, and environmental pollution through legal systems, policy regulations, administrative measures, and other means. By formulating industrial layout plans, guiding the selection, adjustment, and development of industries in various regions, avoiding duplicate construction, and investing saved resources into other innovative industries, the goal of maximizing the use of limited resources is achieved.

Conclusion

Serving as the “aircraft carrier” and “engine” of the industry, the innovation capability of the equipment manufacturing is related to its competitiveness and position in the value chain. To improve the innovation capability, the research and development investment needs to be strengthened and the conversion efficiency of scientific research achievements needs to be enhanced, to maintain the core technological advantages of enterprises, closely integrate the new generation of information technology into all production aspects of the industry, and to achieve industrial intelligent transformation and upgrading. In addition, the government should take the initiative to build bridges to promote school enterprise cooperation, build industry university research information platforms, relevant expert talent pools, scientific research achievement pools, and related information exchange, and strengthen the connection between innovation and development upstream and downstream. The equipment manufacturing industry is the most profound and influential industry that transforms science, technology, and knowledge into productivity. Therefore, in scientific research management, it is not only necessary to evaluate scientific research achievements but also to use the economic and social benefits obtained after the transformation of achievements as the basis for evaluating scientific researchers. This guides scientific researchers to not only value academic value but also pay attention to the role of market value, and improve the application rate of scientific research achievements.

The innovation capability in Shaanxi is relatively lower nationwide, so improving the capability to form a competitive advantage for enterprises and upgrading to intelligent manufacturing is an inevitable choice and development direction for the industry. Therefore, Shaanxi should strengthen environmental innovation and increase investment in intelligent devices, to promote the three chain integration of innovation chain, industrial chain and financial chain. Simultaneously, it should emphasize R&D of new high-end products, promote the transfer of value chain to high-end links, to build a high-tech industry chain and vigorously revitalize intelligent manufacturing, and assist in the high-quality development of new industries, new formats, and new models in Shaanxi.

Footnotes

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study is supported by Shaanxi Social Science Fund Project (No. 2024D025); Special Research Project of Philosophy and Social Sciences in Shaanxi Province in 2024 (No. 2024HZ0931, No. 2024ZD435); Research Team Project on High-quality Development of Rural Economy in Shaanxi (No. 2024SKXJCTD01).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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