Manufacturing In China Research Articles

Research on Key Technologies of Artificial Intelligence for Intelligent Manufacturing

Intelligent manufacturing, as the main form of future manufacturing, is the highland of a new round of competition in the global manufacturing industry, which will fundamentally change the production and manufacturing methods and technological and economic paradigms formed by human beings since the industrial revolution. At present, the intelligent manufacturing industry is still in the early stage of development, which is characterized by three typical characteristics: continuous growth of market size, strong promotion by national governments and diversification of competition subjects. At present, China's manufacturing industry is facing transformation, and the manufacturing industry is bound to need to transform to intelligent manufacturing. Industrial robots are the main content of current intelligent manufacturing, how to fully apply artificial intelligence technology to the manufacturing of industrial robots, so that its manufacturing efficiency, manufacturing accuracy and other product attributes are improved, to maximize its economic benefits, has a particularly important application value. The application of intelligent manufacturing and robots can not only promote the faster development of manufacturing, but also reconstruct new forms of manufacturing. Based on this, the paper first summarizes the application technology of intelligent manufacturing, and then analyzes the key technology of robot application for reference to relevant workers.

Research on the Effect of Minimum Wage on the Bargaining Power of Chinese Manufacturing

Based on the data of China's industrial enterprise database from 2003 to 2013 and the national minimum wage data at county (city) level, this paper investigates the impact of minimum wage policy on China's manufacturing bargaining power. The results show that (1) the increase of minimum wage will reduce the price markup of Chinese manufacturers and weaken the bargaining power of Chinese manufacturers. (2) Compared with the minimum wage adjustment in the lag period, the cost impact of the minimum wage in the current period is more intense in weakening the bargaining power of the manufacturing industry.

What network roles affect the decline of the embodied carbon emission reduction pressure in China's manufacturing sector foreign trade?

China's manufacturing sector faces pressure to reduce its carbon emissions embodied in foreign trade, yet the influence of the sector's structural and path characteristics on reducing embodied carbon emission (ECE) remains unclear. We developed a research framework to analyze the trade-embodied carbon transfer networks of China's 11 manufacturing industries with 48 economies from 2000 to 2019, identifying the key carbon transfer-in industries and paths. By integrating network characteristics, we assessed the ECE reduction pressure on key manufacturing industries and identified factors hindering its decline. ECE in China's manufacturing foreign trade increased by 273.1% from 2000 to 2019, with the ECE in exports to developing countries since 2012 (255.58 Mt) surpassing exports to developed countries (228.31 Mt). China's medium-low and medium-high technology manufacturing are key carbon transfer-in industries, observed large decreases in the importance of the carbon transfer-in paths from Europe, the United States, Japan, and South Korea, versus notable increases from the Row Asia and Pacific region. Metal manufacturing, non-metallic mineral manufacturing, and chemical manufacturing were the top three industries facing ECE reduction pressure. The network structure indicators obstruct the ECE reduction pressure in metal manufacturing, whereas the network path indicators obstruct the pressure in the other key manufacturing industries. To alleviate the ECE reduction pressure within China's manufacturing foreign trade, China's manufacturing sector should enhance trade cooperation with Asia–Pacific countries. The study's insights offer valuable references for China's formulation of domestic manufacturing emission reduction policies and its participation in international climate negotiations.

Influencing Factors as well as Implementation Path of Synergistic Development of Digitalization and Greening in Manufacturing Industry: Analysis from a Configuration Perspective

<p class="MsoNormal" style="text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">The manufacturing sector must incorporate digitalization and greening as crucial strategies to attain high level of development in the context of the digital economy, given its significant role in quality of environment. Most of existing studies explore net effect of a single or several factors on the digitalization or greening in the manufacturing industry. Limited investigation has been done to integrate various factors impacting the connection and synchronized advancement of digitalization and greening within the manufacturing. Factors affecting the synergy of digitalization and green change within manufacturing are recognized in this article, and a theoretical framework is established to analyze. FsQCA tool is also put into use to survey configurational cases, which contains synergy degree of green digitalization from 30 provinces’ manufacturing in China. Results show that: (1) Between 2007 and 2020, the development level of digital-green integration in the manufacturing industry shows an upward trend. There are three types of collaborative modes, namely, "industrial robot driven" "market development driven" and "market development driven - industrial robot driven", with each mode consisting of at least one to four equivalent pathways. (2) The marketization level and application of industrial robots are the important external and internal pulling forces that drive the synergy degree of digitalization and green change within manufacturing industry, respectively.(3) The influencing factors of the collaborative mode choice of digital-green development within manufacturing industry are complementary, among which, the degree of marketization is similar to the role of industrial robots in the configuration, which are capable to weaken the negative impact from insufficient government R&D investment support under specific conditions; (4) The non-high collaborative state of digital green in manufacturing industry is mainly due to the low level of marketization, openness, and economic development, as well as the low application rate of industrial robots and software products. This study enriches the theoretical understanding on digitalization and green change of manufacturing industry. Meanwhile, it also provides insights to advance the integrated and synergistic advancement of digital green transformation in manufacturing practice.</span></p>

The Mechanism of Digital Economy in Promoting the Transformation and Upgrading of China’s Manufacturing Industry

The era of China's digital economy has arrived, Chinese government is actively exploring how to use the technological changes brought about by the digital economy to promote China's economic development in the next 30 years. As the pillar industry of China's economy, manufacturing has maintained rapid growth in the past 30 years and has made great contributions to China's economic development. However, there are also many challenges at present, such as,China's manufacturing industry is at the low end of the global industrial chain, and the added value is very low; Backward technology and poor competitiveness in the international market. In this context, this paper analyzes the impact of the digital economy on the transformation and upgrading of the manufacturing industry from a qualitative perspective ,attempts to explore the way how China's manufacturing industry can use digital technology to achieve intelligent transformation, value chain upgrading, and industrial structure upgrading, so as to achieve comprehensive transformation and upgrading , in the end, gain long-term development momentum.

The impact of digital transformation on enterprise performance: An empirical analysis based on China's manufacturing export enterprises.

Currently, countries worldwide are embracing digital strategies, enabling enterprises to utilize digital technology, digital supply chains, blockchain, and additional digital measures to increase their competitiveness. This paper analyzed the correlation between the digital transformation of manufacturing export enterprises and their business and export performance, focusing on China's manufacturing export enterprises through empirical analysis. The study investigated the influence of digital transformation on enterprise performance. Using the Resource Based View theory and Trade theory, hypotheses were proposed and regression models were developed to analyze a sample of 1007 enterprises listed on the Shanghai and Shenzhen Stock markets from 2012 to 2019. The study conducted regression analysis, intermediate effect test, robustness test, stage lag, and heterogeneity analysis. The study found that (1) Manufacturing export enterprises listed in the stock market implemented digital transformation, leading to a significant positive impact on their overall performance. (2) Digital transformation led to cost reduction, improved R&D intensity, and enhanced human resources, among other benefits for enterprise performance. (3) According to the fractal analysis, non-state-owned enterprises exhibited more favorable effects on enterprise performance, and the digital transformation of manufacturing export companies in developed regions had a more significant impact on their performance. Finally, the study's empirical results yielded pertinent proposals for digital transformation.

"Machine replacement" or "job creation": How does artificial intelligence impact employment patterns in China's manufacturing industry?

Artificial intelligence (AI), as an important engine for promoting high-quality economic development, should not be overlooked in terms of its impact on the employment of the labor force while promoting the digital and intelligent transformation of industries. In the face of the complex international environment and non-systemic shocks, it is of great significance to explore whether it is "machine replacement" or "job creation" in the process of the integration of AI and industry, as well as the impact of technological progress on the employment pattern of the labor force, in order to promote the economic development, respond to and solve the employment problem. It is of great significance to promote economic development and cope with and solve the employment problem. Based on the task model, this paper analyses the mechanism of the impact of AI on the employment pattern of manufacturing industry. Meanwhile, based on the provincial panel data of China's manufacturing industry from 2011 to 2020, it empirically examines the impact of AI on the total employment, employment structure and employment quality of the labor force, and analyses the multiple responses of AI on the employment pattern of the manufacturing industry. The study shows that: Firstly, the level of development of AI and the total amount of employment is a positive U-shaped relationship, the short term is dominated by the substitution effect, and the long term is dominated by the creation effect; Secondly, with regard to the employment structure, low-skilled labor is more likely to be replaced. The financial, accommodation and catering industries are relatively less affected by the spillover effects of the manufacturing industry; Third, with regard to the employment quality, the gap between urban and rural incomes has eased, with per capita net income of rural residents rising to a higher degree than per capita disposable income of urban residents. Thus, in order to further address the impact of AI on the employment patterns of the labor force, the level of AI development should be increased while expanding employment channels, paying attention to labor force skills training, reinforcing the leading role of developed regions, and accelerating regional integration and urban-rural integration, so as to share the dividends of technological progress.

STUDY ON THE SUSTAINABLE DEVELOPMENT OF CHINA'S EXPORT MANUFACTURING ENTERPRISES IN THE YANGTZE RIVER ECONOMIC BELT UNDER THE BACKGROUND OF "DOUBLE CARBON"

As a core part of the national economy, manufacturing is the key to achieving sustainable economic development. However, the development of China's manufacturing industry faces many problems, such as poor industrial structure, lack of key core technologies, and serious overcapacity, which seriously affect the quality of China's manufacturing industry. The Yangtze River Economic Belt not only has great strategic significance for coordinating the development of the east, middle and west China, but also a demonstration belt for the sustainable development of manufacturing industries. Amid the Chinese government's "double carbon" implementation in 2020, how to transform and upgrade these export manufacturing enterprises and find a path to sustainable development is a major challenge they will have to face. Therefore, on the basis of combing domestic and foreign literature, taking "quality-sustainable development-sustainable development of manufacturing industry" as the logic line, and combine the theory of sustainable development, environmental Kuznets curve theory, equatorial principle theory, and corporate governance theory. Second, it measures and evaluates the sustainable development of manufacturing industry in the Yangtze River Economic Belt from 2007 to 2019 through quantitative research. Conclusions: When assessing the status, the quality level of sustainable development of manufacturing industry in the Yangtze River Economic Belt has been steadily improving, more than 80% of the provinces and cities in the Yangtze River Economic Belt have high and medium quality. Finally, targeted countermeasures and proposals are put forward.

Blockchain technology adoption and sustainable supply chain finance: The perspective of information processing theory

AbstractAlthough the significance of sustainable supply chain finance (SSCF) has been recognized, our knowledge of its antecedents remains limited. Drawing upon organizational information processing theory (OIPT), our research explores how blockchain technology adoption influences SSCF via supply chain visibility, and the moderating role of supply chain ethical leadership. We examine the proposed relationships employing survey data from 317 manufacturers in China. The findings indicate that blockchain technology adoption positively affects SSCF. Supply visibility and demand visibility partially mediate the influence of blockchain technology adoption on SSCF. Furthermore, supply chain ethical leadership undermines the positive influence of blockchain technology adoption on demand visibility. This research enriches our understanding of the antecedents affecting SSCF by offering insights from the perspective of OIPT.

A Feasibility Study on the Application of Artificial Intelligence on the Human Resource Practices among Manufacturing Companies in China

This paper presents a feasibility study on the integration of artificial intelligence (AI) into human resource (HR) practices within the manufacturing sector of China. With the rapid advancement of AI technologies, industries worldwide are exploring its potential applications to streamline operations and enhance efficiency. However, the adoption of AI in HR functions, particularly within manufacturing companies in China, remains relatively unexplored. This study aims to assess the feasibility of implementing AI-driven solutions in various HR processes such as recruitment, training, performance evaluation, and employee engagement. The research methodology involves a combination of qualitative and quantitative approaches. Primary data will be collected through surveys, interviews, and focus group discussions with HR professionals, managers, and employees from a diverse range of manufacturing companies across different regions in China. Additionally, secondary data from relevant literature, industry reports, and case studies will be analysed to gain insights into current trends, challenges, and best practices associated with AI adoption in HR. Key factors influencing the feasibility of AI integration will be examined, including technological readiness, organizational culture, regulatory environment, cost-benefit analysis, and potential socio-economic implications. The study will also explore the perceived benefits and concerns regarding the use of AI in HR practices, such as improved recruitment accuracy, enhanced employee productivity, data privacy concerns, and ethical considerations. Furthermore, the research will identify potential barriers and enablers to successful AI implementation and provide recommendations for policymakers, HR practitioners, and organizational leaders to navigate the challenges and leverage the opportunities presented by AI in the manufacturing sector. By shedding light on the feasibility and implications of AI adoption in HR practices, this study seeks to contribute to the ongoing discourse on the future of work and technological innovation in China's manufacturing industry.

The impact of population aging on FDI: A panel data analysis based on 27 segments in China's manufacturing industry.

Foreign Direct Investment (FDI) is an important part of China's new "double-cycle" development pattern. Among the many factors affecting FDI, will the aging population have an impact on manufacturing, the key industry for FDI? This paper examines the direct and indirect effects of an aging population on FDI using panel data from 27 manufacturing sub-industries in China between 2005 and 2020. It is found that (1) the deepening of the population's aging negatively affects FDI inflows and this result continues to hold after a series of robustness tests. (2) Using labor quantity and labor cost as mediating variables, it is found that the population's aging indirectly affects FDI by reducing labor quantity and increasing labor cost. (3) The heterogeneity analysis study finds that the deepening of the population's aging significantly inhibits FDI in labor-intensive and capital-intensive industries among manufacturing sub-industries, and the inhibitory effect on FDI in technology-intensive industries is not significant. This study provides meso-evidence to support the findings of existing studies and provides suggestions and insights for the government to formulate relevant policies to actively cope with aging.

The impact of digital infrastructure on industrial chain resilience: evidence from China's manufacturing

ABSTRACT The improvement of digital infrastructure is a key support for enhancing the industrial chain resilience and ensuring its normal operation under various unconventional disturbance risks. This paper uses panel regression model, quantile regression model, panel threshold model, and Spatial Durbin Model to explore the internal mechanism and spatial impact effect of digital infrastructure construction on China's manufacturing industry chain resilience. Research found that digital infrastructure does have a positive impact on industrial chain resilience. The estimation results of heterogeneity indicate that compared with underdevelopment or regions with tight finance, the positive promotion effect of digital infrastructure on the manufacturing industry chain in regions with developed economies and abundant regional finance is smaller. In regions with relatively abundant human capital, it is easier to promote the positive effect of digital infrastructure on industrial chain resilience. This paper not only enriches the quantitative research on industrial chain resilience, but also expands the research boundaries of industrial chain resilience. And it provides policy inspiration for promoting digital infrastructure and industrial chain resilience development in a coordinated manner.

Tracing environmental footprint of copper wire rod manufacturing in China

Copper wire rod (CWR) is an indispensable processed material for electrification. However, CWR's environmental impacts and emission reduction pathway haven't been clearly investigated, especially for China where most CWRs are produced. This study quantified provincial environmental impacts and emission reduction potentials of China CWR manufacturing. Key findings are: (1) Most of gate-to-gate environmental impacts of CWR come from energy use, including electricity and natural gas; (2) 42 % of Global Warming Potential of CWR manufacturing can be reduced by electricity transition and energy efficiency increase; (3) There are regional disparities in the environmental impacts and emission reduction potentials of CWR, due to the differences in regional electricity structure, energy efficiency, and production capacities; (4) Cumulative carbon emission reduction of China CWR industry during 2022–2050 can reach 22 Mt CO2 eq., of which Jiangsu, Jiangxi, Anhui, and Guangdong provinces totally account for 71 %. This study supports the green transition of CWR industry.

When Gray Markets Meet “Made in China”: The Impacts of Global Markets and Value-Added Tax Refund

Motivated by the trend that a gray market such as overseas shopping is thriving on E-commerce platforms, this paper investigates the impacts of the gray market on global supply chain management. We consider a multinational firm (MNF) that outsources product manufacturing to a contract manufacturer (CM) in China and sells products in both the China market and overseas market. Owing to the price gap between the two markets, gray firms may purchase products in the low-price market and compete with the MNF in the high-price market. We build MNF-led Stackelberg game models under the two cases where the wholesale price between the CM and MNF is exogenously given or endogenously set by the CM. We also consider the impacts of China’s partial value-added tax (VAT) refund policy and consumers’ valuation discount for gray products. Our analysis shows that the gray market emerges if the two markets are highly asymmetric in size. Moreover, the gray market is more likely to emerge with lower values of valuation discount, substitutability, and VAT loss. We find that both the MNF and CM may be better off with the emergence of the gray market. When the gray market does not emerge but poses a threat, the MNF strategically adjusts her global pricing strategy to prevent the gray market. In this case, the threat of the gray market always hurts the MNF and CM under exogenous wholesale price, but may improve the MNF’s profit under endogenous wholesale price, since the CM is motivated to lower wholesale price to restore demand.

Involving purchasing and supply management in open ecological innovation: the moderating role of digital technologies

ABSTRACT Open Eco-innovation (OEI) is an emerging approach for achieving sustainable development in manufacturing industries. However, studies on internal organisational factors and employee involvement in the development of eco-innovations are lacking. Here, we developed a model to examine whether supply management innovativeness (SMI) and environmental collaboration (EC) with suppliers affect firms’ eco-product and eco-process innovations. The proposed model was tested using data from 328 manufacturers in China. We found that SMI has a direct and positive impact on both eco-product and eco-process innovation. Mediation analysis suggests EC partially mediating this relationship. Additionally, EC had an inverted, U-shaped relationship with eco-products innovation (EPDI)and a positive effect on eco-process innovation (EPCI). While, digital technologies (DT) moderate the relationship between EC and EPDI, they do not affect EPCI. This study presents an opportunity for supply management managers to understand these challenges more comprehensively and add value to the eco-innovation process.

Improving Supply Chain Human-Machine Systems by the Analysis of Departmental-Level User Characteristics

In this study, the authors aimed to enhance supply chain efficiency at Company A, a key player in China's manufacturing sector, by focusing on user-centric system design. The approach involved analyzing departmental-level user characteristics to inform the development of an optimized human-machine interface. To achieve this, they conducted focus groups and card sorting exercises, identifying specific user needs across departments like planning, purchasing, receiving, warehousing, production, and quality control. The application of these insights into system design led to significant operational improvements. Notably, time savings were observed in departments such as receiving (74.44%) and quality control (41.86%). This tailored approach underscored the importance of understanding and integrating user characteristics in supply chain systems, leading to enhanced productivity and a competitive edge. The study demonstrates the impact of user-focused system design in improving both efficiency and user satisfaction in supply chain management.

Service-oriented Transformation in Manufacturing and The Deepening of Corporate Value Chains

This paper examines the challenges and opportunities faced by China's manufacturing industry in the context of global value chain division and technological revolution. It explores the impact of manufacturing servitization on enterprise value chain advancement, aiming to address how this transformation affects climbing the enterprise value chain. Drawing on data from Chinese industrial enterprises between 2003 and 2013, this study employs micro-level empirical analysis using a two-way fixed effect regression model and 2SLS instrumental variable method to establish a causal relationship between manufacturing servitization and enterprise value chain advancement. The findings reveal that manufacturing servitization has a significant positive impact on climbing the enterprise value chain, particularly for technology-intensive, capital-intensive, and resource-intensive enterprises. By providing empirical evidence and policy implications for China's manufacturing industry transformation and upgrading efforts, this research enriches existing literature on both manufacturing servitization and value chain advancement.

Biased innovation and network evolution: digital driver for green innovation of manufacturing in China

ABSTRACT The study aims to explore the spatial association network characteristics of biased green innovation in the manufacturing sector and its core drivers. This study constructs a Malmquist-Luenberger decomposition index model to identify the input and output biases of green technological innovation (GIIM and GIOM) in the manufacturing industry. This study uses a modified gravity model and social network analysis method to conduct a robust assessment of GIIM spatial association network of 30 provinces in China from 2012 to 2021. The results show: (1) The GIIM association network structure is stable and has good accessibility, with close connections between provinces and blocks, and significant spillover effects between provinces. (2) The regional network shows a “core-periphery” spatial variation, with the core area expanding and the peripheral area shrinking. (3) The digital transformation characteristics of the network components and the intensity of environmental regulation have a significant impact on GIIM.

The hidden linkage of corporate efficiency and green innovation with human resource management practices: a newly perspective from China.

It is disappointing that despite having access to human resources at very little cost, rising countries' administrations are so ineffective. It is often believed that an organization's unheralded employees hold the keys to its success or failure. A person's mood and demeanor may be influenced by a variety of factors. Human resource management (HRM) methods have been created and used by scientists all over the globe in an effort to maximize the potential of their most important asset. Eco-friendly inventions are crucial to the survival of humanity and the prosperity of enterprises throughout the world. Therefore, the purpose of this research is to look at how green innovation affects both the environment and businesses. Green process metrics and green product metrics, for example, may have an impact on green innovation, which is why these metrics are important to track. The second primary goal of this research is to learn how the commitment and HR practices of top management shape the link between green innovation and economic and environmental success. A self-reported poll was taken by 370 employees in China's manufacturing industry. A partial least square structural equation modelling was used to examine the data collected between April 2021 and February 2022. This research employed confirmatory component analysis, a standard technique of structural equation modelling (SEM) for examining both overt and covert variables and indicators, to assess the reflecting indicators measurement model. The findings suggest that HRM has an effect on green innovation that is both positive and statistically significant. The environment and the economy benefit greatly from green innovations. Relationships between HRM and green innovation have no unfavorable implications on business results or the environment. However, human resource management has the potential to boost organizations' prosocial impact. In a three-way interaction (moderated moderation) model, organizational success is found to have a significant role, whereas environmental and organizational success play just a little one. To improve economic and environmental outcomes across all sectors, this study adds to the existing body of knowledge and argues for the wider implementation of green innovation practices. Human resource managers play a crucial role in developing a company's culture and values. The findings suggest that with leadership's blessing, green technologies may spread across a company.

Multi-resource constrained flexible job shop scheduling problem with fixture-pallet combinatorial optimisation

There is a lack of research on the flexible job shop scheduling problem (FJSP) considering limited fixture-pallet resources in multi-product mixed manufacturing workshops. However, field research in a leading engine manufacturer in China has revealed that fixture-pallet resources are a key factor limiting capacity breakthroughs although they play an auxiliary role in the production process. Thus, in this paper, we propose a methodology for the multi-resource constrained flexible job shop scheduling problem with fixture-pallet combinatorial optimisation. First, a mixed integer programming model with machine-fixture-pallet constraints is constructed aiming to minimize makespan. Then, a novel genetic algorithm integrated with feasibility correction strategy and self-learning variable neighbourhood search (VNS) is proposed to address the complicated scheduling problem, where the feasibility correction strategy is designed to solve potential conflict between machine selection and fixture selection chromosomes and self-learning VNS is executed to further improve the optimisation capability. Moreover, the effectiveness and efficiency of proposed algorithm are demonstrated by computational experiments with real data from cooperated engine manufacturing plant, which would provide convincing support for real production scheduling under complex scenarios.