Research and Application of Capability Maturity Model for Chinese Intelligent Manufacturing

Hu, Jingyi; Gao, Sini

doi:10.1016/j.procir.2019.05.013

Cited by 23 publications

(6 citation statements)

References 1 publication

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“…Government fiscal spending can promote manufacturing equipment and improve productivity, but these activities may disrupt the market and lead to an excessive reliance on the government and decrease the desire to innovate. None of these are conducive to the independent development of the manufacturing industry [34,35]. Although not significant, the industrial scale has a positive influence on the intelligentization of the manufacturing industry.…”

Section: Empirical Results Analysismentioning

confidence: 95%

An Evaluation of the Yangtze River Economic Belt Manufacturing Industry Level of Intelligentization and Influencing Factors: Evidence from China

2021

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Over recent decades, the application of artificial intelligence methods in manufacturing has led to new spheres of research such as the Internet of Things, Cyber–Physical Systems, and Cloud Computing and Big Data, leading to the so-called Industry 4.0. However, to date, little research has been geared towards assessing the factors that influence intelligent manufacturing on a regional scale. Addressing this problem, this paper constructs an evaluation index system for the Yangtze River Economic Belt (YREB) intelligent manufacturing sector using eleven years (2008–2018) of provincial panel data. The entropy method is applied to three evaluation criteria, namely intelligent innovation, equipment, and profit, to construct an evaluation index system. An analysis of the results revealed that the level intelligentization of the manufacturing industry of the YREB increases yearly, and that intelligent innovations are notably occurring at a faster rate than profits. Disproportional enterprise returns on investment have occurred, which decreases enterprise motivation to be innovative in the first place. Additionally, it was also observed that FDI, financial development, government intervention, and the level of opening-up were the primary factors modulating regional intelligent manufacturing levels.

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Section: Empirical Results Analysismentioning

confidence: 95%

An Evaluation of the Yangtze River Economic Belt Manufacturing Industry Level of Intelligentization and Influencing Factors: Evidence from China

2021

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“…On this basis, the microgrid Industry 4.0 physical layer, adjustment layer, coordination layer, 6-layer system framework of connection layer, network layer, and application layer were proposed. Hu and Gao [14] researched and designed the building structure health monitoring Industry 4.0 system and studied how to build a reusable architecture so that physical testing and computer simulation testing can be precisely combined, which can effectively support mixed testing of building structures. Wang et al [15] studied the use of wireless sensor networks (WSN) to construct distributed building structure health monitoring and defect location methods.…”

Section: Related Workmentioning

confidence: 99%

A Construction Method of Intelligent Manufacturing System under Industry 4.0 Model

Xiao

Zeng

2021

Scientific Programming

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Starting from the current problems facing Industry 4.0, this article analyzes the changes in the macro and industrial environment that Industry 4.0 faces and explains the problems, opportunities, and strategies for the manufacturing industry in the external environment. First, the reference system of the intelligent manufacturing system, the current status, and the existing problems of industrial production management are analyzed through the investigation of the status quo of industrial production and management. This puts forward the detailed requirements of the industrial intelligent manufacturing system in the data acquisition layer, data storage layer, and analysis and decision support layer and then designs the hierarchical structure of the industrial intelligent manufacturing system. Subsequently, it adopts design methods and lists product manufacturing costs, pointing out that Industry 4.0 requires industrial transformation, and finally proposes the strategic direction of smart manufacturing in combination with the Industry 4.0 network strategy. At the same time, in view of the problems of long parameter measurement time and untimely system feedback in the existing koji-making process, an online parameter measurement method based on network optimization is proposed. On the basis of the neural network, an industrial neural network with double hidden layers and self-feedback of the output layer is proposed. Through algorithm comparison experiments, the proposed parameter prediction model based on industrial neural network has better prediction results and higher accuracy. Finally, a comparison of cost, quality, delivery time, etc., before and after the implementation of Industry 4.0 intelligent manufacturing is carried out. An intelligent solution is proposed, the implementation goal is formulated, and the implementation is gradually implemented in stages, and finally an intelligent upgrade and transformation are realized. It is shown in many aspects that intelligent manufacturing provides a powerful means for enterprises to achieve agility, virtualization, lean, integration, and collaboration, and it can bring efficiency, reliability, and safety to the manufacturing process of enterprises.

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“…This model has been tested and proven in the area of management studies that it is very important to consider CSFs that will help an activity or organization to grow and also achieve effective and sustainable goals (Yalegama, Chileshe, & Ma, 2016). Iqbal, Khan, and Minhas (2018), Hu and Gao (2019), Ofori and Deffor (2013) and many more asserted that CMM is widely used when implementing an activity or project of a sort. It shows the particular activity that is necessary at a certain stage of an activity, organizations must be extra careful when implementing a methodology in the system that would be used for the lifetime of the organization.…”

Section: Underpinning Theoriesmentioning

confidence: 99%

Preliminary Investigations into the Factors Affecting Successful Implementation of Project Management Frameworks and its Effects on Project Success: Evidence from Ghana

Taana¹,

Raju²

2020

Int. j. bus. adm. stud

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Whether undertaken by a public or private organization, the successful completion of every project is of primary concern. However, literature has confirmed that most project success depends on adhering to good practices and are keenly documented in the Project Management Frameworks (PMFs). The purpose of this study is to explore the factors that affect the implementation of PMFs in Ghana. This is motivated by the dearth of academic literature concerning PMFs and their successful implementation in Ghana. A conceptual framework was developed grounded on the Capability Maturity Model (CMM) and the Critical Success Factors (CSFs) theory to achieve this aim. Four independent variables and the dependent variable were hypothesized to be tested to establish the success factors affecting PMFs implementation. Data were collected from 105 project management consultants, contractors, and other key stakeholders in Ghana and analyzed to establish the relationships between the hypothesized constructs. The findings indicate that Stakeholder Involvement (SI) and Top Management Support (TMS) influence the adoption of PMFs. However, Acceptability and Organizational Maturity (OM) does not influence the adoption of PMFs. Moreover, PMFs does not influence Project Success. This preliminary study has given us insights into the factors to consider when adopting PMFs in Ghana. Practically, key stakeholders should encourage project management consultants and contractors to accept the PMFs, which, when adhered to, will lead to project success which is contrary to what this study found.

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Research and Application of Capability Maturity Model for Chinese Intelligent Manufacturing

Cited by 23 publications

References 1 publication

An Evaluation of the Yangtze River Economic Belt Manufacturing Industry Level of Intelligentization and Influencing Factors: Evidence from China

An Evaluation of the Yangtze River Economic Belt Manufacturing Industry Level of Intelligentization and Influencing Factors: Evidence from China

A Construction Method of Intelligent Manufacturing System under Industry 4.0 Model

Preliminary Investigations into the Factors Affecting Successful Implementation of Project Management Frameworks and its Effects on Project Success: Evidence from Ghana

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