Evaluation of Factors Influencing the Sustainable Development of the Marine Equipment Manufacturing Industry Cluster

Li, Huanyan; Xu, Wei; Zhang, Yuanyuan; Wu, Tingting

doi:10.2112/si106-099.1

Cited by 7 publications

(4 citation statements)

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“…However, most of them are based on the construction of an evaluation index, and there are almost no machine learning methods. Li et al (2020) [22] constructed the measurement model for the equipment manufacturing industry. In addition, it analyzes the indicators affecting the development of the industrial cluster and provides suggestions for its development.…”

Section: Evaluation Methodsmentioning

confidence: 99%

The Evaluation Prediction System for Urban Advanced Manufacturing Development

Dou,

Sun,

Zhu

et al. 2023

Systems

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With the rapid development of the economy, it is important to reasonably evaluate the development status of the regional manufacturing industry. Given this, this article expands the evaluation indicators of urban advanced manufacturing (UAM) from the perspective of the push–pull-mooring (PPM). Then, it uses a machine learning (ML) method to predict the evaluation results of other cities through a small amount of sample data. The results show that: (1) From the current development status of UAM in Guangdong Province (GD), cities in the Pearl River Delta region occupy a dominant position. However, cities in eastern, western, and mountainous regions have strong development potential and lead cities. Therefore, each region has cities with high levels of development and has a demonstrative role. (2) By comparison, it was found that the overall development level of UAM in GD is not significantly different from that of the Yangtze River Economic Belt. However, due to significant differences in their extreme values, the proportion of cities above the average in the overall population is relatively small. This indirectly proves that GD’s UAM not only has a phased nature, but also has a demonstrative role. (3) The prediction effect of the perceptron model is better than other methods. Although neural network models have better prediction performance than other machine learning models, they should not overly rely on complex network structure prediction data. By comparing the results, the reliability is verified. Finally, according to the life cycle theory, we propose a targeted development path for different UAM.

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Section: Evaluation Methodsmentioning

confidence: 99%

The Evaluation Prediction System for Urban Advanced Manufacturing Development

Dou,

Sun,

Zhu

et al. 2023

Systems

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“…Menurut para ahli, over kapasitas ini terjadi karena penambahan produksi. Hal-hal ini harus menjaga keseimbangan antara kapasitas produksi dan permintaan pasar, menghindari undercapacity yang dapat menyebabkan hilangnya peluang bisnis dan overcapacity yang dapat meningkatkan biaya produksi yang tidak perlu [12].…”

Section: Pendahuluanunclassified

Capacity Strategies a Comparative Perspective in Manufacturing vs Service Industries

Rayo,

Inaray,

Lule

2023

INFEB

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The main aim of this research is to provide a deeper understanding of how wise capacity planning can become a key success factor across various industrial sectors. It can have a positive impact on organizations by achieving optimal operational efficiency and delivering services or products that satisfy consumers. This study employs a qualitative approach focusing on literature review to explore and comprehend capacity strategies in a comparative context between manufacturing and service industries. Capacity planning holds significant importance for the expansion of all companies, particularly those operating in industrial sectors. Businesses can leverage these strategies to meet client needs, save on operational costs, and facilitate sustainable expansion. It’s crucial for companies to comprehend and address current obstacles. Key factors include accurate demand forecasting, effective company constrain management, and the ability to adapt and respond to market fluctuations. In today’s era of digitalization, companies have opportunities to employ sophisticated technologies like ERP, big data analytics, and machine learning to effectively achieve their capacity planning goals. Implementing these tools can result in more accurate forecasting and enable faster adaptation to market fluctuations. To enhance operational efficiency and align output with business objectives, companies need to engage in effective planning.

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“…Most of the Chinese offshore engineering equipment manufacturing enterprises are only in the initial stage; the management mode is relatively backward, the overall level of technological innovation, quality control management, etc., is low, the product production period is long, and product quality is poor [6][7][8]. Mainly relying on the low price to attract orders, holding a small proportion of orders, profits are also low, and the weak profitability, in turn, further limits the development of China's offshore engineering equipment manufacturing industry [9][10].…”

Section: Introductionmentioning

confidence: 99%

Research on the quality control strategy of marine engineering based on big data technology

Yuan,

Yu,

Sun

et al. 2023

Applied Mathematics and Nonlinear Sciences

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The research of quality control strategy of marine engineering using big data technology is to better promote the enterprise to reduce cost and increase efficiency. In this paper, starting from the project quality control system, a quality control risk evaluation model is constructed based on hierarchical analysis and fuzzy comprehensive evaluation by means of the fuzzy comprehensive evaluation of affiliation degree. For the model constructed based on AHP-FCR in this paper, experimental analysis of quality control influencing factors and fuzzy comprehensive evaluation examples were conducted by establishing indicators. From the influence factors, the severity of engineering personnel risk and engineering machine risk are 19.289 and 20.216, respectively, both of which are significant levels of influence. From the fuzzy comprehensive evaluation, 49.09% of the experts voted that the level of quality control risk of S Company’s offshore engineering module oilfield construction project is now low and in an acceptable state. It can be concluded that the AHP-FCR model with big data technology can realize the quality control risk assessment of offshore engineering and also provides theoretical support for companies to solve the risk.

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Evaluation of Factors Influencing the Sustainable Development of the Marine Equipment Manufacturing Industry Cluster

Cited by 7 publications

References 0 publications

The Evaluation Prediction System for Urban Advanced Manufacturing Development

The Evaluation Prediction System for Urban Advanced Manufacturing Development

Capacity Strategies a Comparative Perspective in Manufacturing vs Service Industries

Research on the quality control strategy of marine engineering based on big data technology

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