Evaluation of Enterprise Management Innovation in Manufacturing Industry Using Fuzzy Multicriteria Decision-Making under the Background of Big Data

Yan, Yi; De-jiang, Chu

doi:10.1155/2021/2439978

Cited by 9 publications

(8 citation statements)

References 29 publications

(24 reference statements)

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“…(1) In terms of research methods, the current research methods for industrial evaluation include a factor analysis [45], DEA [26], and AHP [5] method, which rely on indicator data. However, due to the large number of cities in China, the statistical indicators of each city are not necessarily the same.…”

Section: Discussionmentioning

confidence: 99%

“…Yin et al (2021) [23] evaluated the efficiency of green innovation in the equipment manufacturing industry and analyzed its dynamic evolution trend. Yan et al (2021) [5] used the AHP to analyze the performance management of manufacturing enterprises. Singh et al (2019) [24] used the AHP to evaluate the performance sustainability of Indian manufacturing enterprises.…”

Section: Evaluation Methodsmentioning

confidence: 99%

“…On the other hand, there are many evaluation methods for the industry, for example, DEA method [4], and analytic hierarchy process (AHP) method [5]; due to the limitations of the objective indicators of the research sample, these methods can only be applied to a certain range of cities for research. If the statistical indicators of two urban clusters are inconsistent, it is difficult to achieve comprehensive measurement and comparative analysis.…”

Section: Introductionmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: Evaluation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Evaluation Prediction System for Urban Advanced Manufacturing Development

Dou,

Sun,

Zhu

et al. 2023

Systems

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“…Jana et al proposed a www.ijacsa.thesai.org multi-attribute decision method for enterprise financial performance evaluation, applying the new Dombi hybrid operators, which work on parameters have the advantage of good adaptability to complete financial performance assessment through intuitionistic fuzzy methods [9]. Yan Y et al researchers analysed the performance appraisal management of manufacturing enterprises based on the Analytic Hierarchy Process (AHP) of fuzzy multi-criteria decision making to establish an enterprise performance management evaluation system, and this This exploration can provide a theoretical basis for improving the management innovation ability of manufacturing enterprises, and also has important reference value for expanding the market competitiveness of manufacturing enterprises [10]. Fang H applied the enterprise value management theory to the performance evaluation of university discipline construction members, established a performance value evaluation model, and applied the AHP method and quantitative research methods to costs and the functions achieved were evaluated [11].…”

Section: Related Workmentioning

confidence: 99%

“…, LS and maximizing   ln , LS are equivalent, the objective of training the RBM becomes maximizing the log-likelihood function, as in equation (10).…”

Section:  mentioning

confidence: 99%

A Deep Learning-based Model for Evaluating the Sustainability Performance of Accounting Firms

Chen¹

2022

IJACSA

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The harmonious and stable development of society is strongly related to the sustainable development of enterprises. In order to better face the challenges of environmental resources, sustainable development must be included in the development focus of accounting enterprises. The research proposes a performance evaluation model based on deep learning, improves RBMs model on the basis of deep belief network (DBN), improves the accuracy of the model through reverse fine-tuning technology, and effectively combines multiple restricted Boltzmann machines (RBMs) and Softmax classifiers to build a modular multi classification model to complete the sustainable development performance evaluation of accounting enterprises. The performance of RBM fine tuning classifier is higher than that of RBM expression and PCA (Principal Component Analysis) expression, which mainly shows the effectiveness and stability of feature extraction. The network output results of test samples are converted into prediction performance evaluation. The model is evaluated by average precision (AP), average recall (AR), and prediction accuracy. The AP, AR, and prediction accuracy of the proposed method are 86.95%, 89.74%, and 88.29% respectively, which are higher than Softmax classifiers, Back Propagation (BP) neural networks, and DBN based Softmax methods, It shows that this method is superior to other algorithms in the application of performance evaluation model for sustainable development of accounting enterprises, and it is feasible and effective, which is of great significance to the establishment of performance evaluation model for the accounting industry.

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Research on the Transformation of Financial Accountants in Manufacturing Enterprises Under the Background of Cloud Computing

Han¹

2023

Application of Big Data, Blockchain, and Internet of Things for Education Informatization

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Evaluation of Enterprise Management Innovation in Manufacturing Industry Using Fuzzy Multicriteria Decision-Making under the Background of Big Data

Cited by 9 publications

References 29 publications

The Evaluation Prediction System for Urban Advanced Manufacturing Development

The Evaluation Prediction System for Urban Advanced Manufacturing Development

A Deep Learning-based Model for Evaluating the Sustainability Performance of Accounting Firms

Research on the Transformation of Financial Accountants in Manufacturing Enterprises Under the Background of Cloud Computing

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