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<p>The profound impact of the digital transformation and upgrading of China’s manufacturing industry on the global supply chain forms the core of this analysis. This paper introduces a measurement model for assessing the manufacturing digital transformation and upgrading, which includes constructing an indicator system and calculating the weights of these indicators using the entropy value method. Subsequently, the raw data for each indicator are processed through the maximum-minimum value method. Furthermore, this study advances a global supply chain performance assessment SCOR model tailored to the specifics of manufacturing digital transformation and upgrading. It conducts a quantitative analysis of key indicators, typically challenging to quantify, thus deriving their numerical expressions and examining the effects of performance changes. In 2023, China’s comprehensive index for transformation and upgrading escalated to 83.84%, a significant increase from 6.78% in 1999. In a simulation experiment focusing on the integrated circuit manufacturing industry, the digital transformation and upgrading of China’s manufacturing sector is shown to enhance production inventory levels within the global supply chain and reduce delivery times. The Estimated Lead Time (ELT) service level improved by 5%, reaching 100%. At the same time, revenues and profits saw increments of approximately 44.66 million and 32.11 million US dollars, respectively, relative to scenarios absent of such digital advancements.</p>
</abstract>

The profound impact of the digital transformation and upgrading of China’s manufacturing industry on the global supply chain forms the core of this analysis. This paper introduces a measurement model for assessing the manufacturing digital transformation and upgrading, which includes constructing an indicator system and calculating the weights of these indicators using the entropy value method. Subsequently, the raw data for each indicator are processed through the maximum-minimum value method. Furthermore, this study advances a global supply chain performance assessment SCOR model tailored to the specifics of manufacturing digital transformation and upgrading. It conducts a quantitative analysis of key indicators, typically challenging to quantify, thus deriving their numerical expressions and examining the effects of performance changes. In 2023, China’s comprehensive index for transformation and upgrading escalated to 83.84%, a significant increase from 6.78% in 1999. In a simulation experiment focusing on the integrated circuit manufacturing industry, the digital transformation and upgrading of China’s manufacturing sector is shown to enhance production inventory levels within the global supply chain and reduce delivery times. The Estimated Lead Time (ELT) service level improved by 5%, reaching 100%. At the same time, revenues and profits saw increments of approximately 44.66 million and 32.11 million US dollars, respectively, relative to scenarios absent of such digital advancements.

Analysis and Research on the Impact of Digital Transformation and Upgrading of China’s Manufacturing Industry on Global Supply Chains

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The profound impact of the digital transformation and upgrading of China’s manufacturing industry on the global supply chain forms the core of this analysis....

Analysis and Research on the Impact of Digital Transformation and Upgrading of China’s Manufacturing Industry on Global Supply Chains

Publié en ligne: 07 juin 2024

Pages: -

Reçu: 24 févr. 2024

Accepté: 30 avr. 2024

DOI: https://doi.org/10.2478/amns-2024-1451

Mots clés
Global supply chain, Entropy method, SCOR model, Supply chain performance

© 2024 Peng Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis and Research on the Impact of Digital Transformation and Upgrading of China’s Manufacturing Industry on Global Supply Chains

Publié en ligne: 07 juin 2024

Pages: -

Reçu: 24 févr. 2024

Accepté: 30 avr. 2024

DOI: https://doi.org/10.2478/amns-2024-1451

Mots clésGlobal supply chain, Entropy method, SCOR model, Supply chain performance

© 2024 Peng Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Global supply chain, Entropy method, SCOR model, Supply chain performance