Research on Intelligent Supply Chain for Intelligent Manufacturing

Leveraging computer systems and information technologies, intelligent manufacturing demonstrates enhanced collaborative capabilities and elevated decision-making capacity. From a digital cognitive framework and information system-oriented view, this paper focuses on the process of supply chain digital transformation, interpreting the methodology and approaches to eliminate obstacles such as the lack of data standards and inaccurate data analysis, lack of supply chain resilience, contradiction between customized and operational efficiency in production systems, uncooperative market conditions and other internal or external factors. To address these challenges, the systematic integration of big data analytics, deep learning-based intelligent optimization, and blockchain-enabled collaborative mechanisms establishes practical technical pathways. This convergence augments supply chain cognitive capabilities, advances intelligent production scheduling competencies, amplifies market adaptive intelligence, and facilitates sustainable evolution of intelligent supply chain ecosystems.

Authors

Publication Details

Journal
Journal of Computer Signal and System Research
Published
2025-03-22
DOI
https://doi.org/10.71222/9029wy98
Primary Topic
Industrial Technology and Control Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Research on Intelligent Supply Chain for Intelligent Manufacturing

Jie Li
Journal of Computer Signal and System Research
Industrial Technology and Control Systems
article

Research on Intelligent Supply Chain for Intelligent Manufacturing

Jie Li
article en

Abstract

Leveraging computer systems and information technologies, intelligent manufacturing demonstrates enhanced collaborative capabilities and elevated decision-making capacity. From a digital cognitive framework and information system-oriented view, this paper focuses on the process of supply chain digital transformation, interpreting the methodology and approaches to eliminate obstacles such as the lack of data standards and inaccurate data analysis, lack of supply chain resilience, contradiction between customized and operational efficiency in production systems, uncooperative market conditions and other internal or external factors. To address these challenges, the systematic integration of big data analytics, deep learning-based intelligent optimization, and blockchain-enabled collaborative mechanisms establishes practical technical pathways. This convergence augments supply chain cognitive capabilities, advances intelligent production scheduling competencies, amplifies market adaptive intelligence, and facilitates sustainable evolution of intelligent supply chain ecosystems.

Journal of Computer Signal and System ResearchVol. 2(2)
Openalex Percentile: Top 94%
Industrial Technology and Control Systems
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