The application of big data algorithm in the intelligent manufacturing supply chain wisdom embodiment

2021 ◽  
Author(s):  
Peipei Xin
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Intelligent Manufacturing ◽  
Manufacturing Supply Chain

scholarly journals Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain

2017 ◽  
Vol 9 (4) ◽  
pp. 608 ◽  
Author(s):  
Venkatesh Mani ◽  
Catarina Delgado ◽  
Benjamin Hazen ◽  
Purvishkumar Patel
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Supply Chain Risk ◽  
Manufacturing Supply Chain

Research on Evaluation of Intelligent Manufacturing Capability and Layout Superiority of Supply Chain by Big Data Analysis

2022 ◽  
Vol 30 (7) ◽  
pp. 0-0
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Manufacturing Industry ◽  
Cost Model ◽  
Intelligent Manufacturing ◽  
Research On Evaluation ◽  
Manufacturing Capability ◽  
Enterprise Collaboration ◽  
Manufacturing Mode ◽  
Internet Of Things Technology

With the rise of cloud computing, big data and Internet of Things technology, intelligent manufacturing is leading the transformation of manufacturing mode and industrial upgrading of manufacturing industry, becoming the commanding point of a new round of global manufacturing competition. Based on the literature review of intelligent manufacturing and intelligent supply chain, a total factor production cost model for intelligent manufacturing and its formal expression are proposed. Based on the analysis of the model, 12 first-level indicators and 29 second-level indicators of production line, workshop/factory, enterprise and enterprise collaboration are proposed to evaluate the intelligent manufacturing capability of supply chain. This article also further studies the layout superiority and spatial agglomeration characteristics of intelligent manufacturing supply chain, providing useful reference and support for enterprises and policy makers in the decision-making.

Product Quality Tracing in Manufacturing Supply Chain Based on Big Data Technology

2020 ◽  
Vol 13 (4) ◽  
pp. 340-351
Author(s):  
Yin Huang ◽  
Shumin Huang ◽  
Yichen Zhang ◽  
Xue Yang ◽  
Runda Liu
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Product Quality ◽  
Patent Analysis ◽  
Development Mode ◽  
Product Lifecycle ◽  
Whole Process ◽  
Manufacturing Supply Chain ◽  
Process Product ◽  
Big Data Technology

Background: Big data technology has been widely used in manufacturing supply chain management. However, traditional big data technology has some limitations, and it cannot achieve the continuous improvement of whole-process product quality tracing. Objective : The purpose of this study is to overcome the limitations by patents analysis and provide new big data technology and technical modes to make the continuous improvements of whole-process product quality tracing for achieving effective product lifecycle management based on big data technology. Methods: The research method, patent analysis, and comparative analysis are employed in this study to analyze product quality tracing in the manufacturing supply chain based on big data technology. Moreover, the procedure and steps of the new big data technology - Product Digital Twin (PDT), and its technical modes are designed by process design methods. Its key technologies are also analyzed and compared with traditional big data technology by the comparative study. Results: The research achieves the continuous improvements of whole-process product quality tracing based on new big data technology - PDT by patent analysis. The formation process and behavior of manufactured products in the realistic environment are simulated, monitored, diagnosed, predicted, and controlled. In this way, the high-efficient coordination in various stages of the product lifecycle is propelled fundamentally and the continuous improvements of the whole-process product quality tracing based on big data technology is analyzed. Conclusion: Three new technical modes based on big data technology are predicted for future researches and patents, namely, the immersive development mode integrating big data and the virtual reality technology, the knowledge-based multivariant coordinated development mode, and the lifecycle extended development model based on multi-domain interoperability.

scholarly journals Measuring the Environmental Maturity of the Supply Chain Finance: A Big Data-Based Multi-Criteria Perspective

Logistics ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 22
Author(s):  
Hisham Alidrisi
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Multicriteria Decision Making ◽  
Analytic Network Process ◽  
Assessment Model ◽  
Self Assessment ◽  
Interpretive Structural Modeling ◽  
Supply Chain Finance ◽  
Network Process ◽  
Order Preference

This paper presents a strategic roadmap to handle the issue of resource allocation among the green supply chain management (GSCM) practices. This complex issue for supply chain stakeholders highlights the need for the application of supply chain finance (SCF). This paper proposes the five Vs of big data (value, volume, velocity, variety, and veracity) as a platform for determining the role of GSCM practices in improving SCF implementation. The fuzzy analytic network process (ANP) was employed to prioritize the five Vs by their roles in SCF. The fuzzy technique for order preference by similarity to ideal solution (TOPSIS) was then applied to evaluate GSCM practices on the basis of the five Vs. In addition, interpretive structural modeling (ISM) was used to visualize the optimum implementation of the GSCM practices. The outcome is a hybrid self-assessment model that measures the environmental maturity of SCF by the coherent application of three multicriteria decision-making techniques. The development of the Basic Readiness Index (BRI), Relative Readiness Index (RRI), and Strategic Matrix Tool (SMT) creates the potential for further improvements through the integration of the RRI scores and ISM results. This hybrid model presents a practical tool for decision-makers.

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