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Exploring the Application and Optimization Strategy of the LMBP Algorithm in Supply Chain Performance Evaluation.

Fei Gu1

  • 1Capital University of Economics and Business, Beijing 100070, China.

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Summary

Big data analysis capability enhances supply chain resilience and performance. This study examines its impact on supply chain performance management across different enterprise scales using the LMBP algorithm.

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Area of Science:

  • Supply Chain Management
  • Business Analytics
  • Operations Research

Background:

  • Emerging technologies like big data and AI significantly impact supply chain management.
  • Big data analysis capability is crucial for supply chain resilience management.
  • Understanding the interplay between big data, resilience, and performance is vital for enterprises.

Purpose of the Study:

  • To analyze the impact of big data analysis capability on supply chain performance.
  • To investigate the relationship between supply chain performance management, collaboration, resilience elements, and big data analysis capability.
  • To examine how enterprise scale influences the effect of big data analysis on supply chain performance.

Main Methods:

  • Performance management perspective based on supply chain resilience theory.
  • Analysis of big data analysis capability and its effect on supply chain performance.
  • Utilizing the LMBP algorithm for supply chain performance evaluation and optimization.

Main Results:

  • Big data analysis capability significantly impacts supply chain resilience and performance.
  • The influence of big data analysis on supply chain performance varies with enterprise scale.
  • The study provides insights into optimizing supply chain performance through data analytics.

Conclusions:

  • Big data analysis capability is a key driver of enhanced supply chain performance and resilience.
  • Tailoring big data strategies to enterprise scale is essential for maximizing benefits.
  • The LMBP algorithm offers a valuable tool for supply chain performance evaluation and optimization.