A machine learning framework for exploring the relationship between supply chain management best practices and agility, risk management, and performance
This study provides a comprehensive analysis of supply chain management practices based on survey responses from a sample of enterprises. Through descriptive statistics, hypothesis testing, predictive modeling, advanced analytics techniques such as classification, clustering, and association ru...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Growing Science
2025-01-01
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| Series: | Management Science Letters |
| Online Access: | http://www.growingscience.com/msl/Vol15/msl_2024_29.pdf |
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| Summary: | This study provides a comprehensive analysis of supply chain management practices based on survey responses from a sample of enterprises. Through descriptive statistics, hypothesis testing, predictive modeling, advanced analytics techniques such as classification, clustering, and association rule mining, the research offers valuable insights into key areas of collaboration, quality management, technology adoption, agility, risk management, and customer responsiveness within supply chains. The findings highlight the importance of strategic integration, proactive problem-solving, customer-centric practices, and agility in meeting changing demands. The study also identifies distinct profiles of practice adoption and reveals intricate relationships between different supply chain practices. Overall, the research contributes to a deeper understanding of supply chain dynamics and offers actionable insights for improving operational performance and strategic decision-making. |
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| ISSN: | 1923-9335 1923-9343 |