An agility and performance assessment framework for supply chains using confirmatory factor analysis and structural equation modelling

This study examines the impact of agile practices on supply chain performance measurements in manufacturing firms. Following COVID-19, there have been operational and logistics disruptions in manufacturing firms and supply chains worldwide. We study the link between supply chain performance and agile manufacturing practices by designing experimental research and collecting data from 340 responses from manufacturing firms. The experimental design proposed in this study uses a confirmatory factor and reliability analysis and smart-partial least square structural equation modeling. This research demonstrates the positive effect of agile supply chain strategies on manufacturing companies’ performance. The values obtained from the experiment support the dependability and effectiveness of the study. The research is supported by factors like customer involvement, facility management, supply chain responsiveness, strategic management, and supplier relationships but is undermined by technology utilization and supply chain contracts. The study will aid companies in combining agile with more conventional approaches to better adapt to market volatility and fierce global competition. Developing core competencies and acquiring a competitive advantage contribute to sustained advantage in the manufacturing industry. This study further outlines the need to understand how supply chains perform when agile practices are adopted. Figure: Theoretical framework of SCP

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Publication Details

Journal
Supply Chain Analytics
Published
2024-11-23
DOI
https://doi.org/10.1016/j.sca.2024.100093
Citations
7
Primary Topic
Quality and Supply Management
Type
article
Field-Weighted Citation Impact
3.70
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article

An agility and performance assessment framework for supply chains using confirmatory factor analysis and structural equation modelling

Rohit Raj, Akhil NSB, Tanmoy De, Phanitha Kalyani Gangaraju et al.
7 citations
Supply Chain Analytics
Quality and Supply Management
3.70
article

An agility and performance assessment framework for supply chains using confirmatory factor analysis and structural equation modelling

Rohit Raj, Akhil NSB, Tanmoy De, Phanitha Kalyani Gangaraju, Vimal Kumar
article en
7 citations

Abstract

This study examines the impact of agile practices on supply chain performance measurements in manufacturing firms. Following COVID-19, there have been operational and logistics disruptions in manufacturing firms and supply chains worldwide. We study the link between supply chain performance and agile manufacturing practices by designing experimental research and collecting data from 340 responses from manufacturing firms. The experimental design proposed in this study uses a confirmatory factor and reliability analysis and smart-partial least square structural equation modeling. This research demonstrates the positive effect of agile supply chain strategies on manufacturing companies’ performance. The values obtained from the experiment support the dependability and effectiveness of the study. The research is supported by factors like customer involvement, facility management, supply chain responsiveness, strategic management, and supplier relationships but is undermined by technology utilization and supply chain contracts. The study will aid companies in combining agile with more conventional approaches to better adapt to market volatility and fierce global competition. Developing core competencies and acquiring a competitive advantage contribute to sustained advantage in the manufacturing industry. This study further outlines the need to understand how supply chains perform when agile practices are adopted. Figure: Theoretical framework of SCP

Supply Chain AnalyticsVol. 9
Chaoyang University of Technology (TW), Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (IN), National Taiwan University of Science and Technology (TW), Symbiosis International University (IN)
Openalex Percentile: Top 6%
Quality and Supply Management
3.70
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