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This study aims to develop a comprehensive framework for supply chain analytics and its outcomes. To this purpose, it has established the relationship among supply chain analytics capabilities (CSAC), supply chain visibility (SCV), supply chain collaboration (SCC), supply chain innovation (SCI), and stakeholder trust (ST) to achieve environmental performance (EP) and financial performance (FP). In this quantitative study, the deductive approach has been followed by the survey method to collect the responses from respondents. Data has been collected from the various business sectors with different firm sizes, operations, and countries. Moreover, the partial least square-structure equation modeling (PLS-SEM) method has been used to analyze the data. The findings of the study have supported the conceptual framework. The direct effects of SCAC on SCV, SCC, and ST were found significantly positive. Moreover, SCV on SCC and ST showed the same pattern. Additionally, indirect effects were found between SCAC, SCV, and SCC, and SCAC, SCV, and ST were found. The previous quantitative supply chain literature has focused the big data analytics and capabilities and big data predictive analytics, but they have ignored supply chain analytics.

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Leveraging the Synergy of Supply Chain Analytics, Visibility, Innovation, and Collaboration to Improve Environmental and Financial Performance: An Empirical Investigation

  • 会议论文

  • 发表日期：

    2025年

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  • Muhammad Noman Shafique,
  • Elisabeth T. Pereira

摘要

This study aims to develop a comprehensive framework for supply chain analytics and its outcomes. To this purpose, it has established the relationship among supply chain analytics capabilities (CSAC), supply chain visibility (SCV), supply chain collaboration (SCC), supply chain innovation (SCI), and stakeholder trust (ST) to achieve environmental performance (EP) and financial performance (FP). In this quantitative study, the deductive approach has been followed by the survey method to collect the responses from respondents. Data has been collected from the various business sectors with different firm sizes, operations, and countries. Moreover, the partial least square-structure equation modeling (PLS-SEM) method has been used to analyze the data. The findings of the study have supported the conceptual framework. The direct effects of SCAC on SCV, SCC, and ST were found significantly positive. Moreover, SCV on SCC and ST showed the same pattern. Additionally, indirect effects were found between SCAC, SCV, and SCC, and SCAC, SCV, and ST were found. The previous quantitative supply chain literature has focused the big data analytics and capabilities and big data predictive analytics, but they have ignored supply chain analytics.