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<p>This study examines big data analytics capability (BDAC) and circular supply chain management (CSCM) indicators within manufacturing SMEs. We employed a combination of CODAS, ISM, and MICMAC methodologies to create a comprehensive model, presenting an integrated framework based on BDAC-CSCM indicators. The BDAC indicators were classified into infrastructure, management, and personnel capabilities, and the CSCM indicators were segmented into economic, environmental, and social categories, using SMART (specific, measurable, attainable, relevant, and time-bound) criteria and expert assessments for robust evaluation. The research findings highlight eco-innovation, transportation costs, and personnel data analytics capability as the most critical indicators for enhancing CSCM in manufacturing SMEs by looking at the assessment score (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41660_2025_482_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="22" /> </InlineMediaObject> <EquationSource Format="TEX">\({H}_{i}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>H</mi> <mi>i</mi> </msub> </math></EquationSource> </InlineEquation>) values, respectively, 0.776, 0.748, and 0.553. Additionally, structural self-interaction matrix (SSIM) and reachability matrix analysis revealed complex interrelationships among these indicators, providing valuable insights for improving circular supply chain management practices in the manufacturing sector. These indicators provide a robust framework for assessing BDAC-CSCM in manufacturing SMEs, contributing to improved decision-making in the context of sustainable supply chain management.</p>

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Enhancing Circular Supply Chain Management (CSCM) in Manufacturing SMEs: An Integrated CODAS-ISM-MICMAC Approach to Big Data Analytics Capability

  • 期刊论文

  • 发表日期：

    2025年1月18日

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  • Rangga Primadasa,
  • Noor Nailie Azzat,
  • Elisa Kusrini,
  • Agus Mansur,
  • Ilyas Masudin

摘要

This study examines big data analytics capability (BDAC) and circular supply chain management (CSCM) indicators within manufacturing SMEs. We employed a combination of CODAS, ISM, and MICMAC methodologies to create a comprehensive model, presenting an integrated framework based on BDAC-CSCM indicators. The BDAC indicators were classified into infrastructure, management, and personnel capabilities, and the CSCM indicators were segmented into economic, environmental, and social categories, using SMART (specific, measurable, attainable, relevant, and time-bound) criteria and expert assessments for robust evaluation. The research findings highlight eco-innovation, transportation costs, and personnel data analytics capability as the most critical indicators for enhancing CSCM in manufacturing SMEs by looking at the assessment score ( \({H}_{i}\) H i ) values, respectively, 0.776, 0.748, and 0.553. Additionally, structural self-interaction matrix (SSIM) and reachability matrix analysis revealed complex interrelationships among these indicators, providing valuable insights for improving circular supply chain management practices in the manufacturing sector. These indicators provide a robust framework for assessing BDAC-CSCM in manufacturing SMEs, contributing to improved decision-making in the context of sustainable supply chain management.