Bridging Theory and Practice: A Comprehensive Framework for Digital Supply Chain Orchestration Through Big Data Analytics

Background: Digital supply chain transformation research exhibits a critical gap, examining technologies in isolation rather than as integrated ecosystems. Methods: This study addresses this limitation by developing a comprehensive orchestration frame-work through PRISMA-guided systematic review of 96 publications (2012–2024) using bibliometric analysis, structural topic modeling, and thematic synthesis across Scopus and Web of Science databases. Results: Analysis revealed three distinct research clusters: Supply Chain Management (centrality: 14.95), Digital Transformation (centrality: 9.50, density: 101.05), and Big Data Analytics (density: 113.22), with substantial negative correlations (−0.48 to −0.54) indicating organizational evolution from fragmented adoption toward integration. Conclusions: Publications increased 78% year-over-year during 2021–2022, while Supply Chain Management dominated topic prevalence (41%) and Big Data Analytics declined from 0.9 to 0.15 as practices normalized. The Digital Supply Chain Orchestration Framework conceptualizes transformation as multi-layered with hierarchical relationships between foundational domains, technological enablers, integration mechanisms, and value creation dimensions. This framework provides structured approaches for organizations to assess digital maturity, identify technological gaps, and develop strategic roadmaps aligned with Sustainable Development Goals, bridging theory and practice for integrated, value-driven digital transformation.

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Publication Details

Journal
Logistics
Published
2025-11-25
DOI
https://doi.org/10.3390/logistics9040168
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
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article

Bridging Theory and Practice: A Comprehensive Framework for Digital Supply Chain Orchestration Through Big Data Analytics

Muhammad Imran Qureshi, Nohman Khan, Samrena Jabeen, Mohammad Falahat et al.
Logistics
Big Data and Business Intelligence
article

Bridging Theory and Practice: A Comprehensive Framework for Digital Supply Chain Orchestration Through Big Data Analytics

Muhammad Imran Qureshi, Nohman Khan, Samrena Jabeen, Mohammad Falahat, Sabeen Hussain Bhatti, Mudassar Khan
article en

Abstract

Background: Digital supply chain transformation research exhibits a critical gap, examining technologies in isolation rather than as integrated ecosystems. Methods: This study addresses this limitation by developing a comprehensive orchestration frame-work through PRISMA-guided systematic review of 96 publications (2012–2024) using bibliometric analysis, structural topic modeling, and thematic synthesis across Scopus and Web of Science databases. Results: Analysis revealed three distinct research clusters: Supply Chain Management (centrality: 14.95), Digital Transformation (centrality: 9.50, density: 101.05), and Big Data Analytics (density: 113.22), with substantial negative correlations (−0.48 to −0.54) indicating organizational evolution from fragmented adoption toward integration. Conclusions: Publications increased 78% year-over-year during 2021–2022, while Supply Chain Management dominated topic prevalence (41%) and Big Data Analytics declined from 0.9 to 0.15 as practices normalized. The Digital Supply Chain Orchestration Framework conceptualizes transformation as multi-layered with hierarchical relationships between foundational domains, technological enablers, integration mechanisms, and value creation dimensions. This framework provides structured approaches for organizations to assess digital maturity, identify technological gaps, and develop strategic roadmaps aligned with Sustainable Development Goals, bridging theory and practice for integrated, value-driven digital transformation.

LogisticsVol. 9(4)
Asia Pacific University of Technology & Innovation (MY), Abbottabad University of Science and Technology (PK), Arab Open University (BH), Teesside University (GB)
Openalex Percentile: Top 45%
Big Data and Business Intelligence
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