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.
Authors
- Muhammad Imran Qureshi
- Nohman Khan (ORCID: https://orcid.org/0000-0001-9714-6273)
- Samrena Jabeen (ORCID: https://orcid.org/0000-0001-8335-0476)
- Mohammad Falahat (ORCID: https://orcid.org/0000-0002-0423-984X)
- Sabeen Hussain Bhatti (ORCID: https://orcid.org/0000-0002-2599-9856)
- Mudassar Khan
Institutions
- Asia Pacific University of Technology & Innovation (MY)
- Abbottabad University of Science and Technology (PK)
- Arab Open University (BH)
- Teesside University (GB)
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
- 0.00