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Digital Transformation in Bangladesh’s RMG Supply Chains: A Case Study on DBS Group

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  • Rifat Hosain

    (North South University)

Abstract

This study explores how DBS Group, a mid-sized Bangladeshi ready-made garments (RMG) exporter, implements digital transformation technologies—Internet of Things (IoT), blockchain, and Enterprise Resource Planning (ERP)—to enhance supply chain efficiency. Using an exploratory single-case study, secondary data from BGMEA (2024), McKinsey (2021), and Textile Network (2023) were synthesized, with inferred metrics (e.g., 10–15% lead time reduction) benchmarked against RMG standards. Findings show IoT reduces lead times by 10–15% (10 days), blockchain improves traceability by 20%, and ERP cuts order errors by 15%. Challenges include high costs ($150,000 for IoT), 30% staff skill gaps, and infrastructure issues (30% power outages), worsened by 2024 political instability. The SCOR model evaluates performance, while the Technology-Organization-Environment (TOE) framework critiques adoption barriers, questioning the “Digital Bangladesh Vision†narrative. Recommendations include a phased IoT rollout and BGMEA-subsidized training. Limitations stem from secondary data reliance and single-case scope. This study pioneers firm-level digital SCM insights in a developing economy, offering global implications for technology adoption under constraints. Future research should validate findings with primary data across RMG firms. (200 words)

Suggested Citation

  • Rifat Hosain, 2025. "Digital Transformation in Bangladesh’s RMG Supply Chains: A Case Study on DBS Group," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(6), pages 1696-1703, June.
  • Handle: RePEc:bcp:journl:v:9:y:2025:issue-6:p:1696-1703
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    References listed on IDEAS

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    4. Gunasekaran, Angappa & Papadopoulos, Thanos & Dubey, Rameshwar & Wamba, Samuel Fosso & Childe, Stephen J. & Hazen, Benjamin & Akter, Shahriar, 2017. "Big data and predictive analytics for supply chain and organizational performance," Journal of Business Research, Elsevier, vol. 70(C), pages 308-317.
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