The Impact of Digital Supply Chain Technologies on Supply Chain Performance in Bangladesh’s Ready-Made Garment Industry

Abstract

This quantitative, cross-sectional study examines whether four digital supply chain technologies—enterprise resource planning (ERP), data analytics, Internet of Things (IoT) and real-time monitoring, and artificial intelligence (AI) and automation—jointly predict perceived operational, delivery, inventory, and cost performance in Bangladesh’s ready-made garment (RMG) industry. Guided by the Technology–Organization–Environment framework and the Resource-Based View, it also tests whether perceived implementation challenges are negatively associated with overall digital adoption. The final dataset contains 87 complete respondent records, five demographic variables, 40 five-point Likert items grouped into ten four-item constructs, and 71 non-empty optional comments. The analysis includes data screening, respondent frequencies, Cronbach’s alpha, descriptive statistics, Pearson and Spearman correlations, four ordinary least squares regression models, HC3 robust checks, and regression diagnostics at α = .05. All ten scales showed acceptable to good internal consistency (α = .763–.854). Challenges had the highest mean (M = 3.66), followed by Overall Opinion (M = 3.61); among the technology constructs, ERP Adoption was highest (M = 3.46) and AI and Automation lowest (M = 3.01). All four performance models were significant and explained 38.4% to 63.7% of outcome variance (all model p < .001), supporting H1 through H4. Challenges was negatively associated with Digital Adoption (r = −.384, p < .001), supporting H5. The findings show meaningful associations within this sample, not causal effects. Individual technology contributions differed across the four outcomes, and the cost model required HC3 robust inference. Practical priorities include cross-department data integration, role-based training, cybersecurity, affordable systems, dependable connectivity, and phased investment tied to measurable operational outcomes.