SYSTEMATIC LITERATURE REVIEW ON DIGITAL TWIN-ENABLED PROCESS OPTIMISATION IN LARGE-SCALE APPAREL MANUFACTURING: FRAMEWORKS, FACTORS, AND APPLICATIONS IN DEVELOPING ECONOMIES

Abstract

The transformation of manufacturing towards digital twin (DT) enabled systems has reshaped production process optimisation, especially within large-scale apparel industries. This study conducts a systematic literature review (SLR) to critically evaluate frameworks, influencing factors, and applications of DT-driven process optimisation with a focus on developing-economy contexts. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, 30 peer-reviewed articles published between 2018 and 2025 were examined from the database, Google Scholar. Each paper was assessed through quality screening and thematic categorisation. The analysis identified four major readiness domains: technological, organisational, human capital, and external/environmental factors that influence DT implementation for process optimisation. Findings reveal that DT enhances manufacturing efficiency through real-time monitoring, predictive simulation, and data-driven optimisation, yet adoption barriers persist in developing contexts due to infrastructure limitations, financial constraints, and workforce capability gaps. The review also synthesises recent frameworks integrating DT with Lean and Artificial Intelligence (AI) based optimisation models, presenting their relevance to operational excellence. This paper contributes to bridging the research gap by providing a consolidated perspective on DT readiness and optimisation frameworks applicable to Sri Lanka's apparel sector and similar economies. It further suggests developing context-specific maturity models and hybrid frameworks that integrate DT into continuous improvement and sustainability strategies.