Simulating Employment Displacement under Partial, Advanced and Near-Full AI and Automation Deployment in Bangladesh's Ready-Made Garment Factories: A Process-Level Model

Abstract

This paper presents a process-level simulation of employment impacts from artificial intelligence and automation in Bangladesh's ready-made garment (RMG) sector. Using a representative 1,500-worker factory calibrated to recent empirical evidence, three scenarios are modelled: partial automation reflecting current observed trends (approximately 34 per cent reduction), advanced automation combined with AI applications (approximately 58 per cent reduction), and a near-full theoretical maximum (approximately 75 per cent reduction). Results indicate that helpers and lower-skilled operators bear the largest absolute and relative losses, with women accounting for roughly two-thirds of displaced workers across scenarios. Scaling the advanced scenario to sector level produces job losses on the order of 2.3 million, consistent in magnitude with independent forward projections. Technical constraints on fully autonomous sewing of complex garments, combined with potential output growth and new technical roles, moderate absolute employment decline. The findings underscore the urgency of just-transition policies, targeted reskilling, and product upgrading for Bangladesh's largest export industry. KSCCN article, 10 October 2026. Dr. Syed Muntasir Mamun, ORCID 0000-0001-6845-2853. The views expressed are those of the author.