Artificial Intelligence and Machine Learning: Revolutionizing Supply Chain Decision-Making and Operations
Afshar Uddin Jubayer¹
¹Department of Industrial and Production Engineering, American International University-Bangladesh (AIUB), Dhaka, Bangladesh. Email: 24-58405-2@student.aiub.edu
Md. Ehasanul Haque2
2Department of Industrial and Production Engineering, American International University-Bangladesh (AIUB), Dhaka, Bangladesh. Email: ehasanul@aiub.edu
Abstract
We review how artificial intelligence (AI) and machine learning (ML) reshape supply-chain decision-making with direct implications for Bangladesh’s ready-made-garment (RMG) sector. Based on a secondary synthesis of 24 peer-reviewed studies spanning manufacturing, logistics, pharmaceuticals, procurement, and sustainability, we map AI/ML use cases in demand forecasting, procurement analytics, inventory control, routing and scheduling, production planning, digital twins, and green SCM. Reported outcomes include higher forecast fidelity, shorter decision latency, and improved disruption recovery; several studies attribute gains to predictive analytics, reinforcement learning policies, and simulation-backed digital twins. Using an MIT-style systems perspective, we identify adoption frictions salient to Bangladeshi fragmented data, infrastructure gaps, organizational resistance, and manual processes with governance concerns around explainability and data stewardship. We propose a sector-specific adoption path for RMG suppliers that prioritizes (i) data architecture and interoperability, (ii) workforce upskilling for analytics and operations, and (iii) regulatory alignment on data access and accountability. The contribution is a contextualised framework that translates global AI/ML evidence to an emerging-economy garment supply chain, clarifying where near-term value is most likely (forecasting, inventory, and production sequencing) and what prerequisites must be met for resilient and sustainable scale-up.
Keywords: Artificial Intelligence (AI), Machine Learning (ML), Supply Chain Management (SCM), Predictive Analytics, Resilience, Sustainability, Bangladesh RMG Sector.