Journal of Ai ML DL
IoT, Artificial Intelligence, and Lean Systems for Sustainable Supply Chain Performance in SMEs: A Quantitative Industry 4.0 Study
Md Fazle Alahi Bhuiyan1*, Md Arifur Rahman2
Journal of Ai ML DL 1 (1) 1-8 https://doi.org/10.25163/ai.1110772
Submitted: 08 April 2025 Revised: 13 June 2025 Accepted: 18 June 2025 Published: 20 June 2025
Abstract
Background: Small and medium-sized enterprises (SMEs) are increasingly expected to build supply chains that are efficient, resilient, and environmentally responsible. Yet, many SMEs still face practical constraints in adopting Industry 4.0 technologies, particularly because digital transformation requires financial investment, technical capacity, and process-level readiness. This study examined how Internet of Things (IoT), artificial intelligence (AI), blockchain, and lean-oriented practices relate to supply chain performance, sustainability outcomes, and financial indicators in SMEs.
Methods: A quantitative cross-sectional design was used, drawing on firm-level data from 300 SMEs across supply-chain-dependent sectors. Technology adoption was assessed through normalized indicators for AI, IoT, and blockchain adoption. Sustainability-related outcomes included carbon footprint reduction, waste management efficiency, ethical sourcing, and circular economy practices. Descriptive statistics, multiple regression analysis, feature selection, and cross-validation were applied using SPSS and Python to evaluate associations between technology adoption, sustainability performance, and supply chain outcomes.
Results: The descriptive findings showed moderate adoption of IoT, AI, and blockchain among the sampled SMEs, with IoT adoption slightly higher than AI and blockchain. Sustainability indicators also showed meaningful variation, suggesting different levels of environmental and operational maturity across firms. Regression results indicated positive associations between AI, IoT, blockchain adoption, and supply chain performance, with AI and IoT showing stronger practical relevance than blockchain. Cross-validation suggested that a smaller, well-selected set of predictors may explain supply chain performance more effectively than larger feature sets.
Conclusion: The study suggests that SMEs may improve supply chain performance when digital technologies are aligned with lean process improvement and sustainability objectives. However, stronger methodological transparency and verified statistical outputs are needed to support more definitive causal conclusions.
Keywords: Internet of Things; Artificial Intelligence; Lean Systems; Sustainable Supply Chain; SMEs
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