<![CDATA[Traditional textile SMEs still rely on manual processes, resulting in inefficiencies in production and data management. This study proposes a cost-conscious digitalization framework that integrates Cyber-Physical Systems (CPS), a lightweight information layer, and artificial intelligence (AI), specifically designed for labour-intensive textile operations. The framework adheres to the ISA-95 architecture, emphasizing affordability and scalability. Stakeholder interviews, business process reengineering, and a three-month field implementation were conducted in a textile hub in Bandung. Key digital tools, including e-kiosks for real-time logging, integrated digital scales for inventory management, and mobile vision-based quality control using convolutional neural networks (Xception and VGG), were evaluated through an immersion study and user acceptance testing. Evaluation results show improvements in workflow visibility, data reliability, and consistency of quality inspection compared to the pre-digitalized process, while maintaining ease of use for operators. Evaluation results indicate qualitative operational improvements—such as enhanced workflow visibility, more reliable data capture, and more consistent quality inspection—reflecting meaningful enhancements observed during the digitalization pilot. The study contributes a replicable CPS–AI model that enables traditional SMEs to enhance efficiency and quality through scalable digital transformation.]]>
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