This study develops and validates an autonomous maintenance (AM) implementation and sustainability framework for sewing operations in the garment manufacturing industry, where labor-intensive production and limited operator maintenance time constrain conventional AM implementation.
A qualitative framework development approach was adopted. An initial framework was developed through a structured literature review and refined through semi-structured interviews with twelve garment manufacturing experts from Sri Lanka, Bangladesh, Indonesia, and Kenya. Interview data were analyzed using thematic analysis.
Six refinement themes emerged from the study: impactful abnormality identification, operator time constraints, comprehensive inspection, training and awareness, performance monitoring, and management governance. These informed a six-stage framework integrating abnormality management, layered inspections, digital performance monitoring, and governance mechanisms to support sustainable AM implementation.
The framework was developed based on qualitative insights from experts across South Asia, Southeast Asia, and East Africa. Future research should focus on empirically validating the framework across diverse manufacturing contexts.
The framework provides manufacturers with a structured approach for implementing and sustaining AM while maintaining production efficiency.
Improved operator involvement enhances equipment awareness, safer machine operation, and workforce capability.
The study contributes an expert-validated AM framework that adapts established AM principles to the operational realities of sewing operations in the garment manufacturing industry. Its originality lies in explicitly addressing operator time constraints and integrating AM implementation with routine production practices and sustainability mechanisms.
