This article presents the design and development of an automated employee work monitoring and payroll management system for garment manufacturing enterprises. The proposed system aims to improve workforce management efficiency, automate attendance tracking, monitor employee productivity, and ensure accurate salary calculations. The system is based on a web-based architecture integrated with cloud technologies, real-time data processing mechanisms, and intelligent analytics modules. It enables automatic recording of employee attendance, work hours, production performance, and task completion status. The developed platform incorporates biometric authentication, QR-code attendance tracking, performance monitoring dashboards, and automated payroll calculation algorithms. Machine learning techniques are utilized to analyze employee productivity trends, identify performance patterns, and support management decision-making. The system also provides comprehensive reporting tools for managers and human resource departments. Experimental implementation in a garment manufacturing enterprise involving 150 employees demonstrated that the system reduced payroll processing time by 68%, improved attendance monitoring accuracy by 94%, increased workforce productivity by 27%, and reduced administrative workload by 52%. Furthermore, the platform improved transparency, minimized human errors in salary calculations, and enhanced operational efficiency. The article is intended for software developers, industrial engineers, human resource specialists, and researchers working in enterprise automation and workforce management systems.
Djurayeva et al. (Sat,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: