A Conceptual and Analytical Framework for Workforce Productivity Optimization in Diamond Manufacturing SMEs using Biometric-Integrated ERP Systems

Abstract

For small to medium-sized enterprises within the diamond manufacturing industry, skilled labor is a key component necessary for operational profitability through maximizing workforce productivity. In most cases electronic fingerprint attendance recording devices are used for clocking in and out, and because payroll calculations and worker performance assessments are largely done manually, issues arise with payroll discrepancies, financial losses, delays in salary payments, and a lack of visibility into employee productivity. The following paper presents an analytical and conceptual framework for optimizing workforce productivity through a biometric-integrated ERP model specifically designed for small diamond manufacturing enterprises. By combining biometric attendance logs with structured worker intelligence, the framework provides productivity indices to measure productivity of workers, equations for automating payroll calculations, metrics to evaluate worker attendance consistency, and analytics to review the degree of employee dependency. The development of an analytical model to forecast payroll leakage reductions of 10% to 12%, improvement of productivity consistency by 15% to 20%, and overall reductions in length of time needed to complete payroll is demonstrated using simulated multi-branch operational data collected over 90 days from 50 workers. Thus, the results of this study provide the basis for a data-driven approach towards the digital transformation of the workforce for labor-intensive industries.