MANUFACTURING MANAGEMENT SYSTEM: HUMAN RESOURCES 1 (EMPLOYEE RECORDS MANAGEMENT, ATTENDANCE AND TIME TRACKING MANAGEMENT, HR COMPLIANCE, ONBOARDING/OFFBOARDING) WITH PREDICTIVE ANALYTICS USING TENSORFLOWJS

Abstract

This capstone project explores an innovative approach to human resource management in the manufacturing sector. The system integrates advanced technology to create an environment where employees can perform optimally and feel valued. Using TensorFlow’s machine learning capabilities, it personalizes learning paths for workers such as John in assembly and Maria in quality control to enhance their skills based on individual strengths and goals. A system was developed using the MERN stack to build a unified platform with four main modules: Employee Records, Attendance and Time Tracking, HR Compliance, and Onboarding/Offboarding. Predictive analytics were incorporated through TensorFlow.js to forecast employee attrition, while facial recognition ensured secure attendance management. Testing showed that the system effectively streamlined HR operations, improved employee retention by identifying at-risk individuals, and simplified key HR processes. Managers reported better decision-making capabilities without relying on extensive spreadsheets. The findings demonstrate how advanced technology can reduce bureaucratic barriers in HR management. By personalizing learning paths and enabling data-driven decision-making, the system supports employee development and enhances overall workforce management in manufacturing settings.