Modern Evaluation Techniques Using AI in Industrial Workforce Management: Evidence from Erode Mills
- Version
- Download 2
- File Size 653.19 KB
- File Count 1
- Create Date 23 December 2025
- Last Updated 23 December 2025
“Modern Evaluation Techniques Using AI in Industrial Workforce Management: Evidence from Erode Mills”
Corresponding author: sakthipriyamba89@gmail.com
Author 1: Mrs N. Sakthi Priya, Research Scholar, Department of Business Administration,
Government and Science College, Kangeyam (T.K), Tirupur (Dist..), 638108.
Author 2: Dr.P.Komarasamy Head of the Department & Associate professor,Department of
Business Administration, Government and Science College,Kangeyam (T.K), Tirupur (Dist..),638108
Author 3: Miss S.Stella angel(A) Lakshmi shruthi, Assistant Professor, Department of MBA , Surya Engineering College,Mettukada Erode-638107.
Author 4: Mrs.N.SakthiPriya, Assistant Professor, Department of MBA , Surya Engineering
College, Mettukadai Erode-638107.
Abstract
The article focuses on detailing“Modern Evaluation Techniques Using AI in Industrial Workforce Management: Evidence from Erode Mills”This study explains the role of Artificial Intelligence (AI) in transforming performance appraisal systems within the textile mills of the Erode zone. In the labour-intensive industry like textiles, a fair and efficient performance evaluation is critical for sustaining productivity and retaining skilled employees. Traditional appraisal methods are often criticized for being subjective, inconsistent, and lacking real-time feedback. AI-powered tools, such as predictive analytics, AI-driven dashboards, natural language processing for performance reviews, and continuous feedback, as well as AI-enabled 360-degree appraisal systems, offer a data-driven and transparent alternative. This research examines how these AI tools are being adopted in Erode mills and their impact on employee productivity, job satisfaction, and organization efficiency. This study also highlights employee perceptions and experiences with AI-based appraisal systems while comparing them to traditional methods. By employing a mixed methods approach including surveys, interviews, and case studies across selected mills, the research aims to provide actionable insights for HR managers, policymakers, and technology providers to enhance fairness, accuracy, and efficiency in performance appraisal practices.
Keywords: Artificial Intelligence, Performance appraisal, Employee productivity, AI-driven Dashboards,
Download