Optimizing Workforce Efficiency: Leveraging Integrated Business Analytics and Machine Learning for Enhanced Performance Prediction

EasyChair Preprint 12702

Abstract

In today's competitive business landscape, optimizing workforce efficiency is crucial for organizational success. This paper proposes a strategic framework for enhancing employee productivity through the integration of business analytics and machine learning techniques. By leveraging advanced analytical tools, organizations can gain insights into employee behavior, performance patterns, and potential areas for improvement. This integrated approach enables proactive decision-making, resource allocation, and targeted interventions to maximize workforce productivity. Through a comprehensive analysis of historical data and real-time inputs, organizations can accurately predict future performance trends, identify high-performing employees, and mitigate potential risks. This paper outlines the key components of the proposed framework, including data collection, preprocessing, modeling, evaluation, and deployment. Additionally, it discusses the potential benefits, challenges, and ethical considerations associated with implementing predictive performance analytics in the workplace. By adopting this strategic approach, organizations can unlock the full potential of their workforce and achieve sustainable competitive advantage in today's dynamic business environment.

Keyphrases: Business Analytics, Employee Productivity, Predictive Analytics, Workforce efficiency, machine learning, performance prediction

@booklet{EasyChair:12702,
  author    = {Jonny Bairstow},
  title     = {Optimizing Workforce Efficiency: Leveraging Integrated Business Analytics and Machine Learning for Enhanced Performance Prediction},
  howpublished = {EasyChair Preprint 12702},
  year      = {EasyChair, 2024}}