International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Revolutionizing Workforce Planning and Pay Strategies Through Advanced Payroll Predictive Analytics

Author(s) John Selvaraj Arulappan
Country United States
Abstract Predictive analytics is transforming payroll systems, enabling organizations to make data-driven decisions that enhance workforce planning and optimize compensation strategies. By leveraging advanced analytics, companies can forecast labor costs, identify trends in employee performance, and address pay inequities, fostering a more equitable and efficient workplace. This paper explores the integration of predictive analytics in payroll systems, highlighting its role in automating compliance, minimizing errors, and providing actionable insights for strategic decision-making. Case studies from industry leaders demonstrate the measurable impact of these technologies, including improved workforce productivity, cost reduction, and enhanced employee satisfaction. The discussion also examines challenges such as data security, integration with legacy systems, and ethical considerations in predictive modeling. Ultimately, the paper underscores how advanced payroll predictive analytics can revolutionize workforce management, aligning organizational goals with employee well-being and financial sustainability
Keywords Predictive Analytics, Payroll Systems, Workforce Planning, Compensation Strategies, Data-Driven Decision-Making, Labor Cost Forecasting, Pay Equity, Employee Performance Trends, Compliance Automation, Workforce Productivity, Cost Optimization, Advanced Analytics, HR Management, Ethical AI in Payroll Systems
Field Computer > Data / Information
Published In Volume 7, Issue 1, January-February 2025
Published On 2025-02-21
Cite This Revolutionizing Workforce Planning and Pay Strategies Through Advanced Payroll Predictive Analytics - John Selvaraj Arulappan - IJFMR Volume 7, Issue 1, January-February 2025. DOI 10.36948/ijfmr.2025.v07i01.37392
DOI https://doi.org/10.36948/ijfmr.2025.v07i01.37392
Short DOI https://doi.org/g85szk
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