AI-Powered Workforce Forecasting in SAP Time Management: Predictive Models for Staffing Needs, Absenteeism, and Peak Demand Planning

This research explores the application of Artificial Intelligence (AI) in SAP Time Management to enhance the accuracy and responsiveness of workforce demand forecasting. It solves forecasting staff requirements, absenteeism trends, and demand spikes using predictive models of historical attendance trends, company calendars, and external data. With machine learning algorithms implemented through SAP Analytics Cloud and SAP Business Technology Platform, the research demonstrates how companies can transition from reactive to proactive workforce planning. The research process involves data ingestion from SAP Time and Attendance Management, preprocessing using SAP Data Intelligence, and model training using regression and classification techniques. Results demonstrate a dramatic increase in forecasting precision and employee resource utilization. The research concludes that AI-powered time management systems empower HR executives to dynamically match workforce availability to business requirements and minimize absenteeism-linked risks.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-30
DOI
https://doi.org/10.5281/zenodo.15387283
Primary Topic
Advanced Neuroimaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-Powered Workforce Forecasting in SAP Time Management: Predictive Models for Staffing Needs, Absenteeism, and Peak Demand Planning

Zenodo (CERN European Organization for Nuclear Research)
Advanced Neuroimaging Techniques and Applications
article

AI-Powered Workforce Forecasting in SAP Time Management: Predictive Models for Staffing Needs, Absenteeism, and Peak Demand Planning

article en

Abstract

This research explores the application of Artificial Intelligence (AI) in SAP Time Management to enhance the accuracy and responsiveness of workforce demand forecasting. It solves forecasting staff requirements, absenteeism trends, and demand spikes using predictive models of historical attendance trends, company calendars, and external data. With machine learning algorithms implemented through SAP Analytics Cloud and SAP Business Technology Platform, the research demonstrates how companies can transition from reactive to proactive workforce planning. The research process involves data ingestion from SAP Time and Attendance Management, preprocessing using SAP Data Intelligence, and model training using regression and classification techniques. Results demonstrate a dramatic increase in forecasting precision and employee resource utilization. The research concludes that AI-powered time management systems empower HR executives to dynamically match workforce availability to business requirements and minimize absenteeism-linked risks.

Zenodo (CERN European Organization for Nuclear Research)
Decent work and economic growth
Openalex Percentile: Top 41%
Advanced Neuroimaging Techniques and Applications
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