Citation:

Abdul Jabbar Mohammad. "Adaptive Timekeeping Systems for Fluctuating Workload Environments" International Research Journal of Economics and Management Studies, Vol. 4, No. 5, pp. 285-293, 2025.

Abstract:

Conventional timekeeping & also scheduling solutions can prove insufficient in modern, dynamic & erratic work environments struggling to fit quickly shifting workloads. This work introduces a novel approach: AI and actual time data driven adaptive timekeeping systems. These technologies more dynamically change the workforce in line with expected demand using more predictive analytics, therefore transcending strict schedules. This study shows how adaptive scheduling may have a major influence on a retail store planning maximum foot traffic, a hospital reacting to patient surges, or a logistics hub managing changing shipments. We investigate how companies could improve employee satisfaction as well as output using data logs, ML models & also industry-specific knowledge. Empirical case studies in retail, logistics & healthcare show how these solutions all while following employment laws reduce overstaffing, prevent burnout & also enable more simplified operations. Our findings show not just gains in operational performance but also in work-life balance & also employee morale. The article asks important questions about transparency & also fairness in algorithmic scheduling and suggests more legislative safeguards giving human needs first priority. Looking forward, we saw great possibilities in combining these systems with actual time data sources such as sales or medical records and extending their usage to more flexible workforces including gig economy platforms.

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Keywords:

Adaptive Scheduling, AI Timekeeping, Workload Prediction, Workforce Analytics, Labor Law Compliance, Employee Well-Being, Dynamic Workforce Management, Predictive Shift Allocation.