Volume 1 Number 1 2025
Optimizing Resource Allocation and Operational Efficiency in Management Information Systems Using Predictive Machine Learning
Proggo Choudhury1*, Md Sharfuddin2
Journal of Ai ML DL 1 (1) 1-8 https://doi.org/10.25163/ai.1110443
Submitted: 29 July 2025 Revised: 18 October 2025 Accepted: 21 October 2025 Published: 23 October 2025
Abstract
Background: Management Information Systems (MIS) play a crucial role in ensuring data-driven decision-making and operational control across organizations. Organizations face operational delays and budget overruns because they distribute their resources poorly which results in decreased operational efficiency. Organizations can enhance their operational efficiency through predictive analytics integration which enables them to foresee resource requirements and minimize operational waste.
Methods: A hybrid predictive framework was developed combining time-series forecasting (ARIMA) and multiple regression models, applied to 24 months of enterprise data. The system operated based on three essential variables which included system load and processing time and human resource allocation. The study included a questionnaire survey which collected opinions from 120 MIS professionals to evaluate predictive tools benefits and user adaptability. The researchers employed Likert-scale evaluation together with Pearson correlation analysis to match user input with model predictions.
Results: The predictive analytics system achieved a 27.4% boost in resource utilization while it decreased operational delays by 21.3% and reduced overall expenses by 18.7%. The validation metrics demonstrated excellent model reliability through statistical results which included R² = 0.92 and RMSE = 0.072 with p = 0.01. The questionnaire results showed that 87% of participants believed workflow efficiency had improved and 82% expressed satisfaction with decision transparency.
Conclusion: The results obtained from empirical data combined with user feedback allow predictive analytics to convert MIS systems into proactive optimization tools which enhance organizational performance and decision accuracy.
Keywords: Predictive analytics, Management Information Systems, Resource allocation, Operational efficiency, Decision-making
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