AI and Predictive Analytics in Resource Allocation for Optimizing Operational Efficiency

  • Authors

    • Ms. E. Fathima Novseen Assistant Professor, Department of computer science, National College (Autonomous), Tiruchirappalli, Tamil Nadu, India. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-2018PI2K9M

    Published 01-04-2018

  • Artificial Intelligence (AI), Predictive Analytics, Resource Allocation, Operational Efficiency, Machine Learning, Optimization Algorithms, Demand Forecasting, Data-Driven Decision Making, Business Operations, Cost Reduction

    Issue

    Vol. 1 No. 1 (2018)

    Section

    Articles

    How to Cite

    [1]
    F. Novseen, “AI and Predictive Analytics in Resource Allocation for Optimizing Operational Efficiency”, IJADSMC, vol. 1, no. 1, pp. 01–14, Jan. 2018, doi: 10.67228/30713498/IJADSMC-2018PI2K9M.

  • Abstract

    In today's competitive business environment, organizations face the constant challenge of optimizing operational efficiency while managing limited resources. Traditional resource allocation methods often fall short in addressing the complexity and dynamic nature of modern operations. This paper explores the transformative role of Artificial Intelligence (AI) and Predictive Analytics in revolutionizing resource allocation processes. AI techniques, including machine learning and optimization algorithms, enable businesses to make data-driven decisions, while predictive analytics provides insights into future demand and resource needs. By integrating AI and predictive analytics, organizations can enhance decision-making accuracy, reduce costs, and improve overall operational efficiency. Through case studies and industry examples, this paper demonstrates the potential of these technologies to optimize resource allocation in various sectors, including manufacturing, healthcare, and logistics. The paper also discusses the challenges, ethical considerations, and future trends in leveraging AI and predictive analytics for operational optimization.

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