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Author: search.filters.author.Andrés Leiva-Araos

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    Evaluating Smart Building Features for Fire, Electrical, and Life Safety: A Rapid Human-LLM Framework for Literature Review and Research Mapping
    (Institute of Electrical and Electronics Engineers (IEEE), 2025)
    Andrés Leiva-Araos
    ;
    Vamsi Sai Kalasapudi
    ;
    Aiyin Jiang
    ;
    Hemani Kaushal
      2
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    Predicting Particulate Matter Values in Metropolitan Areas Using Machine Learning
    (IEEE, 2024-12-18)
    Andrés Leiva-Araos
    ;
    Tushya Vemuri
    ;
    David Mena
    ;
    Xudong Liu
      3
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    Large scale summarization using ensemble prompts and in context learning approaches
    (Springer Science and Business Media LLC, 2025-03-25)
    Andrés Leiva-Araos
    ;
    Bady Gana
    ;
    Héctor Allende-Cid
    ;
    José García
    ;
    Manob Jyoti Saikia
      5
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    Predictive Optimization of Patient No-Show Management in Primary Healthcare Using Machine Learning
    (Springer Science and Business Media LLC, 2025-01-14)
    Andrés Leiva-Araos
    ;
    Cristián Contreras
    ;
    Hemani Kaushal
    ;
    Zornitza Prodanoff
    Scopus© Citations 7  4
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    A Robust Statistical Methodology for Measuring Enterprise Agility
    (2023)
    Roberto Moraga-Díaz
    ;
    Andrés Leiva-Araos
    ;
    José García
    <jats:p>In an era characterized by rapid technological advancements, economic fluctuations, and global competition, adaptability and resilience have become critical success factors for businesses navigating uncertainty and complexity. This article explores the role of enterprise agility in today’s business landscape at Latam branch of Tata Consultancy Services, where organizations face complex and diverse operations. We aim to examine how companies can become more agile in the face of emerging challenges and seize opportunities swiftly to drive growth and deliver value. Since 2014, the division has embarked on an agile transformation journey to drive growth, deliver value, foster innovation, and build resilience in an increasingly dynamic environment. We scrutinize an approach to measuring and enhancing enterprise agility, employing statistical analysis and continuous improvement methodologies to tackle real-world challenges while offering valuable insights and recommendations for organizations aiming to implement similar systems. The results of an agile transformation in a certain company’s Latam branch serve as a compelling case study, demonstrating how the implementation of targeted measures and continuous improvement can significantly bolster enterprise agility. Methodologically, our work applies a novel sequence of parametric statistical tests which, to the best of our knowledge, have not been used in the industry to validate the results of business agility metrics. In future work, we aim to create a new workflow considering non-parametric tests to address data with other statistical distributions. We conclude our work by proposing a sequence of steps for organizations to implement business agility metrics.</jats:p>
      19Scopus© Citations 5