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Article

A Robust Statistical Methodology for Measuring Enterprise Agility

by
Roberto Moraga-Díaz
1,
Andrés Leiva-Araos
2,* and
José García
3,*
1
Tata Consultancy Services Limited—TCS Latam, Santiago 8330088, Chile
2
Facultad de Ingeniería, Centro de Transformación Digital, Universidad del Desarrollo, Santiago 7610658, Chile
3
Escuela de Ingeniería de Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362804, Chile
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2023, 13(14), 8445; https://doi.org/10.3390/app13148445
Submission received: 26 May 2023 / Revised: 18 July 2023 / Accepted: 19 July 2023 / Published: 21 July 2023
(This article belongs to the Special Issue Smart Industrial System)

Abstract

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.
Keywords: business agility; enterprise agility; organizational transformation; data analytics; statistics; metrics business agility; enterprise agility; organizational transformation; data analytics; statistics; metrics

Share and Cite

MDPI and ACS Style

Moraga-Díaz, R.; Leiva-Araos, A.; García, J. A Robust Statistical Methodology for Measuring Enterprise Agility. Appl. Sci. 2023, 13, 8445. https://doi.org/10.3390/app13148445

AMA Style

Moraga-Díaz R, Leiva-Araos A, García J. A Robust Statistical Methodology for Measuring Enterprise Agility. Applied Sciences. 2023; 13(14):8445. https://doi.org/10.3390/app13148445

Chicago/Turabian Style

Moraga-Díaz, Roberto, Andrés Leiva-Araos, and José García. 2023. "A Robust Statistical Methodology for Measuring Enterprise Agility" Applied Sciences 13, no. 14: 8445. https://doi.org/10.3390/app13148445

APA Style

Moraga-Díaz, R., Leiva-Araos, A., & García, J. (2023). A Robust Statistical Methodology for Measuring Enterprise Agility. Applied Sciences, 13(14), 8445. https://doi.org/10.3390/app13148445

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