# A Robust Statistical Methodology for Measuring Enterprise Agility
## 1 Introduction  
In an era marked by rapid technological advancements, economic fluctuations, and global competition, businesses are constantly grappling with uncertainty and complexity [1, 2, 3]. These challenges demand not only operational excellence but also strategic adaptability to remain competitive and thrive in the marketplace [4, 5]. Organizations must now evolve at an accelerated pace to meet the ever-changing needs of their customers and stakeholders, while also addressing emerging threats and opportunities [6, 7]. This growing demand for adaptability and resilience has led to the emergence of enterprise agility [8], a critical factor for success in today's volatile business landscape [9]. In today's fast-paced business landscape, the criticality of swiftly adapting and seizing emerging opportunities for the success of any organization is recognized. The importance of enterprise agility in driving growth and delivering value for clients and ecosystems is acknowledged by Tata Consultancy Services (TCS) [10, 11, 12]. Since 2014, agile transformation has been actively pursued by the organization on a global scale, both internallyand externally, in order to foster innovation and resilience in an increasingly dynamic environment [13; 14; 15]. Agile organizations demonstrate the capacity to navigate change effectively, capitalizing on new possibilities while minimizing the adverse effects of disruptions [16; 17]. This approach necessitates a proactive and adaptable mindset, as well as [10; 11; 18] an openness to experimentation, learning from setbacks [19; 20], and innovation and entrepreneur [21]. Companies that fail to embrace agility risk falling behind their competition and losing market share [22; 23; 24]. Despite the burgeoning need for enterprise agility and the acknowledgment of its significance, there remains a conspicuous lack of robust quantitative methodologies to effectively measure the impact of agility implementation within an organization. Although numerous performance indicators and metrics have been proposed, a significant gap persists regarding statistically robust and widely accepted frameworks for comprehensively assessing enterprise agility. This deficiency constrains the ability of organizations to fully comprehend the implications of their agile transformation efforts, consequently limiting their capacity to optimize their strategies and processes. This research delves into an approach for assessing enterprise agility, integrating statistical analysis with continuous improvement methodologies [11; 25; 26]. A concrete challenge encountered by the case-study organization is presented, followed by the strategies that were adopted to overcome it, and the consequent results that were attained. Additionally, valuable insights and recommendations are furnished, catering to organizations that seek to deploy a similar system for the appraisal and enhancement of their enterprise agility. Specifically, this paper yields the following contributions:  
* The primary contribution of this paper is the proposal of a methodology, designed for the statistical measurement of the impact generated by the implementation of business agility within large organizations. * The methodology was validated through its application to TCS. This involved an examination of strategy for enhancing and measuring business agility. * Valuable insights and recommendations are extended to those organizations which are aspiring to devise and implement a similar system aimed at assessing and enhancing their business agility. Throughout this research, the application of robust statistical methods to measure business agility emerged as a crucial highlight. Specifically, the use of the Fisher-Snedecor power analysis test for variance inequality has proven to be valuable within the realm of organizational metric calculations and processes. This unique methodology not only facilitated the effective measurement of the impact generated by the implementation of business agility but also shed light on significant results.