1. Introduction

2. Enterprise Agility

3. Need to Measure Agility

4. The Latin American Challenge

4.2. Escalating to CBO

In this document, we want to explain in detail the challenge of improving enterprise agility in CBO, including the methodology and results obtained.

For CBO, we apply a process similar to the IT case, which involves weekly monitoring of the metric in all TCS Latam countries. After having enough data points, we conduct an initial analysis to calculate the mean and dispersion at the regional level, that is, considering all Latin American countries where TCS operates with CBO services.

This analysis yields a baseline for the second quarter of the fiscal year 2022 (Q2-FY22) of 0.51 on average and 0.19 in terms of dispersion (standard deviation), as shown in Figure 3. As previously explained, the scale of this index ranges from 0 to 1, where 0 represents low debt and high agility, and 1 represents high debt and low agility. In this sense, having a score of 0.51 represents a medium or regular agility, and as our Agile Enterprise Vision inspires us to the Zero-For-Tomorrow (0-4-2), which means achieving zero agile debt and consequently enabling high enterprise agility, we decide to create an improvement team [11,26] with the aim of significantly reducing CBO AgilityDebt™ in the organization

Table 1. Sample statistics Q2-FY22.

Table 1. Sample statistics Q2-FY22.

Statistic	Value Q2-FY22
Size	28
Mean	0.51
Std. dev.	0.19
Variance	0.04

5. Agile Transformation Journey at CBO

6. Results and Statistical Validation

6.2. Data Capture

At the end of the fourth quarter of fiscal year 2022 (Q4-FY22), we extract a new sample of data points from TCS Latam countries with CBO services, yielding a result of 0.29 for the mean and 0.10 for the standard deviation. Although we meet our established target of reducing AgilityDebt™ by 2,230 basis points, we need to perform a statistical analysis to verify the significance of this reduction.

Figure 5 shows a visualization with histograms and main statistics of both evaluated samples, remember that in our case these samples were:

• Sample 1: corresponding to 28 data points from the Q2-FY22 period.
• Sample 2: corresponding to 28 data points from the Q4-FY22 period.

Table 2. Sample statistics Q2-FY22 and Q4-FY22.

Table 2. Sample statistics Q2-FY22 and Q4-FY22.

Statistic	Value Q2-FY22	Value Q4-FY22
Size	28	28
Mean	0.51	0.29
Std. dev.	0.19	0.10
Variance	0.04	0.01

6.4. Difference of Variances Test (Fisher’s F-Test)

Before conducting the mean difference test, it is necessary to evaluate whether the variances of both samples are equal or not, which is done through a Fisher’s F test [41,42]. Running the test shows that the variances differ significantly and that we significantly reduce variability in the Q4-FY22 quarter compared to the Q2-FY22 quarter.

The Fisher’s method involves taking the ratio of the larger population variance, ${\sigma }_{Q2-FY22}^2$, to the smaller population variance, ${\sigma }_{Q4-FY22}^2$, and then looking up the ratio on an F distribution curve [41,42,47].

In this test, the null hypothesis states that the ratio is equal to 1:

$$H_0:\frac{{\sigma }_{Q2-FY22}^2}{{\sigma }_{Q4-FY22}^2}=1$$

and the alternative hypothesis states that the ratio is greater than 1:

$$H_1:\frac{{\sigma }_{Q2-FY22}^2}{{\sigma }_{Q4-FY22}^2}>1$$

Figure 7. Difference of variances test (Fisher’sF-test). This graph allows visualizing the hypothesis testing of variance difference and the associated statistical power analysis, which represents the effect produced.

Figure 7. Difference of variances test (Fisher’sF-test). This graph allows visualizing the hypothesis testing of variance difference and the associated statistical power analysis, which represents the effect produced.

Table 3. F-Test results .

Table 3. F-Test results .

F-test	3.31
F-critical	1.90
p-value	0.0014
Power	0.9217
Beta	0.0783
Effect (rate std. dev)	1.8204

Conclusion: For this one-tailed test, since the p-value of 0.0014 is less than the pre-established alpha ($\alpha$) of 0.05, or since the F statistic of the test equal to 3.31 falls outside the non-rejection zone (0.00 to 1.90), we proceed to reject the $H_0$, therefore, we conclude that the variance of the Q2-FY22 period is statistically higher than the variance of the Q4-FY22 period. Then, upon evaluating the power of $H_1$ [46], we see that the exhibited power of 0.92 is greater than the threshold of 0.80 and the relative effect size of 1.8204 is also highly relevant, so we conclude that in practical terms the variance of the Q4-FY22 period is significantly lower than that of the Q2-FY22 period, meaning that there is a significant and relevant reduction in the variability of the AgilityDebt™ data in CBO.

6.5. Difference of Means Test (Student’s T-Test)

Once we complete the variance difference test, we proceeded to carry out the expected test of difference of means (applying different variances). The T-test of Student can be utilized in cases where at least one of the sample sizes is small (less than 30 units), the samples are independent, the populations have normal distribution, and the variances of both populations are equal. [43,47]. If the latter requirement is not met (equality of variances), as is the case in our study since the variances are not equal, we apply the T-test of Student with an adjustment in the equation to calculate the degrees of freedom, without calculating the pooled variance [48]. In this case, we use the sample variances to calculate the standard deviation of the difference of the sample means.

In this test, the hypotheses are formulated as follows:

The null hypothesis states that the difference is equal to 0:

$$H_0:{\mu }_{Q4-FY22}-{\mu }_{Q2-FY22}=0$$

and the alternative hypothesis states that the difference is less than 0:

$$H_1:{\mu }_{Q4-FY22}-{\mu }_{Q2-FY22}<0$$

This is because we want to test whether the reduction exhibited in AgilityDebt™ from Q4-FY22 compared to Q2-FY22 was actually less than zero, and not just due to chance.

Figure 8. Difference of means test (Student’sT-test). This graph allows visualizing the hypothesis testing of mean difference and the associated statistical power analysis, which represents the effect produced.

Figure 8. Difference of means test (Student’sT-test). This graph allows visualizing the hypothesis testing of mean difference and the associated statistical power analysis, which represents the effect produced.

Table 4. T-Test results .

Table 4. T-Test results .

T-test	-5.48
T-critical	-1.68
p-value	0.0000
Power	0.9998
Beta	0.0002
Effect size	-0.2230

Conclusion: Since the T test statistic of -5.48 was less than the critical T equal to -1.68, or the p-value was less than the preset alpha ($\alpha$) of 0.05, the $H_0$ is rejected, concluding that the average of Q4-FY22 is statistically lower than the average of Q2-FY22.

As the $H_0$ is rejected, we proceed to calculate the effect size and power of the test [44,45].

From the power analysis, we conclude that the effect [46] produced by the AgilityDebt™ improvement project, which reduced the indicator by 2,230 basis points, is highly significant. Additionally, the reduction exhibits statistical power of almost 100%. Therefore, the improvement is not due to random chance, but is real and highly relevant.

7. Continuous Improvement

8. Recommendations to Start an Enterprise Agility Measurement System

9. Conclusions

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