The t-value is a test statistic for t-tests that measures the difference between an observed sample statistic and its hypothesized population parameter in units of standard error. A t-test compares the observed t-value to a critical value on the t-distribution with (n-1) degrees of freedom to determine whether the difference between the estimated and hypothesized values of the population parameter is statistically significant.
The test produces a t-value of 2.5. On the t-distribution with (n-1 = 49) degrees of freedom, this t-value corresponds to a p-value of 0.0158. For most common significance levels, this result is statistically significant. Therefore, you reject the null hypothesis that the mean length meets the target, and conclude that the process needs improvement.