## 4 Results  
The method described in Section 3.3 allows the calculation of the Time-at-Risk for the entire project. Table 4 shows the TaR results for the 95 and 99 quantiles. Scenario A is the baseline scenario, assuming no changes to the schedule. The simulation was performed using the Monte Carlo technique with 10,000 repetitions. A PERT distribution of times for each stage of the investment process was assumed, with the most likely duration defined in the project documents and shown in Tables 2 and 3. The results show that project managers have to allow for a time delay of up to 4.47 and 6.35 quarters, resulting from the 95 and 99 quantiles, respectively. Of course, these are extreme values, which in fact represent the fifth and first percentiles of the least favourable trajectories of the investment process (when arranged in order from 1 to 10,000, i.e., from least favourable to most favourable). Such a view of the project and its associated schedule is the essence of the Time-at-Risk method. While managers do not have to expect such an extremely unfavourable development during the implementation of the entire process as indicated by the Time-at-Risk times shown in Table 4, they cannot rule out the worst-case scenario. Knowing these times allows them to be aware of what to expect if many elements of the project implementation fail.  
All calculations were done in R-Studio using the packages Distributional and PerformanceAnalytics. In the case of Scenarios B and C, some changes in the timing of some stages of the investment project were assumed. In the case of Scenario B, Stages 7 and 11 were postponed by 3 months (in line with previous slack assumptions). Scenario C results in a slightly lower TaR as it assumes a one month earlier start of four phases (10 th, 11 th, 13 th and 16 th), which slightly reduces the risk of project extension.  
The use of PerformanceAnalytics package allows for reflecting the contribution of each stage to the overall TaR measure. For example, one of the stages with the largest contribution to TaR is the project implementation stage (PIS) whose deviations from assumed time schedules are shown in Figure 8. The histograms and Q-Q plot shows that PERT distribution was used to simulate changes from the assumed time schedules.  
More importantly, different project stages can also be visualised in the context of their time completion distribution across the timeline of the whole project (see Figure 9). The individual distributions of changes in time schedules are based on the PERT distribution (more outliers in its right tail).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Time-at-Risk (TaR)** & **Scenario A** & **Scenario B1** & **Scenario C2** \\
**(in Quantiles)** & & & \\ \hline
99\% quantile & 6.35866 & 6.77638 & 6.15269 \\
95\% quantile & 4.47752 & 5.12026 & 4.40843 \\ \hline \end{tabular} \({}^{1}\)Shift in stages: ASECD/seven stage/(three quarters) and SSGCVED/11 stage/(four quarters). \({}^{2}\) Each of the following phases: SEED/10 stage/, SSGCVED/11 stage/, COMMS/13 stage/, and IOS/16 stage/started one quarter earlier and ended earlier.  
\end{table}
Table 4: Different scenarios reflected in time schedules.