## Appendix A Literature Review
The manner of using the stochastic S curve (SS curve) as an alternative method in relation to the determining S curve is described in detail by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], et al. [35]. The probability of this approach allows for determining the related cost with better accuracy. A new method was developed by combining the EAC index method with statistical analyses. In accordance with this method, one of the improvements related to a limitation of the construction cost estimate is the use of the S-curve formulation of the EAC index method, by means of which we can monitor the exceeded cost (so-called tail chasing) whenever there is a CPI change during the construction implementation process [36]. According to the publications written by [PERSON], [PERSON] et al. [37], the index calculation using EAC does not consider the consequences of the future order risks. That is why a risk management (RM) implementation is proposed within the frame of a classic EVM calculation in accordance with [PERSON] [30] and [PERSON], [PERSON] [31]. By implementing RM, the weakness of EVM is removed, namely the assumption that future performance can be predicted from past performance. On the contrary, RM can be weakened by only looking forwards with no respect to the past. By the synergy of the EVM-RM combination, it is possible to determine the optimal solution of construction process management. An interesting probability model introduced by [PERSON], [PERSON] et al. [38] is based on a comparison of the probability ratio of the predicted and subsequently implemented performances for projects conducted in the past and similar projects that are in the planning stage. The authors [PERSON], [PERSON] and [PERSON] [39] tried to identify delays in construction projects. The methodology selected for this research is the questionnaire survey. The form consists of five important items of work which were investigated for delays. The measures reducing the delays were suggested by the means based on these observations. The study published by [PERSON], [PERSON] et al. [40] proposed to construct the distribution function of the SMS/SSMS (statistical metric space/statistical semi-metric space) in a natural way to quantify the reliability of the forecast. The method specifies an easy to implement scheme for the development of algorithms for determining a predictor based on the statistical metric space theory. Two models were presented by [PERSON], [PERSON] et al. [41] predicting the cost overrun percentage in construction projects where the first one is based on the principle of the regression analysis. The second used model is the reasoning (CBR) model. The CBR method can be an effective means of utilizing knowledge gained from past experience to estimate the percentage of cost overrun in construction works. The results revealed that regression model had higher predicting capabilities in comparison with the CBR mode. The authors [PERSON], [PERSON] et al. [42] applied a parsimonious multiple linear regression (MLR) model for predicting the percentage of project cost overruns. The purpose of this study was to determine the factors responsible for impacting the cost performance of Ethiopian public construction projects. A one-factor ANOVA test was conducted to determine whether construction cost and schedule overruns significantly varied based on the types and sizes of the projects [43]. The research works proved that large long-duration projects had significantly higher cost and schedule overruns than smaller ones. * _Simulation technique_Several different simulation techniques, such as discrete event simulation (DES) [44], system dynamics (SD) [45] and agent-based modeling (ABM) [46] have been used to model complex construction systems such as construction processes and project management practices; however, these techniques do not take into account the subjective uncertainties that exist in many construction systems.