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Despite decades of effort, governments and companies still struggle to accurately project the cost and schedule of large capital projects. Cost and schedule risk analysis are routinely used to better characterize potential outcomes. More recently, these formerly independent analyses are being combined into a joint cost and schedule risk analysis. However, very little data is available to describe how cost and schedule variances are related at the project level, and no real-world data is published to describe how cost and schedule variances are related at the cost account level. This research analyzes cost account-level detail from ten similar satellites programs to assess the relationship between cost and schedule variances. A common model breaks activities into two types: level of effort (LOE), in which cost is directly proportional to the cost account’s duration and discrete, where the cost is independent of the schedule. Data from the ten satellite programs is split into these two categories and analyzed separately. Marginal distributions for schedule and cost for each of the two databases are built using the generalized two-sided power distribution (GTSP) (Herrerias-Velasco et. al. 2009). Correlations between cost and schedule variance are calculated for the LOE and discrete cost accounts. Finally, joint distributions are created for each database using generalized diagonal band copulas first introduced by Bojarski (2002). The results showed that the GTSP effectively modeled the marginal distributions and the generalized diagonal band copula with a slope generating function (Kotz and Van Dorp 2009) successfully represented the observed joint distribution. Finally, the correlation values showed that, as expected, the cost and schedule variance for discrete cost accounts are not correlated. However, the LOE cost accounts showed a correlation well below the projected perfect correlation with a Spearman’s Rank correlation of just 0.154 and Kendall’s τ of 0.105. The models developed to validate the computed relationship between cost account-level cost and schedule variance were validated by removing one program’s data from the database, regenerating the models, and assessing the accuracy of the model against the program excluded from the model. The model derived from nine programs successfully modeled the results from the tenth program. These results provide both a modeling method and guidance for modeling parameters for joint cost and schedule risk analysis.

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