Cash Flow Predictability on a $Billion CIP

摘要

Predicting cash flow is an important element in Gwinnett County's $1.5 Billion Water and Wastewater CapitalrnImprovements Program (CIP). Being able to accurately forecast cash flow reduces numerous problems inrnprioritizing, scheduling, funding, and completing essential projects. Gwinnett's Department of Water Resourcesrn(DWR), in order to meet the infrastructure demands of a rapidly growing Metro-Atlanta community, has manyrnhundreds of projects within its CIP. With such an enormous workload and strain on available capital, DWR soughtrnto achieve better predictability of project cash flow. Because of the quantity and complexity of projects included inrnthe CIP, a Primavera P3 master scheduling program was used and cash flow projections were developed on arnproject-by-project basis. In each project, a cash flow distribution similar to a Gauss-Laplace normal probabilityrndistribution was initially used. Through trial and error, the probability distribution was often modified tornaccommodate front-loading or back-loading depending on project type. The resulting information was then used tornconsider various financial options to make funds available to complete the projects. Although these cash flowrnforecasts seemed to at least get us into the ballpark, these predictions were often overly optimistic, fraught withrntechnical concerns such as a "bow-wave " effect, and relied heavily on individuals' reasoning and experience. Thernresults were often inaccurate forecasts which led to under-expenditure of bond funds and a growing desire for arnmore accurate model. By analyzing historical cash flow patterns and broadly accommodating the variables, DWRrndeveloped normalized cash flow curves for various project types. These curves yield better predictive models,rnthereby improving the forecasting capability of DWR. This technical paper discusses the analyses and presents thernpredictive models used.