Abstract

Cash flow modeling is a very useful financial management tool that contractors use to run a sustained business. Contractors manage multiple activities within a single project. The activities’ start times are the inherent variables which determine the values of periodical negative cumulative balances and the other cash-flow parameters of cash flow model. Since the start times of activities vary while the dependency is maintained, in any given schedule the maximum value of the negative cumulative balance varies, as do the values of the other cash-flow parameters. This work reveals a system that can identify those activities that have the most influence on cash flow.
The Monte Carlo Simulation technique has been employed here to generate schedules and their associated cash flow models for a case study by randomly specifying the activities’ start times between their respective early and late start times. Uniform discrete probability distributions are assumed for the activities’ start times, with the minimum and maximum values representing the early and late start times, respectively. In addition to the randomness of the activities’ start times, the simulation model considered the stochastic nature of the periodic cash in and cash out transactions in the cash flow model by adjusting their values to account for the impact of 43 qualitative factors identified in an earlier study.
The @RISK commercial software was used to implement the simulation of the cash flow model built in an MS-Excel environment. Upon completing the specified number of runs, @RISK displays the probability distributions for the cash-flow parameters including the financing costs, maximum negative cumulative balance, project duration, and project profit.
In addition, three scenarios are defined; each incorporating a different number of qualitative factors which impact the project cash inflow and cash outflow transactions. Scenario I incorporates none, Scenario II incorporates six factors impacting cash inflow transactions and nine factors impacting cash out flow transactions, and scenario III incorporates all of the qualitative factors that impact cash inflow and cash outflow transactions. Moreover, the ranges of the activities’ start times have been extended by supplementing the total floats with extension increments for the three scenarios.
The results are presented and analyzed based on the three scenarios. The activities’ criticality to cash-flow parameters is assessed by evaluating the number of times a given activity determined a particular cash-flow parameter over the number of runs. This criticality measurement offers project managers very useful criteria with which to identify the activities that are most urgent to be completed on time.