The Cost Monitoring of the Construction Machinery with Using the Stochastic Progress-Based S-curves

Abstract

Contribution presents methodology for evaluating at-completion project performance status. Accurate cost and schedule project forecasts are difficult to generate when considering the impact of such events as unforeseen cost changes, material delays, scope deviation, changes to the project execution plan and poor subcontractor performance. In reality, the original estimate may be considered the first project forecast and at the point of project completion, the latest updated estimate (last forecast) and the actual amount of what is being expended should be the same. Final project performance is determined by comparing the planned budget and project duration, with the expected forecasted final budget and elapsed time. The stochastic S-curve methodology permits objective evaluation of project performance without the limitations inherent in a deterministic approach. This paper used the stochastic S curve to monitor the cost and time consumption in operation of the construction machines. The contribution presents a partial outcome from the dissertation thesis called the Interactive tools for resource optimization in construction.