Cash flow optimization for construction engineering portfolios

One of the main issues in construction projects is finance; proper cash-flow management is necessary to insure that a construction project finishes within time, on budget, and yielding a satisfying profit. Poor financial management might put the contractor, or the owner, in a situation where they are unable to finance the project due to insufficient liquidity, or where they are engaged in excessive loans to finance the project, decreasing the profit, and even creating unsettled debts. Engagement with a portfolio of large construction projects, like infrastructure projects, makes attention to finance more critical, due to large budgets and long project durations, which also requires attention to the time value of money when the project spans over many years and the work environment has a high inflation rate. This thesis aims at the analysis and optimization of the cash-flow request for large engineering portfolios from the contractor\u27s point of view. A computational model, with a friendly user interface, was created to achieve that. The user is able to create a portfolio of projects, and create activities in them with different relationship types, lags, constraints, and costs, as similar to commercial scheduling software. Parameters necessary for the renumeration are also considered, which include the down payment percentage, duration between invoices, duration for payment, retention percentage, etc. The model takes into consideration the time value of money, calculated with an interest rate assigned to the projects by the user; this could be the inflation rate or the (Minimum Attractive Rate of Return) MARR of the contractor. Optimization is done with the objective of maximizing the Net Present Value (NPV) for the projects as a whole, discounted at the start of the portfolio. The variables for the optimization are lags that are assigned for each activity, which, after rescheduling, delays the activities after their early start with the value of those lags, and thus creates a modified cash flow for the project. Optimization of those variables, within scheduling constraints results in a near-optimum NPV. Verification of the model was done using sets of portfolios, and the validation was done using an actual construction portfolio from real life. The results were satisfactory and matched initial expectations. The NPV was successfully optimized to a near optimum. A sensitivity analysis of the model was conducted and it showed that the model behaves as expected for different inputs. A time test was performed, taking into consideration the effect of the size and complexity of a portfolio on the calculation time for the model, and it showed that the speed was satisfactory, though it should be improved. Overall, the conclusion is that the model delivers its goal of maximizing the Net Present Value of a large portfolio as a whole

A System Dynamics Approach for Study of Population Growth and the Residential Housing Market in the US

The US Consensus bureau estimated the total construction spending at 1,320,305 Million Dollars, in February 2020, with an increase of 1.1% since last February. The construction market is large, and risky. Prediction of the market behavior, for several years ahead, is needed in order to take strategic investment decision for long and expensive projects. The goal of this research is to study the relationship between population growth and the housing market. To that end, a system dynamics model is developed. System dynamics is a top-down approach that starts with the high-level behavior of a complex system to simulate the behavior of that system over time. The developed model simulates the housing market by matching the population growth with the housing demand in monthly time steps. As such, the parameters of the developed model include birth rate, life expectancy, immigration, emigration, and construction seasonality. Using these parameters, the model simulates the population size and demand for housing. For validation, the outputs of the model are compared with real-life data for the US. When complete, the model should assist market researchers in simulating the housing market. This research benefits large real estate developers, construction companies, governmental and financial agencies

Cash flow optimization for construction portfolios

[abstract not available