A study on the algorithm of cash flow forecasting model in the planning stage of a construction project

2015 ◽  
Vol 20 (6) ◽  
pp. 2170-2176 ◽  
Author(s):  
Jong-Ho Ock ◽  
Hyung Keun Park
Keyword(s):  
Cash Flow ◽  
Construction Project ◽  
Forecasting Model ◽  
Planning Stage ◽  
Cash Flow Forecasting

Cash flow forecasting in construction project

2004 ◽  
Vol 8 (3) ◽  
pp. 265-271 ◽  
Author(s):  
Hyung-Keun Park
Keyword(s):  
Cash Flow ◽  
Construction Project ◽  
Cash Flow Forecasting

Review and Update of Virginia Department of Transportation's Cash Flow Forecasting Model

1997 ◽  
Vol 1576 (1) ◽  
pp. 10-19 ◽  
Author(s):  
James S. Gillespie ◽  
Cherie A. Kyte
Keyword(s):  
Cash Flow ◽  
Flow Model ◽  
Forecasting Model ◽  
Department Of Transportation ◽  
Construction Contracts ◽  
Forecasting Method ◽  
Cash Flow Forecasting ◽  
Transportation Departments ◽  
Virginia Department ◽  
Operational Aspects

Accurate predictions of cash balances are essential to the month-to-month operations of transportation departments. The Virginia Department of Transportation (VDOT) relies on a general cash flow forecasting model to predict monthly outflows and inflows of cash to various activities. VDOT’s model was developed in the mid-1980s and reflects the realities of that time. Much has changed in the operational aspects of VDOT since then. The cash flow model was updated. The research focused on two components (or submodels) of the general cash flow model: the monthly factors submodel, which is used to forecast monthly expenditures on construction contracts, and the maintenance expenditures submodel, which is used to forecast monthly outlays on maintenance activities. The result was an update of the basic elements of the monthly factors submodel while, for the most part, retaining the original underlying methodology. The research yielded a new methodology for the prediction of monthly expenditures on maintenance. The new forecasting method is based on a regression equation.

scholarly journals Development of a Cash Flow Forecasting Model for Housing Construction

2012 ◽  
Vol 12 (3) ◽  
pp. 257-265 ◽  
Author(s):  
Joo-Hwan Jang ◽  
Ju-Hyung Kim ◽  
Nam-Yong Jee
Keyword(s):  
Cash Flow ◽  
Housing Construction ◽  
Forecasting Model ◽  
Cash Flow Forecasting

scholarly journals Delay in payment effects on productivity of small and medium construction companies in Oman: exploration and ranking

2021 ◽  
Author(s):  
Mubarak Al Alawi
Keyword(s):  
Cash Flow ◽  
Questionnaire Survey ◽  
Financial Stability ◽  
Construction Project ◽  
Internal Factors ◽  
Construction Productivity ◽  
Construction Companies ◽  
Productivity Rate ◽  
Delay In Payments ◽  
Productivity Factors

AbstractMaintaining a stable productivity rate in a construction project is a challenge. Many external and internal factors influence it. Delay in payment is one of the factors representing the project cash flow and mirrors the company’s financial stability status. This study explores the delay in payments effects on the construction productivity of the small and medium construction companies in Oman. Also, it ranks the delay in payment among other productivity factors. Sixty-five small and medium construction companies registered in Oman Tender Board participated in the questionnaire survey. The results showed that delay in payment significantly affects the financial stability of the companies. The delay in payment was ranked third out of 21 influencing productivity factors. The results were compared with a previous study that covered large construction companies in Oman. It was found that the rank of delay in payment in the small and medium construction is significantly higher than what was found in large companies.

Application of machine learning in predicting construction project profit in Ghana using Support Vector Regression Algorithm (SVRA)

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Emmanuel Adinyira ◽  
Emmanuel Akoi-Gyebi Adjei ◽  
Kofi Agyekum ◽  
Frank Desmond Kofi Fugar
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Cash Flow ◽  
Predictive Accuracy ◽  
Model Development ◽  
Construction Project ◽  
Support Vector ◽  
Sensitivity Index ◽  
Content Type ◽  
Hyperparameter Selection

PurposeKnowledge of the effect of various cash-flow factors on expected project profit is important to effectively manage productivity on construction projects. This study was conducted to develop and test the sensitivity of a Machine Learning Support Vector Regression Algorithm (SVRA) to predict construction project profit in Ghana.Design/methodology/approachThe study relied on data from 150 institutional projects executed within the past five years (2014–2018) in developing the model. Eighty percent (80%) of the data from the 150 projects was used at hyperparameter selection and final training phases of the model development and the remaining 20% for model testing. Using MATLAB for Support Vector Regression, the parameters available for tuning were the epsilon values, the kernel scale, the box constraint and standardisations. The sensitivity index was computed to determine the degree to which the independent variables impact the dependent variable.FindingsThe developed model's predictions perfectly fitted the data and explained all the variability of the response data around its mean. Average predictive accuracy of 73.66% was achieved with all the variables on the different projects in validation. The developed SVR model was sensitive to labour and loan.Originality/valueThe developed SVRA combines variation, defective works and labour with other financial constraints, which have been the variables used in previous studies. It will aid contractors in predicting profit on completion at commencement and also provide information on the effect of changes to cash-flow factors on profit.

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