Cash flow forecasting in construction project

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.

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

Engineering Construction & Architectural Management ◽

10.1108/ecam-08-2020-0618 ◽

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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