Abstract
Duration and cost of a
construction project is estimated based on the construction schedule and cost
analysis which are arranged according to the quantity take-off data prepared
before the commencement of the construction. Construction schedule and budget
may deviate because of the unforeseeable factors which might happen during
construction. This situation would also affect cash-flow. Omission of updating the cash-flow because of the
heavy workload during the construction would prevent the foreknowledge of
balance of income and expenditure which may jeopardize financial situation of
the construction. In this study, a model is developed which construct a
relation between construction schedule and progress payments in order to obtain
most accurate and update cash-flow and prevent the aforementioned problems. To
accomplish this, all of the operation steps of quantity take-off are formulated
and the formulae are defined to spreadsheet application. Man-hour and durations
of activities are estimated by considering the quantity take-off data. Start
and finish dates of the activities are obtained by work planed which is
prepared by linear schedule. Costs of activities are determined by unit price
analysis and daily performed construction items and daily expenditures are
computed. Possible revisions
on the durations of the activities are updated together with the construction
schedule and the progress payments are recomputed without any human
intervention. Computations are executed and demonstrated on spreadsheet
application in order to make sure that all of the contractors can implement the
proposed method. The contractors would be able to update the cash-flow
immediately and foresee any adverse condition beforehand and may take
precautions.
References
- Boussabaine, A. H., & Kaka, A. P. (1998). A neural networks approach for cost flow forecasting. Construction Management & Economics, 16(4), 471-479.
- Chen, H. L., O’Brien, W. J., & Herbsman, Z. J. (2005). Assessing the accuracy of cash flow models: the significance of payment conditions. Journal of construction engineering and management, 131(6), 669-676.
- Hwee, N. G., & Tiong, R. L. (2002). Model on cash flow forecasting and risk analysis for contracting firms. International Journal of Project Management, 20(5), 351-363.
- Jarrah, R. E., Kulkarni, D., & O’Connor, J. T. (2007). Cash flow projections for selected TxDoT highway projects. Journal of construction engineering and management, 133(3), 235-241.
- Kaka, A. P., & Price, A. D. F. (1993). Modelling standard cost commitment curves for contractors' cash flow forecasting. Construction Management and Economics, 11(4), 271-283.
- Kaka, A. P. (1996). Towards more flexible and accurate cash flow forecasting. Construction Management and Economics, 14(1), 35-44.
- Kenley, R., & Wilson, O. D. (1989). A construction project net cash flow model. Construction Management and Economics, 7(1), 3-18.
- Kenley, R. (1999). Cash farming in building and construction: a stochastic analysis. Construction Management & Economics, 17(3), 393-401.
- Maravas, A., & Pantouvakis, J. P. (2012). Project cash flow analysis in the presence of uncertainty in activity duration and cost. International journal of project management, 30(3), 374-384.
- Miskawi, Z. (1989). An S-curve equation for project control. Construction Management and Economics, 7(2), 115-124.
- Ranasinghe, M. (1996). Total project cost: a simplified model for decision makers. Construction Management & Economics, 14(6), 497-505.
- Park, H. K., Han, S. H., & Russell, J. S. (2005). Cash flow forecasting model for general contractors using moving weights of cost categories. Journal of management in Engineering, 21(4), 164-172.
- Tangsucheeva, R., & Prabhu, V. (2014). Stochastic financial analytics for cash flow forecasting. International Journal of Production Economics, 158, 65-76.
Abstract
Keywords
References
- Boussabaine, A. H., & Kaka, A. P. (1998). A neural networks approach for cost flow forecasting. Construction Management & Economics, 16(4), 471-479.
- Chen, H. L., O’Brien, W. J., & Herbsman, Z. J. (2005). Assessing the accuracy of cash flow models: the significance of payment conditions. Journal of construction engineering and management, 131(6), 669-676.
- Hwee, N. G., & Tiong, R. L. (2002). Model on cash flow forecasting and risk analysis for contracting firms. International Journal of Project Management, 20(5), 351-363.
- Jarrah, R. E., Kulkarni, D., & O’Connor, J. T. (2007). Cash flow projections for selected TxDoT highway projects. Journal of construction engineering and management, 133(3), 235-241.
- Kaka, A. P., & Price, A. D. F. (1993). Modelling standard cost commitment curves for contractors' cash flow forecasting. Construction Management and Economics, 11(4), 271-283.
- Kaka, A. P. (1996). Towards more flexible and accurate cash flow forecasting. Construction Management and Economics, 14(1), 35-44.
- Kenley, R., & Wilson, O. D. (1989). A construction project net cash flow model. Construction Management and Economics, 7(1), 3-18.
- Kenley, R. (1999). Cash farming in building and construction: a stochastic analysis. Construction Management & Economics, 17(3), 393-401.
- Maravas, A., & Pantouvakis, J. P. (2012). Project cash flow analysis in the presence of uncertainty in activity duration and cost. International journal of project management, 30(3), 374-384.
- Miskawi, Z. (1989). An S-curve equation for project control. Construction Management and Economics, 7(2), 115-124.
- Ranasinghe, M. (1996). Total project cost: a simplified model for decision makers. Construction Management & Economics, 14(6), 497-505.
- Park, H. K., Han, S. H., & Russell, J. S. (2005). Cash flow forecasting model for general contractors using moving weights of cost categories. Journal of management in Engineering, 21(4), 164-172.
- Tangsucheeva, R., & Prabhu, V. (2014). Stochastic financial analytics for cash flow forecasting. International Journal of Production Economics, 158, 65-76.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Civil Engineering |
| Journal Section | Araştırma Articlessi \ Research Articles |
| Authors | |
| Publication Date | March 25, 2019 |
| Submission Date | September 14, 2018 |
| Acceptance Date | December 4, 2018 |
| Published in Issue | Year 2019 Volume: 7 Issue: 1 |