Reviewed Literature

Construction projects are associated with high uncertainty and risks. During the cost estimation phase, before the execution of a project, contractors are faced with limited information to rely on for reliable financial planning. Likewise, initial budget estimation tends varying because of micro and macroeconomic forces. Without effective cash flow management, constructing companies cannot survive in the industry. Construction projects are executed within a specific period. Therefore, cash flow refers to the balance between cash received and cash spent on construction during a project period (Zayed & Liu, 2012, pp. 170-73).

Lack of financial liquidity has been cited as the main reason why construction companies fail. A census report conducted in the United States associated construction companies with a higher failure rate at 14% compared to other companies. Another study by BizMiner industry reports realised in 2005 showed that 28.5% of the 853,372 construction companies that had been established in 2002 had failed two years later. 26.71% of the failures were caused by poor financial management and lack of liquidity (Park, et al., 2005, p. 165).

Cash flow forecasting and control is important for the survival of construction companies. Today, financial management has become part and parcel of organisational management. Using financial tools and models, contractors can easily forecast the construction budget. Moreover, contractors use cash flow diagrams to determine if the budget estimation is below or over budget (Kenley & Wilson, 1986, p. 214).

Contractors use both mathematical and nonmathematical approaches to forecast project cash flow. Nonmathematical models use traditional estimation methods; they are expensive, time-consuming and complicated. On the other hand, mathematical models use deterministic approaches hence cheaper and more straightforward (Ock & Park, 2016, p. 2170).

This paper is a review of the previous studies that have analysed models of project cash flow in the context of construction projects in Australia. Specific focus has been paid on the factors that cause discrepancies in the actual and projected cash flows as well as financial models used to control such forecast in the construction industry.

Zayed & Liu (2012, pp. 170-3), examined both micro- and macroeconomic factors that impact cash flow forecasting and how these factors can be used to develop a new cash flow modelling. Zayed & Liu identified 43 factors which were grouped into seven categories namely financial management, communication skills, subcontractors, and suppliers among others. The categorisation of the factors is based on their influence on the Cash inflows and outflows. This study established cash flow factors that should be considered as cash retention which influences cash inflow and is a loss to a contractor if a project is not completed; fluctuation of material cost which impacts cash outflow; change in project duration also affect both cash inflows and outflows.

Other factors that have been identified in this study are the change of contract order which affect the Internal Rate of Return (IRR) of a contract; Rework also hurts the cash flow of a project. Lastly, contract terms also make it difficult to manage liquidity and cash flow. Clauses like “pay if paid” or “pay when paid” make it difficult for contractors to manage project liquidity and cash flow. Zayed & Liu uses the identified factors to; first, establish the advantages and disadvantages of the previous cash flow forecasting models. Second, categorise the factors into three parameters of cash inflow, cash outflow, and overdraft which are important in developing new cash flow forecasting model (Zayed & Liu, 2012, p. 175).

Park, et al. (2005, p. 165), presented a  simple cash flow forecasting model (MWM) aimed at helping general contractors during the construction phase of a project. Park, Han, & Russell developed the model using the nature of a contractor’s budget and the general procedures to be followed during a construction phase. The authors used the comparable case study method to validate the benefits and limitation of MWM. This study relied on real data from four project to test the validity of the proposed model. The results showed that the model was reliable, accurate and straightforward.

However, the proposed model was associated with two issues that required further research. First, the MWM model relies heavily on earned value and planning cost; the forecasted cash flow cannot be accurate if the two factors are inaccurate. Second, obtaining a reliable variable at the job site is difficult especially where a subcontractor is executing an activity. Irrespective of the limitations, the authors maintain that the proposed MWM provides a comfortable and practical model for cash flow forecasting (Park, et al., 2005, p. 172).

Khanzadi, et al. (2017, pp. 1045-58), proposed the use of Bayesian Belief Networks (BBNs) model for cash flow forecasting. One advantage of the BBNs is that it takes financial risks associated with the construction project into consideration. Khanzadi, Eshtehardian, & Esfahani relied on the cause and effect relationship between the risk factors to develop the BBNs model. The authors successfully demonstrated the accuracy of their methodology in cash flow forecasting when applied in real projects. The study used mathematical formulas and input data to calculate the probabilistic cash inflows and outflows. The study was based on global construction projects. The authors proposed that further studies should be conducted on specific projects using the BBNs model. This study shows that construction projects can be managed effectively, in terms of cost and time, using the BBNs model.

Kenley & Wilson (1986, pp. 214-30), constructed the logit project cash flow model using an idiographic approach. Kenley & Wilson states that the model shows excellent results after successfully testing a large data sample. The authors This study registered a variation ranging between 1.0% and 4.6% from 72 tested projects which show insignificant systematic error. Authors took into account the idiosyncratic differences between projects which were ignored by the previous models. However, the logit model has some limitations. First, it ignores data below 10% and above 90% which affect the goodness of fit. A good cash flow forecasting model should be sensitive to the early stages of a project. Second, the model does not take into account allowance for late payments into account. Third, the model does not provide an explanation why variation exists between different projects. Irregardless of the weaknesses, the logit model is accurate and suitable for forecasting the future cash flows for construction projects.

Cheng, et al. (2015, pp. 678-82), introduced the Least Squares Support Vector Machine (LS-SVMAT) model for cash flow projections. LS-SVM is integrated with adaptive time function (ATF) to allow mapping of input and output elements of cash flow. ATF assist in determining the weight associated with each factor of cash flow projection over the project duration. Compared to the other models, LS-SVMAT applies appropriate time function hence produce excellent forecasting results. The authors also use the Differential Evolution (DE) for process validation which is more appropriate compared to the trial and error approach. However, the LS-SVMAT has several limitations. First, the model was developed using data collected from a single construction site. Although the data was capable and homogenous, historical data, as well as data from other databases, should be incorporated. Nevertheless, the model has a promising future.

Hwee & Tiong (2001, pp. 351-63), developed a Cash Flow Forecasting System (CAFFS) model by relying on prior knowledge on cash flow and associated risk factors. Hwee & Tiong used capital requirements and Internal Rate of Return (IRR) to analyse cash flow performance of projects. Likewise, the authors tested the capability of the forecasting model and established that CAFFS could achieve good results. Although this study relied on a few and simple data, the findings showed the accurate prediction of cash flow forecasting. The authors also used sensitivity analysis to test the five identified risk factors. The analysis showed that IRR improved slightly in the short term while the capital requirement increased significantly. In the longer term, capital requirement decreased which led to the reduction of IRR as well. Lastly, the authors also consider the material cost and found out that it had a significant impact on the cash flow. The model proved to be useful in determining the impact of risks factors on the cash flow as well as predicting the cash flow with the progress of a project.

Ock & Park (2016, pp. 2170-73), proposed the Algorithm as an appropriate model for cash flow forecasting. Ock & Park focused on comparing and analysing the existing problems in the current forecasting models and develop a new model to established problems. The authors go-ahead to develop and validate the algorithm model using a sample project. The algorithm model would help at the planning stage of a project by managing liquidity effectively. The authors developed the model by relying on factors such as time lag, budget at the beginning, costs categories, and the weight associated with such costs. The model also considers cash inflow variables, cash outflow variables and output variables in cash flow forecasting. The study provided the algorithm model as a solution to the liquidity problems that face construction projects.

Lucko (2013, pp. 239-42), proposed the use of the singularity functions, based on the Time Value of Money (TVM), to forecast cash flows in construction management. Lucko suggests that the model will help contractors improve the net present value (NPV) of a project by enhancing cash flows. The difference between the singularity model and the previous models is that the former considers both NPV and profit maximization while the latter ignores NPV. The study has successfully connected all the elements of cash flow as well as the time frame for project completion. One advantage of the model is that it relies on TVM which allow comparison of values during the planning and budgeting phases of a project. Lastly, the models provide contractors with a capable analytical process which supports competition in the challenging construction market.

Mohagheghi, et al. (2017, pp. 3394-96), developed an interval type-2 fuzzy model for cash flow forecasting. Mohagheghi, S, & Vahdani developed the model in such a way that it can calculate both the minimum and the maximum cash distribution during the project life. The model gives contractors a more flexible approach to calculating uncertainties in the construction industry. The authors use different levels to define the minimum and maximum levels of project durations and uncertainties. The levels are used to show the worst and best scenarios during the project life cycle. The authors made the model more reliable and practical by dividing the total costs into direct and indirect costs. The interval type-2 fuzzy model is reliable because it provides contractors with an insightful approach to forecasting future cash flows. Another advantage of the model is that it considers the uncertainty and risks associated with projects. Mohagheghi, S, & Vahdani have presented a model that has capabilities of resolving real problems facing the construction industry.

Conclusion

Although nine different cash flow forecasting models have been discussed, this sections focus on the application of Algorithm of Cash Flow Forecasting Model in the planning stage of construction projects in Australia.

The Algorithm of Cash Flow Forecasting Model is applicable in any construction industry globally. Specifically, the model introduced the concept of time lag which was lacking in the previous models. With the ability to determine the precise time lag for a given project, contractors have the ability to make a strategic decision during the planning phase. Ock & Park (2016) considered the time lag associated with different cost elements which make the model more precise and reliable (Ock & Park, 2016, p. 2171).

Second, the model cost of five stages namely: input of project information, weights associated with each cost, calculation of cash flows, monthly progress planning, time lag and comparing cashes in and out. Therefore, the model realises on these five models to produce a reliable formula of cash flow forecasting. Contractors can easily establish the exact time lag of a construction project as the planning stage (Ock & Park, 2016, p. 2175).

The factors mentioned above support the application of the Algorithm of Cash Flow Forecasting Model in the planning stage of construction projects in Australia.

References

cheng, M.-Y., Hoang, N.-D. & Wu, Y.-W., 2015. Cash Flow Prediction for Construction Project using A Novel Adaptive Time-Dependent Least Squares Support Ector Machine Inference Model. Journal of Civil Engineering and Management, P. 21(6): 679–688.

Hwee, N. G. & Tiong, R. L. K., 2001. Model on cash flow forecasting and risk analysis for contracting firms. International Journal of Project Management , p. 20 (2002) 351–363.

Kenley, R. & Wilson, O. D., 1986. A construction project cash flow model- an idiographic approach. Construction Management and Economics, pp. 4, 213-232.

Khanzadi, M., Eshtehardian, E. & Esfahani , M. M., 2017. Cash flow forecasting with risk consideration using Bayesian Belief Networks (BBNS). Journal of Civil Engineering and Management, pp. 23:8, 1045-1059.

LUCKO, G., 2013. Supporting financial decision-making based on time value of money with singularity functions in cash flow models. Construction Management and Economics, pp. Vol. 31, No. 3, 238–253.

Mohagheghi, V., S, M. . M. & Vahdani, B., 2017. Analyzing project cash flow by a new interval type-2 fuzzy model with an application to construction industry. Neural Comput & Applic, p. 28:3393–3411.

Ock, J.-H. & Park, . H. K., 2016. A Study on the Algorithm of Cash Flow Forecasting Model in The Planning Stage of a Construction Project. KSCE Journal of Civil Engineering , pp. 20(6):2170-2176.

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 .

Zayed, T. & Liu, Y., 2012. Cash flow modeling for construction projects. Engineering, Construction and Architectural Management, pp. Vol. 21 Issue: 2, pp.170-189.