Description
Cash flow forecasting or Cash Flow management is a key aspect of financial management of a business, planning its future cash requirements to avoid a crisis of liquidity.
Cash Flow Forecasting Model for General Contractors Using Moving Weights of Cost Categories
Hyung K. Park1; Seung H. Han2; and Jeffrey S. Russell3
Abstract: This research introduces the development of a project-level cash ?ow forecasting model from a general contractor’s viewpoint. While most previous models have been proposed to assist contractors in forecasting cash ?ow in the early stage of pretendering or the planning phase, this paper aims to provide a tool that can be applicable during the construction phase based on the planned earned value and the actual incurred cost on a jobsite level. The critical key to cash ?ow forecasting at this level lies in how to build a realistic cash-out model. Toward the end, this paper adopts moving weights of cost categories in a budget that are variable depending on the progress of construction works. In addition, it addresses time lags in accordance with the contractual payment conditions and credit times given by suppliers or vendors. As for the cash-in model, net planned monthly earned values are simply transferred to the cash-in forecast with a consideration of billing time and retention money. Validation of the proposed model involves applying realistic data from four ongoing projects. Based on the results of comparative analyses, the writers conclude that the proposed model is more accurate and reliable, yet simpler to ?eld engineers who are generally not familiar with certain intricate ?nancial knowledge. DOI: 10.1061/?ASCE?0742-597X?2005?21:4?164? CE Database subject headings: Financial management; Forecasting; Contractors; Cost control; Construction industry.
Introduction
Background Cash is the most important of a construction company’s resources. More construction companies fail due to a lack of liquidity for supporting their daily activities than because of inadequate management of other resources ?Singh and Lakanathan 1992; Navon 1994?. Russell ?1991? pointed out that more than 60% of construction contractor failures are due mainly to economic factors. In an attempt to analyze the real business environment in the construction industry, various forecasting methods have been applied to cash ?ow management. Numerous techniques for cash ?ow forecasting and management differ in their levels of accuracy and detail, the degree of automation in compiling them, and the method to integrate the time and money elements. Some of the techniques are probabilistic, but most of them are deterministic ?Navon 1995?. Reinschmidt and Frank ?1976? proposed a model for cash ?ow forecasting in the early planning stage of a project. This model
PhD, General Manager, G.K CM Team, Daewoo Engineering & Construction Co. Ltd., Seoul, Korea. E-mail: [email protected] 2 Associate Professor, Dept. of Civil and Environmental Engineering, Yonsei Univ., Seoul, Korea ?corresponding author?. E-mail: [email protected] 3 Professor, Dept. of Civil and Environmental Engineering, Univ. of Wisconsin, Madison, WI. E-mail: [email protected] Note. Discussion open until March 1, 2006. Separate discussions must be submitted for individual papers. To extend the closing date by one month, a written request must be ?led with the ASCE Managing Editor. The manuscript for this paper was submitted for review and possible publication on November 2, 2004; approved on March 18, 2005. This paper is part of the Journal of Management in Engineering, Vol. 21, No. 4, October 1, 2005. ©ASCE, ISSN 0742-597X/2005/4-164–172/$25.00.
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integrated schedule and cost items using a simulation model applied to the stochastic duration of the activities. However, it does not consider the time lag’s impact on costs, which is essential in cash ?ow forecasting. The technique proposed by Sears ?1981? is viewed accurately by manually integrating the schedule and cost items, but it requires considerable work and further, it does not consider the time lag between the expenditure and payment of a related cost item. Navon’s model ?1995, 1997? automatically integrates the bill of quantity ?BOQ?, cost estimate, and the schedule associated with a lower level of resources. However, if either the BOQ or the schedule is altered due to various changes, integration is likely to be more complicated and time consuming. Moreover, the main obstacle to automating the integration process is compatibility between cost items of the BOQ and activity elements of schedule. In an attempt to improve the accuracy of a model for forecasting cash ?ow, Ashley and Teicholz ?1977? suggested a cash ?ow forecast based on detailed methods of cost ?ow. They classi?ed the direct cost by a number of cost categories such as labor, materials, and equipment which are speci?ed as percentages of total cost. This approach is very realistic because it considers the nature of the budget and cost. However, each of these cost elements is assumed to be a ?xed percentage of total cost over the project’s duration. Moreover, this model does not consider the effect of time lags on the costs. Also, Gates and Scarpa ?1979? and Peer ?1982? developed cash ?ow models in the conceptual and planning stages using algebraic formulations and polynomial regressions. However, none of these models considered time lags to the costs and earned values. In reality, many factors exist during construction that may affect the cash ?ow including time delays, cost overruns, uncon?rmed earned values, change orders, and changes of cost plan elements ?Bennett and Ormerod 1984?. The key points of cash ?ow forecasts lie in how accurate, ?exible, and comprehensive they are to be calculated and how effectively they consider uncer-
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tain factors such as time delay, cost overrun, variation of cost, and earned value between plan and actual. Of course, it is impossible to ensure that a project will de?nitely be as successful as initially planned. Even though construction is in progress, cash ?ow forecasts cannot be determined precisely. As a result, most models and techniques aforementioned are found to have the following problems: ?1? they are not based on the construction stage, but rather only on the planning or preliminary stages in the project delivery process; ?2? they do not consider time lags for the costs and earned values in forecasting cash ?ow; and ?3? with regard to integration of cost items and activities, they are not compatible with each item and are rather complicated depending on when change factors occur in the subsequent construction stage. Because cash ?ow is a reality, a cash ?ow forecast on a job site should be more precise than those during the preconstruction phases by addressing the uncertainties of the construction business and jobsite procedures. The main objectives of this paper are: ?1? to quantitatively study construction project cash ?ows; ?2? to propose a forecasting cash ?ow model for construction projects with a consideration of both variable cost weights and a time lag; and ?3? to validate the proposed model and suggest guidelines for implementing this cash ?ow forecasting system. In addition, this paper provides implications to management by focusing more on how project managers or ?eld engineers can bene?t by using the presented model. Research Scope and Methodology Among a variety of types of construction projects, this research is focused on bid projects. The proposed model is intended to be applicable to the construction stage in the project delivery process from a general contractor’s viewpoint. Accordingly, the research scope does not include investment projects such as Build– Operate–Transfer or Build–Operate–Own. Moreover, this research is relevant to the viewpoint of cash ?ow management at the project level, and subsequently project evaluation is performed by allowing contractors to re?ect the capital cost ?or socalled, interest cost? whenever negative cash ?ows occur. To achieve its end, methodology should possess several necessary steps. As an initial step to meet the objectives, previous research papers that deal with cash ?ow management are reviewed to investigate problems with existing cash ?ow forecasting models. It should then suggest a new model of cash ?ow forecasting for a jobsite using a new algorithm. A numerical example is prepared to demonstrate and verify the computational aspects of the model. The next step is to perform a simulation using experimental data and to compare the model results to existing models proposed by other researcher. The last step of this research is to validate the model. Although the model is developed to offer a practical guideline to improve forecasting quality in evaluating the cash ?ow on a job site, objectively assessing the validity of the model in a real business scheme is quite dif?cult. Accordingly, a comparative case study methodology is chosen as a proper validation approach to the research features. Four projects in progress, including one building project and three civil projects, with data compiled over a duration of 12 months are identi?ed as the case study materials. Based on the results of comparative analyses, we measure to see if the proposed model
can be more accurate, ?exible, and simpler to typical ?eld engineers on a job site.
Cash Flow to General Contractor
Typical Project Cash Flow Most construction projects are individual pro?t centers, each with its own cash cycle based on the costs of activities related to the project and on payments from a client, both of which are prescribed by a contract. Typical cash ?ow on a construction project consists of: ?1? cash out such as bid costs, preconstruction costs ?engineering, design, mobilization, etc.?, materials and supplies, equipment and equipment rentals, payments of subcontracts, labor and overhead; and ?2? cash in such as billings ?less retentions?, retentions, claims and change orders. The factors that affect cash ?ows are the duration of the project, the retention conditions, the times for receiving payments from the client, credit arrangement with suppliers or vendors, equipment rentals, and times of payments to subcontractors, etc. Cash ?ow at the project level consists of a complete history of all cash disbursement and all earnings received as a result of project execution. Many construction projects have negative net cash ?ows until the very end of construction when the ?nal payment is received or advanced payment is received before starting the project. This is a typical situation when the ?nal payment consists of retention money and the retention percentage is greater than the pro?t percentage of the project. Structure of Construction Budget A budget structure in construction projects is constituted of cost accounts such as bills, sections, items, and resources. A budget is a plan for allocating resources ?Meredith and Mantel 1995?. Hendrickson and Au ?1989? identi?ed the fact that allocation of a cost to the budget may be used to develop the cost function of an operation. The basic idea in this method is that each expenditure item can be assigned to particular categories of operation. Ideally, the allocation item of joint costs should be causally related to the category of basic costs in an allocation process. Generally, a budget structure in construction projects is set up into labor, material, equipment, subcontract, and indirect expenses. If a general contractor performs all the areas of job management on site, expenses for management and overhead cost become a higher burden to the general contractor. To mitigate these costs, general contractors prefer to distribute a role of management to other participants. As an example, if a portion of a subcontract is increased, the general contractor is able to decrease the indirect expenses used for hiring project personnel: the workers, supervisory personnel, and engineers associated with the project. From the general contractor’s viewpoint, labor and equipment costs are uncertain because productivity is extremely volatile and hard to measure. For this reason, general contractors attempt to hire subcontractors to reduce job-management costs and to maximize their pro?t opportunity by concentrating their control ability on variable costs, uncertain time, and strict quality. Typically, the portions of subcontract cost range from 50 to 70%. Material, labor, equipment, and indirect cost are arrayed between 25 and 35%, 5 and 15%, 10 and 25%, and 5 and 15%, respectively ?Oberlender 2000?.
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Jobsite Cash Flow Forecast Model
Cash-Out Model Time Lag The critical key to cash ?ow forecasting at the project level is how to build a cash-out model. All resources to be incurred to costs in a budget have different time lags. They are subject to contracting procedures and a corporation’s payment policy to other organizations. Accordingly, cash-out forecasts set the tone for time lags. Cost categories are classi?ed in order to compile construction resources with similar time lags. Time lag, as used here, is based on contracting payment conditions and credit times given by suppliers or vendors. Ahuja and Walsh ?1983? also insist that there are delays between the dates of costs incurred and the dates of payment due. These delays will vary depending on resource types and credit arrangements as negotiated with subcontractors and suppliers. This approach is maintained by a number of previous researchers ?Peterman 1973; Ashley and Teicholz 1977; McCaffer 1979; Trimble 1982; Kenley and Wilson 1989; Navon 1995; Kaka 1996?. Different cost categories are de?ned for materials, labor, equipment, subcontractors, indirect expenses ?site overhead?, and depreciation items since these cost categories generally have different time lags. If additional cost categories are needed, they can be classi?ed. As mentioned before, since payment conditions of subcontracts are controlled by general contractor policy, it can be noted that the general contractors entail 50–70% certainty in cash ?ow forecasting regarding time lags. The only remaining problems are how to determine time lags of other cost categories and how to plan a budget for each period. Jepson ?1969? suggested that net cash ?ow for individual projects must be derived from “component” curves of in?ow and out?ow pro?les. Fondahl and Bacarreza ?1972? claimed that total costs can be broken down as to category since different cost resources may have different cost curves or different time lags related to their payment. Moving Weights of Cost Categories Ashley and Teicholz ?1977? developed ?ve cost curves for cost categories in their highway construction project. Fondahl and Bacarreza ?1972? also applied three cost curves to their school project ?Curves 1, 2, and 3?. Curve 1 is based on the assumption that the rate of expenditure will be uniform over the project duration. Curve 2 assumes that only 25% of the total cost is incurred during the ?rst half of the project duration and the remaining 75% in the second half. Curve 3 assumes that 75% of the total cost is incurred in the ?rst half of project duration. In their research, only ?eld overhead and home of?ce overhead costs were analogous to Curve 1, which implies that only these costs were assumed to be incurred at a uniform rate over the project duration. In other words, all cost categories except ?eld overhead and home of?ce overhead were not incurred at a uniform rate over the project lifetime. Unless the curves of all cost categories are uniform, the relative weights of the different cost categories should be changed whenever costs are incurred over the project duration. If weights of cost categories are uniform over the project duration, curves of all categories should represent straight lines. The concepts of the moving weights method and ?xed weights method are illustrated in Fig. 1.
Fig. 1. Comparison of weights of costs during construction period
For that reason, whenever costs are incurred in a periodic month, weights of cost categories relative to the remaining budget are changed, even though neither the overall budget ?the forecast total cost? nor the planning for execution is changed. Moreover, if a change of project amount or project duration occurred due to a change order or a change of contract conditions, weights of cost categories should also be adjusted ?Park 2001?. Consequently, this implies that the next weight of a cost category to be applied will be set in accordance with the cumulative actual cost and the remaining budget. Thus, “the moving weights method” continuously changes over the project duration to pertain to the remaining budget. Applying moving weights of cost categories to the remaining budget in a month ?time series? reduces the uncertainty of forecasting cash out for the remaining duration of the project. This characteristic of a budget during the construction period is illustrated in Fig. 2. Cash-In Model Billing Time Generally, earned values will be received on a monthly basis or based on billing terms, but planning of earned values on a jobsite is established by a monthly amount. Earned value planning is the basis for estimated cash-in values in actual cash ?ow analysis. Net planned monthly earned values are simply transferred to the cash-in forecast, to be applied there with appropriate time lags. The billing period, the time between the dates of bill submittal and the progress payment receipt, is stipulated in the contract. If a payment delay occurs due to the owner’s circumstances, the billing time of cash in can be adjusted in this model. In practice, billing terms in the contract should provide for a billing schedule for owner and contractor, but those terms can be applied variously depending on the owner’s ?nancing situation.
Fig. 2. Characteristic of budget during construction period
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Retention Money Cash-in planning should consider the effects of retention money and the billing period on earned values. Retention money is based on a percentage of retention stipulated in the contract. A cumulative cash-in curve is obtained from the cumulative earned value curve by applying a retention rate and billing period. Generally, contractors can improve cash ?ow by providing percent retention schedules in contracts with subcontractors. Then, the retention money is released when construction is completed and accepted. If cash in is properly planned and manipulated by a model, it will supply the funds necessary to meet the cash requirements of the project without borrowing from other organizations. Mathematical Algorithm of Model Cash Out The model algorithm for cash out can be represented by equations. In cash out, the cost categories in an initial budget can be classi?ed depending on the time lags of all resources in the budget. After that, the following equation is applied: initial weight ?wi? = Ci ÷ TB ?1?
where i = cost categories; Ci = budget of individual cost categories; and TB= initial total budget ?total costs?. Whenever deviation between actual and planned data occurs, an adjustment of weight is calculated and applied to the next cash planning. Since actual cost in accordance with initial weight of each cost category in each month is not incurred, actual cost and actual earned value should be re?ected in the next weights of individual categories. The weight is called the “moving weight” in this research. Therefore, the next moving weight to be applied is moving weight ??wi? = ?Ci ÷ ?TB ?2?
Fig. 3. Process of model
TCt = actual cumulative total cost; and FCt+1,i = actual cash-out ?ow. Cash In Earned value is converted to cash in by deducting retention and applying billing time. This model considers that most contractors withhold retention from subcontractors at the same rate they are withheld by the owner. Therefore cash in should consider two kinds of retention money: contractors’ retention and subcontractors’ retention. Hence, the cash in is calculated as follows: CIt = Vt ? ?1 ? rc? + rs ? St ?8?
where ?Ci = remaining budget of individual cost category and ?TB= remaining total budget. From Eqs. ?1? and ?2?, the constraints on weights of the individual cost categories can be represented by the following equations: ?wi = 1 or ??wi = 1 ?4? ?3?
where i = individual cost categories. As a result, equations for the moving weights cash-out model are as follows. In terms of this model, the algorithm can be continuously updated to the weight to be applied in each month over the project duration Ft+1,i = wt+1,i ? Ct+1 wt+1,i = Ct,i ? ACt,i TBt ? TCt ?5? ?6? ?7?
where CIt = cash in at the time t; Vt = earned value at the time t; rc = contractual retention rate; rs = subcontractual retention rate; and St = subcontract cost at the time t. Depending on the contractual agreement, release of retention is prescribed in two ways: ?rst at the completion of the contract and second, at the end of the maintenance period. The model is simulated by entering the ?gures of release of retention by contractors whenever the subcontract ends. Model Process Fig. 3 illustrates an integrated process of model to cash ?ow forecasting. It consists of three steps designed for general contractors on a job site level to evaluate cash ?ow. The ?rst step requires input data for evaluating each individual project, such as planned earned values and budget ?cost? to each month, cost categories, weights, and time lags. If more cost categories due to different time lags are required, the users can classify separate cost categories. The second step updates new weights to cost categories re?ected on actual cost. Also, forecast cash ?ow such as cash in, cash out, cumulative cash ?ow, and capital cost are automatically
? ACi = ? ? FCt+1,i
where Ft+1,i = forecast of individual cost categories of time series in period t + 1; Ct+1 = planned costs of time series in period t + 1; wt+1,i = weights of cost categories of time series in period t + 1; AC,i = actual cumulative cost of individual cost categories;
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Table 1. Example of Cash Forecasting at Start of Projecta Time period ?days? 0 30 61 91 122 152 183 213 244 274 305 335 365 396 426 457 487 518 Sum Planed earned value 1,097 1,159 1,104 1,106 982 627 716 997 1,183 1,302 1,173 1,083 — — — — — 12,529 Material Time lag ?days? 150 Planned budget 4,033.80 Initial weight ?%? 32.71% a Unit= 1,000 US dollars. Planned budget 1,072 1,130 1,095 1,084 975 620 709 972 1,176 1,288 1,152 1,059 — — — — — 12,332 Labor 0 456.28 3.70% Actual value — — — — — — — — — — — — — — — — — — Depreciation 0 567.27 4.60% Actual cost — — — — — — — — — — — — — — — — — — Equipment 0 27.13 0.22% Cash in — — — — 5,448.00 — — — 3,523.00 — — — — — — — — 8,971.00 Main materials 0 0 0.00% Cash out 83.08 87.58 84.86 84.01 75.56 987.66 1,045.39 1,035.10 1,041.27 954.41 632.71 703.51 851.96 1,030.76 1,128.93 1,009.73 928.21 11,764.73 Sub-Con 150 6,775.20 54.94% Cumulative balance ?132.39 ?271.95 ?407.18 ?541.05 4,786.53 3,770.36 2,692.35 1,612.54 4,040.18 3,026.52 2,340.82 1,588.60 736.64 ?294.13 ?1,423.06 ?2,432.79 ?3,361.00 — Sum Interest ?10%? 0.00 ?1.09 ?2.24 ?3.35 ?4.45 39.34 30.99 22.13 13.25 33.21 24.88 19.24 13.06 6.05 ?2.42 ?11.70 ?20.00 ?27.62 129.30 Billing time 120
Balance 0.00 ?132.39 ?139.56 ?135.23 ?133.87 5,327.59 ?1,016.18 ?1,078.01 ?1,079.81 2,427.64 ?1,013.66 ?685.70 ?752.23 ?851.96 ?1,030.76 ?1,128.93 ?1,009.73 ?928.21 ?3,361.00 Expense 0 472.32 3.83%
Depreciation 49.31 51.98 50.37 49.86 44.85 28.52 32.61 44.71 54.10 59.25 52.99 48.71 — — — — — 567.27 Retainage 0 0%
12,332 100.00%
calculated. This stage is based on moving weights of each classi?ed cost category in each month. Moving weight is that weight to be applied to the next month that is adjusted and calculated by deducting the actual cost from the initial budget to the individual classi?ed cost category in each month. Therefore, a weight of each budget of each individual cost category to the remaining budget is to be changed every month. The ?nal step provides feedback to estimate the new planned earned values and budget for each month. Whenever deviations between planned and actual costs and earned values occur, they are automatically distributed over the remaining duration if needed. If deviations between them are considerably more or less than expected, the project manager must modify the initial planning to forecast cash ?ow. As a basis for applying the proposed model, this paper sets up basic assumptions: ?1? in the initial time period, the planned earned value to the contract amount and planned cost to the budget are not automatically generated each month. Instead they are made independently by engineers on the jobsite by their own method of planning. ?2? Time lags of cost categories are based on corporate historical data and company policy. ?3? Cost categories classi?ed at the start of a project have to continuously be used in order to maintain the degree of accuracy in moving weight over the project duration. ?4? This model is used to forecast cash ?ow values at the close of each month ?last day of the month?. ?5? Depreciation of company owned equipment is included in actual cash transfer incurred cost in order to show cash ?ow at the project level. ?6? Home of?ce overhead is not considered in this
model since that is not generally considered as a job or project cost. That is incurred at the company level and accordingly may be billed directly on a jobsite. Illustrative Example To illustrate the new methodology proposed, we have conducted a simple case study. The illustrative case is composed with the following ?gures: project duration is 12 months, contract amount is US$12,529,000, and budget is US$12,332,000. Input variables for the case are: ?1? planned earned values ?PE?, planned budget ?PB?, actual earned value ?AE?, and actual cost ?AC? at each month; ?2? cost categories classi?ed based on contract procedure; ?3? weight percentage and credit time of each cost category; ?4? billing time and percentage of retention to be stipulated in contract; and ?5? percentage of interest to be applied by corporate policy or decision. According to investigations by Singh and Lakanathan ?1992?, the application of “S curves” for cash ?ow projections can achieve an accuracy of approximately 88–97%. Subsequently, input data at each month are based on S curves. The basic assumptions applied to the case study are: ?1? changes of AC and AE against PE and PB at each month are addressed, but overrun of budget and delay of duration are not considered; ?2? cost categories depending on time lags are simply classi?ed as labor ?0 days?, materials ?150 days?, rent equipment ?0 days?, depreciation of owned equipment ?0 days?, subcontract ?150 days?, and ?eld expense ?0 days?; ?3? billing time to earned value is 120 days and percent of retainage is 0% of earned value
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Table 2. Example of Updated Cash Forecasting after “1” Montha Time period ?days? 0 30 61 91 122 152 183 213 244 274 305 335 365 396 426 457 487 518 Sum Planned budget Actual cost after 1 month Actual weight ?%? Remaining budget New moving weight ?%? a Unit= 1,000 US dollars. Planed value 1,097 1,159 1,104 1,106 982 627 716 997 1,183 1,302 1,173 1,083 — — — — — 12,529 Material 4,033.80 72.97 10.01 3,960.82 34.14 Planned budget 1,072 1,130 1,095 1,084 975 620 709 972 1,176 1,288 1,152 1,059 — — — — — 12,332 Actual value 1,090 — — — — — — — — — — — — — — — — 1,090 Actual cost 729 — — — — — — — — — — — — — — — — 729 Cash in — — — — 5,441.00 — — — 3,523.00 — — — — — — — — 8,964.00 Cash out 87.48 84.56 81.94 81.12 72.96 654.38 1,046.52 1,035.43 1,041.02 953.57 631.29 702.58 854.55 1,033.90 1,132.37 1,012.80 931.04 11,437.51 Balance 0 ?121.01 ?136.54 ?132.31 ?130.98 5,323.19 ?682.90 ?1,079.13 ?1,080.14 2,427.88 ?1,012.82 ?684.28 ?751.29 ?854.55 ?1,033.90 ?1,132.37 ?1,012.80 ?931.04 ?3,025.00 Expense 472.32 76.91 10.55 395.41 3.41 Cumulative Interest balance ?10%? Depreciation ?121.01 ?257.55 ?389.86 ?520.84 4,802.35 4,119.45 3,040.32 1,960.18 4,388.07 3,375.25 2,690.96 1,939.67 1,085.12 51.22 ?1,081.15 ?2,093.96 ?3,025.00 — Sum 12,332 729 100.00 11,603 100.00 0.00 ?0.99 ?2.12 ?3.20 ?4.28 39.47 33.86 24.99 16.11 36.07 27.74 22.12 15.94 8.92 0.42 ?8.89 ?17.21 ?24.86 164.08 33.53 51.98 50.37 49.86 44.85 28.52 32.61 44.71 54.10 59.25 52.99 48.71 — — — — — 551.49 Retainage 0%
Labor Depreciation Equipment Main materials Sub-Con 456.28 567.27 27.13 0 6,775.20 8.97 33.53 1.60 0 535.01 1.23 4.60 0.22 0.00 73.39 447.32 533.74 25.53 0 6,240.19 3.86 4.60 0.22 0.00 53.78
each time; ?4? in consideration of different time lags of cost categories, cash ?ow is calculated each 30 days; and ?5? whenever negative cumulative cash ?ow occurs, internal interest ?10%? is charged. Table 1 represents the example of cash forecasting at the beginning of project ?“0” month? made in accordance with the basic conditions and aforementioned algorithm with considerations of time lags and moving weights. As an example, in time period of 30 days, cash out ?US$83.03? is gaged only considering cost categories that have no time lags ?planned budget ?US$ 1,072? ? sum of initial weights of labor, equipment, and expenses ?7.75%??. It expects that the ?nal cash balance at completion will be negative—around US$3,316,000. As the ?rst month passes and
Table 3. Project Overview Project name Project A: apartment Project overview • 8–25 story ?seven building apartment? • 490 unit • Area: 279 ha • Width: 20 M ?four lane? • Earth work: 19 million m3 • Joint venture project • Total length: 11.432 km • Bridge: 12 ?4,867 m? • Tunnel: 1 ?545 m? • Stations: three stop • Joint venture project • Treatment capacity: 80,830 t / day
actual cost ?US$729? is incurred, we can update new weights to cost categories. The proposed model employs weights that are updated based on weight percentage of cost categories to the remaining budget each month over the duration, while the traditional approach is designed based only on weights to the initial total budget. For example, new moving weight of the material cost category ?34.14%? can be updated by dividing the remaining budget of material ?US$3,960.82? to the remaining total budget ?US$11,603?. Table 2 shows an example of the updated cash balance ?negative US$3,025,000? in accordance with these revised moving weights.
Validation of Model
Model validation includes measuring the accuracy of a model in describing the actual conditions of a problem to solve and in
Table 4. Project Basic Dataa Items Project A Project B 94,465 49.3 82,946 8.95 21.28 1.33 63.47 0.36 4.61 Project C 79,632 96 72,989 2.20 15.30 5.40 61.70 0 15.40 Project D 51,257 63 42,221 4.21 3.08 0 83.58 0 9.13
Project B: industrial complex
Project C: railway
Project D: sewage treatment
Contract amount 46,648 Duration ?months? 33 Total budget ?dollars? 36,486 Labor ?%? 1.31 Material ?%? 33.73 Equipment ?%? 0.22 Subcontract ?%? 49.14 Depreciation ?%? 4.6 Expense ?%? 11.0 a Unit= 1,000 US dollars.
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Table 5. Time Lags for Each Cost Categorya Project A
b
Table 6. Billing Time and Delay in Payment for Each Projecta Project C
b
Project B
b
Project D
b
Project A
Project B
Project C
Project D 60 30
Labor 0 0 0 0 Material 150 120 90 120 0b 0b 0b 0b Equipmentc Subcontract 150 120 90 90 0b — — Depreciation 0b 0b 0b 0b Expense 0b a Unit= days. b The last day of the month when cost is incurred. c Equipment cost is charged to expense of cash out in accounting perspective.
Billing time 120 30 90 0 0 150 Delay in paymentb a Unit= days. b Delay payment is total delay time from the client.
evaluating the usefulness of the model in terms of its objectives to a larger case with similar problem contexts. Stated earlier, a comparative case study methodology was used to validate whether the model meets its development objective. Validation Procedures To verify the model, we performed simulation using empirical data from actual projects in progress. Simulation results based on the proposed model and existing model are compared. A simulation template is implemented in a common spreadsheet package—Microsoft Excel™—for the simulation experiments. Considering different time lags of cost categories, cash ?ow is calculated in monthly increments. The ?xed weights method ?FWM?—the current approach—applies ?xed weights to cost categories over project duration, whereas the moving weights method ?MWM?—the new proposed model—applies different weights each month using a new algorithm. The results of these two models were then compared to show the accuracy and consistency of the model. The stepwise procedures for the validation of the proposed model are as follows: 1. Four actual projects in progress including one building and three civil infrastructure projects, with data compiled over a duration of 12 months ?see Tables 3 and 4?; 2. To obtain forecast cash ?ow data, MWM and FWM are applied to actual data through the simulation; and 3. To compare the accuracy of MWM to FWM, the results of the simulation are analyzed. To simulate the dynamic cash ?ow forecasting, simulation experiments were conducted 12 times from the ?rst to the twelfth month by each method for individual projects and compared by the two methods: MWM and FWM. In addition, 2 types of simuTable 7. Example of Comparative Analysis for Project Aa Actual cash ?ow Time period ?days? Cash in Cash out Cash ?ow
lations are performed on each project in order to compare the accuracy of forecasting models. Subsequently, 48 simulations per each project and a total of 192 simulations were performed for four projects. In the comparative analysis the results of forecasting are applied to cash ?ow each month instead of the cumulative cash ?ow forecasting applied previously in experiments since previous cash ?ow affects subsequent cash ?ow. To compare the accuracy of two models, MWM and FWM, the simulation is performed in accordance with the following two types: ?1? Type 1—planned data and actual data are identical to each other. This type is used to determine the reliability of the model and compare the two methods under ideal conditions since planned data are one of the most critical variables in this forecasting cash ?ow model, and ?2? Type 2—planned data and actual data are different as reported by the jobsite for 12 months. In this case, the uncertainty of the construction job site is involved and the effect of planned data on the forecasting cash ?ow is considered. Finally, the following data are required for comparative analysis of the four projects in progress: 1. Time lags of billing time and individual cost categories ?see Tables 5 and 6?; 2. Total contract amount and total budget re?ect contract amount and budget changed during the construction stage; 3. Monthly cost planning data and earned value planning data; 4. Weights of cost categories in the budget; 5. Retention rate and capital cost rate; and 6. Actual cash ?ow such as cash in and cash out for each month. Validation Results Measurement of Accuracy Mean absolute deviation ?MAD? was used to measure the error for each month’s forecasted cash ?ow by means of the two models: MWM and FWM. The MAD is a commonly used measure
Forecasting cash ?ow MWM ?678.018 ?875.830 ?1,107.974 3,567.408 ?919.695 ?662.458 ?1,041.238 FWM ?707.169 ?879.505 ?1,112.959 3,523.455 ?943.710 ?697.197 ?1,083.378
Mean absolute deviation MWM 57.985 24.202 52.871 56.577 109.678 30.388 132.229 66.276 FWM 28.834 20.527 47.886 100.530 133.694 65.128 174.369 81.567
183 — 736.003 ?736.003 213 — 900.032 ?900.032 244 — 1,160.845 ?1,160.845 274 4,357.000 733.015 3,623.985 305 — 810.016 ?810.016 335 — 632.069 ?632.069 365 — 909.009 ?909.009 Average MWM?moving weights method; and FWM??xed weights method. a Simulation 1—after 1 month, Type 1.
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Table 8. Comparison of Mean Absolute Deviation ?MAD? in Moving Weight Method ?MWM? and Fixed Weights Method ?FWM? ?Type 1?a MAD MWM ?A? FWM ?B? B?A MAPE
Table 10. Reliability of Moving Weights Method ?Type 1?a Project Mean absolute deviation ?A? Contract amount ?B?b ?A / B? ? 100 ?%? 0.31 0.23 0.60 0.38 0.38
Project A 41.207b 68.010 26.803 65.04%c Project B 62.395 93.356 30.962 49.62% Project C 24.106 24.173 0.067 0.28% Project D 27.781 31.739 3.957 14.24% Average 32.30% a Unit= 1,000 US dollars. b It is calculated by Eq. ?9? through the 12 times of simulations from the 1st month to the 12th month ?refer to Table 7 for the case of simulation 1?. c MAPE= 26.803÷ 41.207= 65.04%.
A 41.207 13,489 B 62.395 27,464 C 27.094 4,539 D 27.781 7,310 Average a Unit?1,000 US dollars. b Contract amount means total earned values for 12 months.
that forecasts accuracy as the degree of variation by the following equation. This measure is simply the measure of the absolute deviations for all forecasts 1 MAD = ?Acfti ? Fcfti? n ?9?
In the same way, the average MAD can be calculated through the 12 times of simulations from the ?rst month to the twelfth month. Based on the results of MAD for simulations, MWM is more accurate than FWM. In the Type 1 simulation, the accuracy is 0.28–65.04%, with an average of 32.30% higher than FWM in the ideal condition, where planning is well established as re?ected on the construction jobsite, and continuously updated ?see Table 8?. In the case of Type 2, the accuracy is 1.14–4.54%, an average of 2.13% higher ?see Table 9?. As a result, the degree of accuracy of MWM is an average of 17.21% higher than FWM. Reliability Kenley and Wilson ?1986? and Kaka and Price ?1991? suggested that the error range of forecasting in the construction industry is within ±3% of the contract amount. This is considered an acceptable limit and demonstrates the reliability of the proposed model. The error range of the forecasting is 0.23–0.6%, with an average of 0.38% for four projects in Type 1, and 0.82–2.78%, with an average of 1.79% for four projects in Type 2 ?see Tables 10 and 11?. Despite unavoidable errors in the planning data, the result is thought of as being reasonably attained by applying the model for forecasting. Consequently, the reliability of the MWM model is acceptable and well demonstrated based on simulation. Practical Implications to Industry Financial management has long been recognized as an important management tool. A company can survive a transitional period without a pro?t, or even with a loss; however, it may fail due to lack of cash during the operation even if it has a good ?nancial statement. In the viewpoint of corporate, cash ?ow forecasts should be made at all stages of the project from the planning stage to the operation and maintenance stage of a project. However, cash ?ow forecasting and management are a dynamic process. Deviations of all projects at the corporate level may signi?cantly affect the ?rm’s ?nancial status. Moreover, inadequate cash ?ow forecasts to a certain project may drive a corporate into a crisis of ?nancial situations.
Table 11. Reliability of Moving Weights Method ?Type 2?a Project Mean absolute deviation ?A? Contract amount ?B?b ?A / B? ? 100 ?%? 1.23 2.78 0.82 2.34 1.79
where Acfti = actual value of cash ?ow, Fcfti = forecasting value of cash ?ow at t month by the ith simulations, respectively; t = 1 – 12; i = 1 – 12; and n = number of observations. In comparative analysis, the mean absolute percent error ?MAPE? method was estimated for comparing errors. It can be achieved by dividing the difference between MAD of MWM and FWM by MAD of MWM. This fraction represents how large the error of FWM is as compared to MWM. All absolute deviation data between MWM and FWM are summed and divided by the number of observations, by the following equations: average MAPE = 1 n
t=12 i=12
? ? t=1 i=1
mMADti ? fMADii 100?% ? ?10? mMADti
where mMADti = MAD value of MWM at t month by ith simulation; fMADti = MAD value of FWM at t month by ith simulation; and n = number of observations. The cash ?ow data consisted of four detailed real projects in progress, together with their associated estimated monthly values. The data were obtained from “D” Construction and Engineering Company in Korea. During the data collecting, adjustment of budget and contract amount including in?ation was applied to speci?c projects. Table 7 shows the actual data to achieve the MAD for the speci?c case of simulation 1, which are estimated by Eq. ?9? after the ?rst month passes from the start of the project.
Table 9. Comparison of Mean Absolute Deviation ?MAD? in Moving Weight Method ?MWM? and Fixed Weights Method ?FWM? ?Type 2? MAD Mean absolute percent error ?%? 2.14 0.68 1.14 4.54 2.13
MWM ?A? Project A Project B Project C Project D Average 153.910 867.361 37.960 222.364 —
FWM ?B? 157.198 873.238 38.395 232.462 —
B?A 3.287 5.877 0.430 10.099 —
A 153.910 12,529 B 867.361 31,158 C 37.045 4,516 D 222.364 9,500 Average a Unit ?1,000 US dollars. b Contract amount means total earned values for 12 months.
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Considering the real business world in construction industry, various forecasting methods may be applied to cash ?ow. Some judgment on a jobsite is needed in the case of forecasting cash ?ow with respect to complexity and unexpected situations in construction industry. However, if jobsite engineers or project managers rely only on judgment based on their experience without the use of any mathematical forecasting techniques, they may not make a good decision to forecast cash ?ow. Essentially, a good forecasting technique needs to include both a historical trendbased data supported method and competent judgments based on construction experience and knowledge. In this respect, the proposed model suggest a practical and easy approach for jobsite engineers and project managers who are generally not familiar with ?nance knowledge of forecasting cash ?ow using the regular reports. This model can be applied as part of a project evaluation process and continuously updated to show deviations between plan and actual data through changes or information from the jobsite. In addition, the fast and simple forecasting cash ?ow allows ?eld engineers or project managers to support and to save time for decision making of strategy of cash management to the corporation and projects.
actual data; ?2? developing cash ?ow forecasts at the planning stage using the relationship between cumulative earned value and cost categories; and ?nally ?3? implementing a system at the corporate level to monitor cash ?ow in a proactive and timely manner.
References
Ahuja, H. N., and Walsh, M. A.. ?1983?. Successful methods in cost engineering, Wiley, New York. Ashley, D. B., and Teicholz, P. M. ?1977?. “Pre-estimate cash ?ow analysis.” J. Constr. Div., Am. Soc. Civ. Eng., 103?3?, 369–379. Bennett, J., and Ormerod, R. N. ?1984?. “Simulation applied to construction projects.” Constr. Manage. Econom., 2, 225–263. Fondahl, J. W., and Bacarreza, R. R. ?1972?. “Construction contract markup related to forecasted cash ?ow.” Technical Rep. Prepared for Construction Industry Institute, Stanford Univ., Stanford, Calif. Gates, M., and Scarpa, A. ?1979?. “Preliminary cumulative cash ?ow analysis.” Cost Eng., 21?6?, 243–249. Hendrickson, C., and Au, T. ?1989?. Project management for construction: Fundamental concepts for owner, engineer, architects, and builders, Prentice Hall, Englewood Cliffs, N.J. Jepson, W. B. ?1969?. “Financial control of construction and reducing the element of risk.” Contact. J., April, 862–864. Kaka, A. P. ?1996?. “Towards more ?exible and accurate cash ?ow.” Constr. Manage. Econom., 14, 35–44. Kaka, A. P., and Price, A. D. F. ?1991?. “Net cash ?ow models: Are they reliable?” Constr. Manage. Econom., 9, 291–308. Kenley, R., and Wilson, O. D. ?1986?. “A construction project cash ?ow model—An idiographic approach.” Constr. Manage. Econom., 4, 213–232. Kenley, R., and Wilson, O. D. ?1989?. “A construction project net cash ?ow model.” Constr. Manage. Econom., 7, 3–18. McCaffer, R. ?1979?. “Cash ?ow forecasting.” Quantity Surveying, August, 22–26. Meredith, J. R., and Mantel, S. J., Jr. ?1995?. Project management—A management approach, 3rd Ed., J Wiley, New York. Navon, R. ?1994?. “Company-level cash-?ow management.” J. Constr. Eng. Manage., 122?1?, 22–29. Navon, R. ?1995?. “Resource-based model for automatic cash-?ow forecasting.” Constr. Manage. Econom., 13, 501–510. Navon, R. ?1997?. “Cash-?ow forecasting and management.” Proc., Construction Congress, ASCE, New York, 1056–1063. Oberlender, G. D. ?2000?. Project management for engineering and construction, 2nd Ed., McGraw–Hill, New York. Park, H. K. ?2001?. “Cash ?ow forecasting model using moving weights of cost categories for general contractors on jobsite.” PhD dissertation, Univ. of Wisconsin, Madison, Wis. Peer, S. ?1982?. “Application of cost-?ow forecasting models.” J. Constr. Div., Am. Soc. Civ. Eng., 108?2?, 226–232. Peterman, G. G. ?1973?. “A way to forecast cash ?ow.” World Constr., October, 17–22. Reinschmidt, K. F., and Frank, W. E. ?1976?. “Construction cash ?ow management system.” J. Constr. Div., Am. Soc. Civ. Eng., 102?4?, 615–627. Russell, J. S. ?1991?. “Contractor failure: Analysis.” J. Perform. Constr. Facil., 5?2?, 163–180. Sears, G. A. ?1981?. “CPM/COST: An integrated approach.” J. Constr. Div., Am. Soc. Civ. Eng., 107?2?, 227–238. Singh, S., and Lakanathan, G. ?1992?. “Computer-based cash ?ow model.” Proc., 36th Annual Trans., AM. Assoc. of Cost Engineers, AACE, R.5.1–R.5.14. Trimble, E. G. ?1982?. “Micro computers in construction management.” Building Technology Management, 2?2?, 11–13.
Conclusions
A simple cash ?ow forecasting model ?MWM? was developed to assist general contractors on jobsites during the construction phase. The model was based on the general procedure of construction jobsites and the nature of a general contractor’s budget. The model included new methodology that was not addressed by previous researchers. A comparative case study methodology was chosen as a proper validation approach to evaluate the bene?ts of the proposed model. Four real projects were identi?ed as the case study materials and the validity of the model was tested by actual data from these projects in progress. The overall validation procedures were derived from a series of simulations by comparing the results of the proposed model with other models suggested by previous researchers. Ultimately, the cash ?ow forecasting model was demonstrated to be a simple, accurate, and reliable forecasting tool for general contractors at the construction stage in comparison with FWM. During the case studies, two issues were recognized as requiring more research. The ?rst is that the model is dependent on the planning of cost and earned value. If planning of cost and earned value are not accurate, the forecasted cash ?ow would not be accurate. The second issue is, similar to the ?rst, how to obtain reliable variables at the jobsite level such as the release of retention money—because it can be applied depending on the duration of a subcontract. Despite several limitations, the proposed model presents a practical and easy approach for jobsite engineers and project managers who are not familiar with extensive ?nancial knowledge, just using regular reports without separate information at the jobsite. In addition, this model can be applied as part of a project evaluation process considering internal interest ?capital cost? at the corporate level. The model can be continuously updated to show deviations between planned and actual data through information changes from the jobsite. Encouraged by the results of current research, future procedural research will concentrate on: ?1? analyzing the impact on difference between planned data and
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doc_992771348.pdf
Cash flow forecasting or Cash Flow management is a key aspect of financial management of a business, planning its future cash requirements to avoid a crisis of liquidity.
Cash Flow Forecasting Model for General Contractors Using Moving Weights of Cost Categories
Hyung K. Park1; Seung H. Han2; and Jeffrey S. Russell3
Abstract: This research introduces the development of a project-level cash ?ow forecasting model from a general contractor’s viewpoint. While most previous models have been proposed to assist contractors in forecasting cash ?ow in the early stage of pretendering or the planning phase, this paper aims to provide a tool that can be applicable during the construction phase based on the planned earned value and the actual incurred cost on a jobsite level. The critical key to cash ?ow forecasting at this level lies in how to build a realistic cash-out model. Toward the end, this paper adopts moving weights of cost categories in a budget that are variable depending on the progress of construction works. In addition, it addresses time lags in accordance with the contractual payment conditions and credit times given by suppliers or vendors. As for the cash-in model, net planned monthly earned values are simply transferred to the cash-in forecast with a consideration of billing time and retention money. Validation of the proposed model involves applying realistic data from four ongoing projects. Based on the results of comparative analyses, the writers conclude that the proposed model is more accurate and reliable, yet simpler to ?eld engineers who are generally not familiar with certain intricate ?nancial knowledge. DOI: 10.1061/?ASCE?0742-597X?2005?21:4?164? CE Database subject headings: Financial management; Forecasting; Contractors; Cost control; Construction industry.
Introduction
Background Cash is the most important of a construction company’s resources. More construction companies fail due to a lack of liquidity for supporting their daily activities than because of inadequate management of other resources ?Singh and Lakanathan 1992; Navon 1994?. Russell ?1991? pointed out that more than 60% of construction contractor failures are due mainly to economic factors. In an attempt to analyze the real business environment in the construction industry, various forecasting methods have been applied to cash ?ow management. Numerous techniques for cash ?ow forecasting and management differ in their levels of accuracy and detail, the degree of automation in compiling them, and the method to integrate the time and money elements. Some of the techniques are probabilistic, but most of them are deterministic ?Navon 1995?. Reinschmidt and Frank ?1976? proposed a model for cash ?ow forecasting in the early planning stage of a project. This model
PhD, General Manager, G.K CM Team, Daewoo Engineering & Construction Co. Ltd., Seoul, Korea. E-mail: [email protected] 2 Associate Professor, Dept. of Civil and Environmental Engineering, Yonsei Univ., Seoul, Korea ?corresponding author?. E-mail: [email protected] 3 Professor, Dept. of Civil and Environmental Engineering, Univ. of Wisconsin, Madison, WI. E-mail: [email protected] Note. Discussion open until March 1, 2006. Separate discussions must be submitted for individual papers. To extend the closing date by one month, a written request must be ?led with the ASCE Managing Editor. The manuscript for this paper was submitted for review and possible publication on November 2, 2004; approved on March 18, 2005. This paper is part of the Journal of Management in Engineering, Vol. 21, No. 4, October 1, 2005. ©ASCE, ISSN 0742-597X/2005/4-164–172/$25.00.
1
integrated schedule and cost items using a simulation model applied to the stochastic duration of the activities. However, it does not consider the time lag’s impact on costs, which is essential in cash ?ow forecasting. The technique proposed by Sears ?1981? is viewed accurately by manually integrating the schedule and cost items, but it requires considerable work and further, it does not consider the time lag between the expenditure and payment of a related cost item. Navon’s model ?1995, 1997? automatically integrates the bill of quantity ?BOQ?, cost estimate, and the schedule associated with a lower level of resources. However, if either the BOQ or the schedule is altered due to various changes, integration is likely to be more complicated and time consuming. Moreover, the main obstacle to automating the integration process is compatibility between cost items of the BOQ and activity elements of schedule. In an attempt to improve the accuracy of a model for forecasting cash ?ow, Ashley and Teicholz ?1977? suggested a cash ?ow forecast based on detailed methods of cost ?ow. They classi?ed the direct cost by a number of cost categories such as labor, materials, and equipment which are speci?ed as percentages of total cost. This approach is very realistic because it considers the nature of the budget and cost. However, each of these cost elements is assumed to be a ?xed percentage of total cost over the project’s duration. Moreover, this model does not consider the effect of time lags on the costs. Also, Gates and Scarpa ?1979? and Peer ?1982? developed cash ?ow models in the conceptual and planning stages using algebraic formulations and polynomial regressions. However, none of these models considered time lags to the costs and earned values. In reality, many factors exist during construction that may affect the cash ?ow including time delays, cost overruns, uncon?rmed earned values, change orders, and changes of cost plan elements ?Bennett and Ormerod 1984?. The key points of cash ?ow forecasts lie in how accurate, ?exible, and comprehensive they are to be calculated and how effectively they consider uncer-
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tain factors such as time delay, cost overrun, variation of cost, and earned value between plan and actual. Of course, it is impossible to ensure that a project will de?nitely be as successful as initially planned. Even though construction is in progress, cash ?ow forecasts cannot be determined precisely. As a result, most models and techniques aforementioned are found to have the following problems: ?1? they are not based on the construction stage, but rather only on the planning or preliminary stages in the project delivery process; ?2? they do not consider time lags for the costs and earned values in forecasting cash ?ow; and ?3? with regard to integration of cost items and activities, they are not compatible with each item and are rather complicated depending on when change factors occur in the subsequent construction stage. Because cash ?ow is a reality, a cash ?ow forecast on a job site should be more precise than those during the preconstruction phases by addressing the uncertainties of the construction business and jobsite procedures. The main objectives of this paper are: ?1? to quantitatively study construction project cash ?ows; ?2? to propose a forecasting cash ?ow model for construction projects with a consideration of both variable cost weights and a time lag; and ?3? to validate the proposed model and suggest guidelines for implementing this cash ?ow forecasting system. In addition, this paper provides implications to management by focusing more on how project managers or ?eld engineers can bene?t by using the presented model. Research Scope and Methodology Among a variety of types of construction projects, this research is focused on bid projects. The proposed model is intended to be applicable to the construction stage in the project delivery process from a general contractor’s viewpoint. Accordingly, the research scope does not include investment projects such as Build– Operate–Transfer or Build–Operate–Own. Moreover, this research is relevant to the viewpoint of cash ?ow management at the project level, and subsequently project evaluation is performed by allowing contractors to re?ect the capital cost ?or socalled, interest cost? whenever negative cash ?ows occur. To achieve its end, methodology should possess several necessary steps. As an initial step to meet the objectives, previous research papers that deal with cash ?ow management are reviewed to investigate problems with existing cash ?ow forecasting models. It should then suggest a new model of cash ?ow forecasting for a jobsite using a new algorithm. A numerical example is prepared to demonstrate and verify the computational aspects of the model. The next step is to perform a simulation using experimental data and to compare the model results to existing models proposed by other researcher. The last step of this research is to validate the model. Although the model is developed to offer a practical guideline to improve forecasting quality in evaluating the cash ?ow on a job site, objectively assessing the validity of the model in a real business scheme is quite dif?cult. Accordingly, a comparative case study methodology is chosen as a proper validation approach to the research features. Four projects in progress, including one building project and three civil projects, with data compiled over a duration of 12 months are identi?ed as the case study materials. Based on the results of comparative analyses, we measure to see if the proposed model
can be more accurate, ?exible, and simpler to typical ?eld engineers on a job site.
Cash Flow to General Contractor
Typical Project Cash Flow Most construction projects are individual pro?t centers, each with its own cash cycle based on the costs of activities related to the project and on payments from a client, both of which are prescribed by a contract. Typical cash ?ow on a construction project consists of: ?1? cash out such as bid costs, preconstruction costs ?engineering, design, mobilization, etc.?, materials and supplies, equipment and equipment rentals, payments of subcontracts, labor and overhead; and ?2? cash in such as billings ?less retentions?, retentions, claims and change orders. The factors that affect cash ?ows are the duration of the project, the retention conditions, the times for receiving payments from the client, credit arrangement with suppliers or vendors, equipment rentals, and times of payments to subcontractors, etc. Cash ?ow at the project level consists of a complete history of all cash disbursement and all earnings received as a result of project execution. Many construction projects have negative net cash ?ows until the very end of construction when the ?nal payment is received or advanced payment is received before starting the project. This is a typical situation when the ?nal payment consists of retention money and the retention percentage is greater than the pro?t percentage of the project. Structure of Construction Budget A budget structure in construction projects is constituted of cost accounts such as bills, sections, items, and resources. A budget is a plan for allocating resources ?Meredith and Mantel 1995?. Hendrickson and Au ?1989? identi?ed the fact that allocation of a cost to the budget may be used to develop the cost function of an operation. The basic idea in this method is that each expenditure item can be assigned to particular categories of operation. Ideally, the allocation item of joint costs should be causally related to the category of basic costs in an allocation process. Generally, a budget structure in construction projects is set up into labor, material, equipment, subcontract, and indirect expenses. If a general contractor performs all the areas of job management on site, expenses for management and overhead cost become a higher burden to the general contractor. To mitigate these costs, general contractors prefer to distribute a role of management to other participants. As an example, if a portion of a subcontract is increased, the general contractor is able to decrease the indirect expenses used for hiring project personnel: the workers, supervisory personnel, and engineers associated with the project. From the general contractor’s viewpoint, labor and equipment costs are uncertain because productivity is extremely volatile and hard to measure. For this reason, general contractors attempt to hire subcontractors to reduce job-management costs and to maximize their pro?t opportunity by concentrating their control ability on variable costs, uncertain time, and strict quality. Typically, the portions of subcontract cost range from 50 to 70%. Material, labor, equipment, and indirect cost are arrayed between 25 and 35%, 5 and 15%, 10 and 25%, and 5 and 15%, respectively ?Oberlender 2000?.
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Jobsite Cash Flow Forecast Model
Cash-Out Model Time Lag The critical key to cash ?ow forecasting at the project level is how to build a cash-out model. All resources to be incurred to costs in a budget have different time lags. They are subject to contracting procedures and a corporation’s payment policy to other organizations. Accordingly, cash-out forecasts set the tone for time lags. Cost categories are classi?ed in order to compile construction resources with similar time lags. Time lag, as used here, is based on contracting payment conditions and credit times given by suppliers or vendors. Ahuja and Walsh ?1983? also insist that there are delays between the dates of costs incurred and the dates of payment due. These delays will vary depending on resource types and credit arrangements as negotiated with subcontractors and suppliers. This approach is maintained by a number of previous researchers ?Peterman 1973; Ashley and Teicholz 1977; McCaffer 1979; Trimble 1982; Kenley and Wilson 1989; Navon 1995; Kaka 1996?. Different cost categories are de?ned for materials, labor, equipment, subcontractors, indirect expenses ?site overhead?, and depreciation items since these cost categories generally have different time lags. If additional cost categories are needed, they can be classi?ed. As mentioned before, since payment conditions of subcontracts are controlled by general contractor policy, it can be noted that the general contractors entail 50–70% certainty in cash ?ow forecasting regarding time lags. The only remaining problems are how to determine time lags of other cost categories and how to plan a budget for each period. Jepson ?1969? suggested that net cash ?ow for individual projects must be derived from “component” curves of in?ow and out?ow pro?les. Fondahl and Bacarreza ?1972? claimed that total costs can be broken down as to category since different cost resources may have different cost curves or different time lags related to their payment. Moving Weights of Cost Categories Ashley and Teicholz ?1977? developed ?ve cost curves for cost categories in their highway construction project. Fondahl and Bacarreza ?1972? also applied three cost curves to their school project ?Curves 1, 2, and 3?. Curve 1 is based on the assumption that the rate of expenditure will be uniform over the project duration. Curve 2 assumes that only 25% of the total cost is incurred during the ?rst half of the project duration and the remaining 75% in the second half. Curve 3 assumes that 75% of the total cost is incurred in the ?rst half of project duration. In their research, only ?eld overhead and home of?ce overhead costs were analogous to Curve 1, which implies that only these costs were assumed to be incurred at a uniform rate over the project duration. In other words, all cost categories except ?eld overhead and home of?ce overhead were not incurred at a uniform rate over the project lifetime. Unless the curves of all cost categories are uniform, the relative weights of the different cost categories should be changed whenever costs are incurred over the project duration. If weights of cost categories are uniform over the project duration, curves of all categories should represent straight lines. The concepts of the moving weights method and ?xed weights method are illustrated in Fig. 1.
Fig. 1. Comparison of weights of costs during construction period
For that reason, whenever costs are incurred in a periodic month, weights of cost categories relative to the remaining budget are changed, even though neither the overall budget ?the forecast total cost? nor the planning for execution is changed. Moreover, if a change of project amount or project duration occurred due to a change order or a change of contract conditions, weights of cost categories should also be adjusted ?Park 2001?. Consequently, this implies that the next weight of a cost category to be applied will be set in accordance with the cumulative actual cost and the remaining budget. Thus, “the moving weights method” continuously changes over the project duration to pertain to the remaining budget. Applying moving weights of cost categories to the remaining budget in a month ?time series? reduces the uncertainty of forecasting cash out for the remaining duration of the project. This characteristic of a budget during the construction period is illustrated in Fig. 2. Cash-In Model Billing Time Generally, earned values will be received on a monthly basis or based on billing terms, but planning of earned values on a jobsite is established by a monthly amount. Earned value planning is the basis for estimated cash-in values in actual cash ?ow analysis. Net planned monthly earned values are simply transferred to the cash-in forecast, to be applied there with appropriate time lags. The billing period, the time between the dates of bill submittal and the progress payment receipt, is stipulated in the contract. If a payment delay occurs due to the owner’s circumstances, the billing time of cash in can be adjusted in this model. In practice, billing terms in the contract should provide for a billing schedule for owner and contractor, but those terms can be applied variously depending on the owner’s ?nancing situation.
Fig. 2. Characteristic of budget during construction period
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Retention Money Cash-in planning should consider the effects of retention money and the billing period on earned values. Retention money is based on a percentage of retention stipulated in the contract. A cumulative cash-in curve is obtained from the cumulative earned value curve by applying a retention rate and billing period. Generally, contractors can improve cash ?ow by providing percent retention schedules in contracts with subcontractors. Then, the retention money is released when construction is completed and accepted. If cash in is properly planned and manipulated by a model, it will supply the funds necessary to meet the cash requirements of the project without borrowing from other organizations. Mathematical Algorithm of Model Cash Out The model algorithm for cash out can be represented by equations. In cash out, the cost categories in an initial budget can be classi?ed depending on the time lags of all resources in the budget. After that, the following equation is applied: initial weight ?wi? = Ci ÷ TB ?1?
where i = cost categories; Ci = budget of individual cost categories; and TB= initial total budget ?total costs?. Whenever deviation between actual and planned data occurs, an adjustment of weight is calculated and applied to the next cash planning. Since actual cost in accordance with initial weight of each cost category in each month is not incurred, actual cost and actual earned value should be re?ected in the next weights of individual categories. The weight is called the “moving weight” in this research. Therefore, the next moving weight to be applied is moving weight ??wi? = ?Ci ÷ ?TB ?2?
Fig. 3. Process of model
TCt = actual cumulative total cost; and FCt+1,i = actual cash-out ?ow. Cash In Earned value is converted to cash in by deducting retention and applying billing time. This model considers that most contractors withhold retention from subcontractors at the same rate they are withheld by the owner. Therefore cash in should consider two kinds of retention money: contractors’ retention and subcontractors’ retention. Hence, the cash in is calculated as follows: CIt = Vt ? ?1 ? rc? + rs ? St ?8?
where ?Ci = remaining budget of individual cost category and ?TB= remaining total budget. From Eqs. ?1? and ?2?, the constraints on weights of the individual cost categories can be represented by the following equations: ?wi = 1 or ??wi = 1 ?4? ?3?
where i = individual cost categories. As a result, equations for the moving weights cash-out model are as follows. In terms of this model, the algorithm can be continuously updated to the weight to be applied in each month over the project duration Ft+1,i = wt+1,i ? Ct+1 wt+1,i = Ct,i ? ACt,i TBt ? TCt ?5? ?6? ?7?
where CIt = cash in at the time t; Vt = earned value at the time t; rc = contractual retention rate; rs = subcontractual retention rate; and St = subcontract cost at the time t. Depending on the contractual agreement, release of retention is prescribed in two ways: ?rst at the completion of the contract and second, at the end of the maintenance period. The model is simulated by entering the ?gures of release of retention by contractors whenever the subcontract ends. Model Process Fig. 3 illustrates an integrated process of model to cash ?ow forecasting. It consists of three steps designed for general contractors on a job site level to evaluate cash ?ow. The ?rst step requires input data for evaluating each individual project, such as planned earned values and budget ?cost? to each month, cost categories, weights, and time lags. If more cost categories due to different time lags are required, the users can classify separate cost categories. The second step updates new weights to cost categories re?ected on actual cost. Also, forecast cash ?ow such as cash in, cash out, cumulative cash ?ow, and capital cost are automatically
? ACi = ? ? FCt+1,i
where Ft+1,i = forecast of individual cost categories of time series in period t + 1; Ct+1 = planned costs of time series in period t + 1; wt+1,i = weights of cost categories of time series in period t + 1; AC,i = actual cumulative cost of individual cost categories;
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Table 1. Example of Cash Forecasting at Start of Projecta Time period ?days? 0 30 61 91 122 152 183 213 244 274 305 335 365 396 426 457 487 518 Sum Planed earned value 1,097 1,159 1,104 1,106 982 627 716 997 1,183 1,302 1,173 1,083 — — — — — 12,529 Material Time lag ?days? 150 Planned budget 4,033.80 Initial weight ?%? 32.71% a Unit= 1,000 US dollars. Planned budget 1,072 1,130 1,095 1,084 975 620 709 972 1,176 1,288 1,152 1,059 — — — — — 12,332 Labor 0 456.28 3.70% Actual value — — — — — — — — — — — — — — — — — — Depreciation 0 567.27 4.60% Actual cost — — — — — — — — — — — — — — — — — — Equipment 0 27.13 0.22% Cash in — — — — 5,448.00 — — — 3,523.00 — — — — — — — — 8,971.00 Main materials 0 0 0.00% Cash out 83.08 87.58 84.86 84.01 75.56 987.66 1,045.39 1,035.10 1,041.27 954.41 632.71 703.51 851.96 1,030.76 1,128.93 1,009.73 928.21 11,764.73 Sub-Con 150 6,775.20 54.94% Cumulative balance ?132.39 ?271.95 ?407.18 ?541.05 4,786.53 3,770.36 2,692.35 1,612.54 4,040.18 3,026.52 2,340.82 1,588.60 736.64 ?294.13 ?1,423.06 ?2,432.79 ?3,361.00 — Sum Interest ?10%? 0.00 ?1.09 ?2.24 ?3.35 ?4.45 39.34 30.99 22.13 13.25 33.21 24.88 19.24 13.06 6.05 ?2.42 ?11.70 ?20.00 ?27.62 129.30 Billing time 120
Balance 0.00 ?132.39 ?139.56 ?135.23 ?133.87 5,327.59 ?1,016.18 ?1,078.01 ?1,079.81 2,427.64 ?1,013.66 ?685.70 ?752.23 ?851.96 ?1,030.76 ?1,128.93 ?1,009.73 ?928.21 ?3,361.00 Expense 0 472.32 3.83%
Depreciation 49.31 51.98 50.37 49.86 44.85 28.52 32.61 44.71 54.10 59.25 52.99 48.71 — — — — — 567.27 Retainage 0 0%
12,332 100.00%
calculated. This stage is based on moving weights of each classi?ed cost category in each month. Moving weight is that weight to be applied to the next month that is adjusted and calculated by deducting the actual cost from the initial budget to the individual classi?ed cost category in each month. Therefore, a weight of each budget of each individual cost category to the remaining budget is to be changed every month. The ?nal step provides feedback to estimate the new planned earned values and budget for each month. Whenever deviations between planned and actual costs and earned values occur, they are automatically distributed over the remaining duration if needed. If deviations between them are considerably more or less than expected, the project manager must modify the initial planning to forecast cash ?ow. As a basis for applying the proposed model, this paper sets up basic assumptions: ?1? in the initial time period, the planned earned value to the contract amount and planned cost to the budget are not automatically generated each month. Instead they are made independently by engineers on the jobsite by their own method of planning. ?2? Time lags of cost categories are based on corporate historical data and company policy. ?3? Cost categories classi?ed at the start of a project have to continuously be used in order to maintain the degree of accuracy in moving weight over the project duration. ?4? This model is used to forecast cash ?ow values at the close of each month ?last day of the month?. ?5? Depreciation of company owned equipment is included in actual cash transfer incurred cost in order to show cash ?ow at the project level. ?6? Home of?ce overhead is not considered in this
model since that is not generally considered as a job or project cost. That is incurred at the company level and accordingly may be billed directly on a jobsite. Illustrative Example To illustrate the new methodology proposed, we have conducted a simple case study. The illustrative case is composed with the following ?gures: project duration is 12 months, contract amount is US$12,529,000, and budget is US$12,332,000. Input variables for the case are: ?1? planned earned values ?PE?, planned budget ?PB?, actual earned value ?AE?, and actual cost ?AC? at each month; ?2? cost categories classi?ed based on contract procedure; ?3? weight percentage and credit time of each cost category; ?4? billing time and percentage of retention to be stipulated in contract; and ?5? percentage of interest to be applied by corporate policy or decision. According to investigations by Singh and Lakanathan ?1992?, the application of “S curves” for cash ?ow projections can achieve an accuracy of approximately 88–97%. Subsequently, input data at each month are based on S curves. The basic assumptions applied to the case study are: ?1? changes of AC and AE against PE and PB at each month are addressed, but overrun of budget and delay of duration are not considered; ?2? cost categories depending on time lags are simply classi?ed as labor ?0 days?, materials ?150 days?, rent equipment ?0 days?, depreciation of owned equipment ?0 days?, subcontract ?150 days?, and ?eld expense ?0 days?; ?3? billing time to earned value is 120 days and percent of retainage is 0% of earned value
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Table 2. Example of Updated Cash Forecasting after “1” Montha Time period ?days? 0 30 61 91 122 152 183 213 244 274 305 335 365 396 426 457 487 518 Sum Planned budget Actual cost after 1 month Actual weight ?%? Remaining budget New moving weight ?%? a Unit= 1,000 US dollars. Planed value 1,097 1,159 1,104 1,106 982 627 716 997 1,183 1,302 1,173 1,083 — — — — — 12,529 Material 4,033.80 72.97 10.01 3,960.82 34.14 Planned budget 1,072 1,130 1,095 1,084 975 620 709 972 1,176 1,288 1,152 1,059 — — — — — 12,332 Actual value 1,090 — — — — — — — — — — — — — — — — 1,090 Actual cost 729 — — — — — — — — — — — — — — — — 729 Cash in — — — — 5,441.00 — — — 3,523.00 — — — — — — — — 8,964.00 Cash out 87.48 84.56 81.94 81.12 72.96 654.38 1,046.52 1,035.43 1,041.02 953.57 631.29 702.58 854.55 1,033.90 1,132.37 1,012.80 931.04 11,437.51 Balance 0 ?121.01 ?136.54 ?132.31 ?130.98 5,323.19 ?682.90 ?1,079.13 ?1,080.14 2,427.88 ?1,012.82 ?684.28 ?751.29 ?854.55 ?1,033.90 ?1,132.37 ?1,012.80 ?931.04 ?3,025.00 Expense 472.32 76.91 10.55 395.41 3.41 Cumulative Interest balance ?10%? Depreciation ?121.01 ?257.55 ?389.86 ?520.84 4,802.35 4,119.45 3,040.32 1,960.18 4,388.07 3,375.25 2,690.96 1,939.67 1,085.12 51.22 ?1,081.15 ?2,093.96 ?3,025.00 — Sum 12,332 729 100.00 11,603 100.00 0.00 ?0.99 ?2.12 ?3.20 ?4.28 39.47 33.86 24.99 16.11 36.07 27.74 22.12 15.94 8.92 0.42 ?8.89 ?17.21 ?24.86 164.08 33.53 51.98 50.37 49.86 44.85 28.52 32.61 44.71 54.10 59.25 52.99 48.71 — — — — — 551.49 Retainage 0%
Labor Depreciation Equipment Main materials Sub-Con 456.28 567.27 27.13 0 6,775.20 8.97 33.53 1.60 0 535.01 1.23 4.60 0.22 0.00 73.39 447.32 533.74 25.53 0 6,240.19 3.86 4.60 0.22 0.00 53.78
each time; ?4? in consideration of different time lags of cost categories, cash ?ow is calculated each 30 days; and ?5? whenever negative cumulative cash ?ow occurs, internal interest ?10%? is charged. Table 1 represents the example of cash forecasting at the beginning of project ?“0” month? made in accordance with the basic conditions and aforementioned algorithm with considerations of time lags and moving weights. As an example, in time period of 30 days, cash out ?US$83.03? is gaged only considering cost categories that have no time lags ?planned budget ?US$ 1,072? ? sum of initial weights of labor, equipment, and expenses ?7.75%??. It expects that the ?nal cash balance at completion will be negative—around US$3,316,000. As the ?rst month passes and
Table 3. Project Overview Project name Project A: apartment Project overview • 8–25 story ?seven building apartment? • 490 unit • Area: 279 ha • Width: 20 M ?four lane? • Earth work: 19 million m3 • Joint venture project • Total length: 11.432 km • Bridge: 12 ?4,867 m? • Tunnel: 1 ?545 m? • Stations: three stop • Joint venture project • Treatment capacity: 80,830 t / day
actual cost ?US$729? is incurred, we can update new weights to cost categories. The proposed model employs weights that are updated based on weight percentage of cost categories to the remaining budget each month over the duration, while the traditional approach is designed based only on weights to the initial total budget. For example, new moving weight of the material cost category ?34.14%? can be updated by dividing the remaining budget of material ?US$3,960.82? to the remaining total budget ?US$11,603?. Table 2 shows an example of the updated cash balance ?negative US$3,025,000? in accordance with these revised moving weights.
Validation of Model
Model validation includes measuring the accuracy of a model in describing the actual conditions of a problem to solve and in
Table 4. Project Basic Dataa Items Project A Project B 94,465 49.3 82,946 8.95 21.28 1.33 63.47 0.36 4.61 Project C 79,632 96 72,989 2.20 15.30 5.40 61.70 0 15.40 Project D 51,257 63 42,221 4.21 3.08 0 83.58 0 9.13
Project B: industrial complex
Project C: railway
Project D: sewage treatment
Contract amount 46,648 Duration ?months? 33 Total budget ?dollars? 36,486 Labor ?%? 1.31 Material ?%? 33.73 Equipment ?%? 0.22 Subcontract ?%? 49.14 Depreciation ?%? 4.6 Expense ?%? 11.0 a Unit= 1,000 US dollars.
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Table 5. Time Lags for Each Cost Categorya Project A
b
Table 6. Billing Time and Delay in Payment for Each Projecta Project C
b
Project B
b
Project D
b
Project A
Project B
Project C
Project D 60 30
Labor 0 0 0 0 Material 150 120 90 120 0b 0b 0b 0b Equipmentc Subcontract 150 120 90 90 0b — — Depreciation 0b 0b 0b 0b Expense 0b a Unit= days. b The last day of the month when cost is incurred. c Equipment cost is charged to expense of cash out in accounting perspective.
Billing time 120 30 90 0 0 150 Delay in paymentb a Unit= days. b Delay payment is total delay time from the client.
evaluating the usefulness of the model in terms of its objectives to a larger case with similar problem contexts. Stated earlier, a comparative case study methodology was used to validate whether the model meets its development objective. Validation Procedures To verify the model, we performed simulation using empirical data from actual projects in progress. Simulation results based on the proposed model and existing model are compared. A simulation template is implemented in a common spreadsheet package—Microsoft Excel™—for the simulation experiments. Considering different time lags of cost categories, cash ?ow is calculated in monthly increments. The ?xed weights method ?FWM?—the current approach—applies ?xed weights to cost categories over project duration, whereas the moving weights method ?MWM?—the new proposed model—applies different weights each month using a new algorithm. The results of these two models were then compared to show the accuracy and consistency of the model. The stepwise procedures for the validation of the proposed model are as follows: 1. Four actual projects in progress including one building and three civil infrastructure projects, with data compiled over a duration of 12 months ?see Tables 3 and 4?; 2. To obtain forecast cash ?ow data, MWM and FWM are applied to actual data through the simulation; and 3. To compare the accuracy of MWM to FWM, the results of the simulation are analyzed. To simulate the dynamic cash ?ow forecasting, simulation experiments were conducted 12 times from the ?rst to the twelfth month by each method for individual projects and compared by the two methods: MWM and FWM. In addition, 2 types of simuTable 7. Example of Comparative Analysis for Project Aa Actual cash ?ow Time period ?days? Cash in Cash out Cash ?ow
lations are performed on each project in order to compare the accuracy of forecasting models. Subsequently, 48 simulations per each project and a total of 192 simulations were performed for four projects. In the comparative analysis the results of forecasting are applied to cash ?ow each month instead of the cumulative cash ?ow forecasting applied previously in experiments since previous cash ?ow affects subsequent cash ?ow. To compare the accuracy of two models, MWM and FWM, the simulation is performed in accordance with the following two types: ?1? Type 1—planned data and actual data are identical to each other. This type is used to determine the reliability of the model and compare the two methods under ideal conditions since planned data are one of the most critical variables in this forecasting cash ?ow model, and ?2? Type 2—planned data and actual data are different as reported by the jobsite for 12 months. In this case, the uncertainty of the construction job site is involved and the effect of planned data on the forecasting cash ?ow is considered. Finally, the following data are required for comparative analysis of the four projects in progress: 1. Time lags of billing time and individual cost categories ?see Tables 5 and 6?; 2. Total contract amount and total budget re?ect contract amount and budget changed during the construction stage; 3. Monthly cost planning data and earned value planning data; 4. Weights of cost categories in the budget; 5. Retention rate and capital cost rate; and 6. Actual cash ?ow such as cash in and cash out for each month. Validation Results Measurement of Accuracy Mean absolute deviation ?MAD? was used to measure the error for each month’s forecasted cash ?ow by means of the two models: MWM and FWM. The MAD is a commonly used measure
Forecasting cash ?ow MWM ?678.018 ?875.830 ?1,107.974 3,567.408 ?919.695 ?662.458 ?1,041.238 FWM ?707.169 ?879.505 ?1,112.959 3,523.455 ?943.710 ?697.197 ?1,083.378
Mean absolute deviation MWM 57.985 24.202 52.871 56.577 109.678 30.388 132.229 66.276 FWM 28.834 20.527 47.886 100.530 133.694 65.128 174.369 81.567
183 — 736.003 ?736.003 213 — 900.032 ?900.032 244 — 1,160.845 ?1,160.845 274 4,357.000 733.015 3,623.985 305 — 810.016 ?810.016 335 — 632.069 ?632.069 365 — 909.009 ?909.009 Average MWM?moving weights method; and FWM??xed weights method. a Simulation 1—after 1 month, Type 1.
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Table 8. Comparison of Mean Absolute Deviation ?MAD? in Moving Weight Method ?MWM? and Fixed Weights Method ?FWM? ?Type 1?a MAD MWM ?A? FWM ?B? B?A MAPE
Table 10. Reliability of Moving Weights Method ?Type 1?a Project Mean absolute deviation ?A? Contract amount ?B?b ?A / B? ? 100 ?%? 0.31 0.23 0.60 0.38 0.38
Project A 41.207b 68.010 26.803 65.04%c Project B 62.395 93.356 30.962 49.62% Project C 24.106 24.173 0.067 0.28% Project D 27.781 31.739 3.957 14.24% Average 32.30% a Unit= 1,000 US dollars. b It is calculated by Eq. ?9? through the 12 times of simulations from the 1st month to the 12th month ?refer to Table 7 for the case of simulation 1?. c MAPE= 26.803÷ 41.207= 65.04%.
A 41.207 13,489 B 62.395 27,464 C 27.094 4,539 D 27.781 7,310 Average a Unit?1,000 US dollars. b Contract amount means total earned values for 12 months.
that forecasts accuracy as the degree of variation by the following equation. This measure is simply the measure of the absolute deviations for all forecasts 1 MAD = ?Acfti ? Fcfti? n ?9?
In the same way, the average MAD can be calculated through the 12 times of simulations from the ?rst month to the twelfth month. Based on the results of MAD for simulations, MWM is more accurate than FWM. In the Type 1 simulation, the accuracy is 0.28–65.04%, with an average of 32.30% higher than FWM in the ideal condition, where planning is well established as re?ected on the construction jobsite, and continuously updated ?see Table 8?. In the case of Type 2, the accuracy is 1.14–4.54%, an average of 2.13% higher ?see Table 9?. As a result, the degree of accuracy of MWM is an average of 17.21% higher than FWM. Reliability Kenley and Wilson ?1986? and Kaka and Price ?1991? suggested that the error range of forecasting in the construction industry is within ±3% of the contract amount. This is considered an acceptable limit and demonstrates the reliability of the proposed model. The error range of the forecasting is 0.23–0.6%, with an average of 0.38% for four projects in Type 1, and 0.82–2.78%, with an average of 1.79% for four projects in Type 2 ?see Tables 10 and 11?. Despite unavoidable errors in the planning data, the result is thought of as being reasonably attained by applying the model for forecasting. Consequently, the reliability of the MWM model is acceptable and well demonstrated based on simulation. Practical Implications to Industry Financial management has long been recognized as an important management tool. A company can survive a transitional period without a pro?t, or even with a loss; however, it may fail due to lack of cash during the operation even if it has a good ?nancial statement. In the viewpoint of corporate, cash ?ow forecasts should be made at all stages of the project from the planning stage to the operation and maintenance stage of a project. However, cash ?ow forecasting and management are a dynamic process. Deviations of all projects at the corporate level may signi?cantly affect the ?rm’s ?nancial status. Moreover, inadequate cash ?ow forecasts to a certain project may drive a corporate into a crisis of ?nancial situations.
Table 11. Reliability of Moving Weights Method ?Type 2?a Project Mean absolute deviation ?A? Contract amount ?B?b ?A / B? ? 100 ?%? 1.23 2.78 0.82 2.34 1.79
where Acfti = actual value of cash ?ow, Fcfti = forecasting value of cash ?ow at t month by the ith simulations, respectively; t = 1 – 12; i = 1 – 12; and n = number of observations. In comparative analysis, the mean absolute percent error ?MAPE? method was estimated for comparing errors. It can be achieved by dividing the difference between MAD of MWM and FWM by MAD of MWM. This fraction represents how large the error of FWM is as compared to MWM. All absolute deviation data between MWM and FWM are summed and divided by the number of observations, by the following equations: average MAPE = 1 n
t=12 i=12
? ? t=1 i=1
mMADti ? fMADii 100?% ? ?10? mMADti
where mMADti = MAD value of MWM at t month by ith simulation; fMADti = MAD value of FWM at t month by ith simulation; and n = number of observations. The cash ?ow data consisted of four detailed real projects in progress, together with their associated estimated monthly values. The data were obtained from “D” Construction and Engineering Company in Korea. During the data collecting, adjustment of budget and contract amount including in?ation was applied to speci?c projects. Table 7 shows the actual data to achieve the MAD for the speci?c case of simulation 1, which are estimated by Eq. ?9? after the ?rst month passes from the start of the project.
Table 9. Comparison of Mean Absolute Deviation ?MAD? in Moving Weight Method ?MWM? and Fixed Weights Method ?FWM? ?Type 2? MAD Mean absolute percent error ?%? 2.14 0.68 1.14 4.54 2.13
MWM ?A? Project A Project B Project C Project D Average 153.910 867.361 37.960 222.364 —
FWM ?B? 157.198 873.238 38.395 232.462 —
B?A 3.287 5.877 0.430 10.099 —
A 153.910 12,529 B 867.361 31,158 C 37.045 4,516 D 222.364 9,500 Average a Unit ?1,000 US dollars. b Contract amount means total earned values for 12 months.
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Considering the real business world in construction industry, various forecasting methods may be applied to cash ?ow. Some judgment on a jobsite is needed in the case of forecasting cash ?ow with respect to complexity and unexpected situations in construction industry. However, if jobsite engineers or project managers rely only on judgment based on their experience without the use of any mathematical forecasting techniques, they may not make a good decision to forecast cash ?ow. Essentially, a good forecasting technique needs to include both a historical trendbased data supported method and competent judgments based on construction experience and knowledge. In this respect, the proposed model suggest a practical and easy approach for jobsite engineers and project managers who are generally not familiar with ?nance knowledge of forecasting cash ?ow using the regular reports. This model can be applied as part of a project evaluation process and continuously updated to show deviations between plan and actual data through changes or information from the jobsite. In addition, the fast and simple forecasting cash ?ow allows ?eld engineers or project managers to support and to save time for decision making of strategy of cash management to the corporation and projects.
actual data; ?2? developing cash ?ow forecasts at the planning stage using the relationship between cumulative earned value and cost categories; and ?nally ?3? implementing a system at the corporate level to monitor cash ?ow in a proactive and timely manner.
References
Ahuja, H. N., and Walsh, M. A.. ?1983?. Successful methods in cost engineering, Wiley, New York. Ashley, D. B., and Teicholz, P. M. ?1977?. “Pre-estimate cash ?ow analysis.” J. Constr. Div., Am. Soc. Civ. Eng., 103?3?, 369–379. Bennett, J., and Ormerod, R. N. ?1984?. “Simulation applied to construction projects.” Constr. Manage. Econom., 2, 225–263. Fondahl, J. W., and Bacarreza, R. R. ?1972?. “Construction contract markup related to forecasted cash ?ow.” Technical Rep. Prepared for Construction Industry Institute, Stanford Univ., Stanford, Calif. Gates, M., and Scarpa, A. ?1979?. “Preliminary cumulative cash ?ow analysis.” Cost Eng., 21?6?, 243–249. Hendrickson, C., and Au, T. ?1989?. Project management for construction: Fundamental concepts for owner, engineer, architects, and builders, Prentice Hall, Englewood Cliffs, N.J. Jepson, W. B. ?1969?. “Financial control of construction and reducing the element of risk.” Contact. J., April, 862–864. Kaka, A. P. ?1996?. “Towards more ?exible and accurate cash ?ow.” Constr. Manage. Econom., 14, 35–44. Kaka, A. P., and Price, A. D. F. ?1991?. “Net cash ?ow models: Are they reliable?” Constr. Manage. Econom., 9, 291–308. Kenley, R., and Wilson, O. D. ?1986?. “A construction project cash ?ow model—An idiographic approach.” Constr. Manage. Econom., 4, 213–232. Kenley, R., and Wilson, O. D. ?1989?. “A construction project net cash ?ow model.” Constr. Manage. Econom., 7, 3–18. McCaffer, R. ?1979?. “Cash ?ow forecasting.” Quantity Surveying, August, 22–26. Meredith, J. R., and Mantel, S. J., Jr. ?1995?. Project management—A management approach, 3rd Ed., J Wiley, New York. Navon, R. ?1994?. “Company-level cash-?ow management.” J. Constr. Eng. Manage., 122?1?, 22–29. Navon, R. ?1995?. “Resource-based model for automatic cash-?ow forecasting.” Constr. Manage. Econom., 13, 501–510. Navon, R. ?1997?. “Cash-?ow forecasting and management.” Proc., Construction Congress, ASCE, New York, 1056–1063. Oberlender, G. D. ?2000?. Project management for engineering and construction, 2nd Ed., McGraw–Hill, New York. Park, H. K. ?2001?. “Cash ?ow forecasting model using moving weights of cost categories for general contractors on jobsite.” PhD dissertation, Univ. of Wisconsin, Madison, Wis. Peer, S. ?1982?. “Application of cost-?ow forecasting models.” J. Constr. Div., Am. Soc. Civ. Eng., 108?2?, 226–232. Peterman, G. G. ?1973?. “A way to forecast cash ?ow.” World Constr., October, 17–22. Reinschmidt, K. F., and Frank, W. E. ?1976?. “Construction cash ?ow management system.” J. Constr. Div., Am. Soc. Civ. Eng., 102?4?, 615–627. Russell, J. S. ?1991?. “Contractor failure: Analysis.” J. Perform. Constr. Facil., 5?2?, 163–180. Sears, G. A. ?1981?. “CPM/COST: An integrated approach.” J. Constr. Div., Am. Soc. Civ. Eng., 107?2?, 227–238. Singh, S., and Lakanathan, G. ?1992?. “Computer-based cash ?ow model.” Proc., 36th Annual Trans., AM. Assoc. of Cost Engineers, AACE, R.5.1–R.5.14. Trimble, E. G. ?1982?. “Micro computers in construction management.” Building Technology Management, 2?2?, 11–13.
Conclusions
A simple cash ?ow forecasting model ?MWM? was developed to assist general contractors on jobsites during the construction phase. The model was based on the general procedure of construction jobsites and the nature of a general contractor’s budget. The model included new methodology that was not addressed by previous researchers. A comparative case study methodology was chosen as a proper validation approach to evaluate the bene?ts of the proposed model. Four real projects were identi?ed as the case study materials and the validity of the model was tested by actual data from these projects in progress. The overall validation procedures were derived from a series of simulations by comparing the results of the proposed model with other models suggested by previous researchers. Ultimately, the cash ?ow forecasting model was demonstrated to be a simple, accurate, and reliable forecasting tool for general contractors at the construction stage in comparison with FWM. During the case studies, two issues were recognized as requiring more research. The ?rst is that the model is dependent on the planning of cost and earned value. If planning of cost and earned value are not accurate, the forecasted cash ?ow would not be accurate. The second issue is, similar to the ?rst, how to obtain reliable variables at the jobsite level such as the release of retention money—because it can be applied depending on the duration of a subcontract. Despite several limitations, the proposed model presents a practical and easy approach for jobsite engineers and project managers who are not familiar with extensive ?nancial knowledge, just using regular reports without separate information at the jobsite. In addition, this model can be applied as part of a project evaluation process considering internal interest ?capital cost? at the corporate level. The model can be continuously updated to show deviations between planned and actual data through information changes from the jobsite. Encouraged by the results of current research, future procedural research will concentrate on: ?1? analyzing the impact on difference between planned data and
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