Evaluation of Models for Forecasting the Final Cost of a Project

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At the top of the paper provide the reference to your Journal Article. Use a scholarly source as described above. Utilizing a non-scholarly/non-peer-reviewed source will result in significant point deduction.

1.The INTRODUCTION is your initial brief discussion of the concept identified from the chapter 9 & 10 (attached below) that you are going to examine. This is just a brief presentation of the concept. 

2.The SUMMARY is the introduction of your journal article and a brief overview of the content.

3.The RELEVANT POINTS section is the most important section of this paper. This is where you compare the article to the text based upon the concept you selected. This should demonstrate how the article either supports or opposes the concept(s) as presented in the text.

4.The CRITIQUE section is where you review the article to state whether it is a better authority on the concept(s) or whether the text provides a better authority. This based upon providing a sound review of the strengths/weaknesses of the article.

5.The Application of Concept(s) section is a personal discussion of how you can implement this concept into our own work. This should be a specific example and not just a general statement.

Apply the concept(s) to your career, field, industry, etc. Provide a real world application not a general statement. This section should demonstrate how you can take the findings of this article or case study and utilize them in a practical way in your career, field or practice. Make the application specific to your own experience. Do not just provide a general overview of the usefulness of the findings. Be specific; not general.

Provide a balanced criticism of the article or case study. What were the strengths and weaknesses of the study? How do the findings support the field of project management? How could it have been altered to better support the field?

Note:

1.References (this does not count toward the required paper length)

FINALLY, you must cite your sources after every sentence which contains information from one of your sources. Just putting a citation at the end of a paragraph or section is not sufficient.

Also, do not state "The author" or "the text". Use proper APA style. This is Last Name of Author (Year). So, the proper reference to the text would be...Kloppenborg, et al. (2019)

2. Give make 250 words document about the important finding in the article.In the summary and relevant points section provide proper citations with page number.

4. Research brief needs to discuss how article compares to text.

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Evaluation of Models for Forecasting the Final Cost of a Project Ofer Zwikael, Graduate School of Business Administration, Tel Aviv University, Tel Aviv 69978 Israel Shlomo Globerson, PMP, Graduate School of Business Administration, Tel Aviv University, Tel Aviv 69978 Israel Tzvi Raz, PMP, Graduate School of Business Administration, Tel Aviv University, Tel Aviv 69978 Israel ■ Abstract This paper addresses how to estimate the final cost of a project and when the estimate becomes accurate. The performance of five forecasting models drawn from literature was evaluated with data from a sample of actual projects. A stability analysis was carried out in order to identify when the forecasts become stable and accurate for the model that emerged as the most accurate. Keywords: cost performance index; earned value; cost control; forecasting ©2000 by the Project Management Institute — 2000, Vol. 31, No. 1, 53–57 — 8756–9728/00/$5.00 per article + $0.50 per page A main concern of every project manager is not to exceed the approved project budget. Indeed, a major portion of project control efforts is devoted to ensuring that actual costs do not deviate from planned costs. In reality, cost overruns are quite common, and, in many business fields, they are the norm rather than the exception. However, cost overruns are not always the fault of the project manager or the project team. Often, in high-risk areas that involve the development of new products, systems, or technologies, projects end up being inadequately funded due to a variety of reasons. These reasons include lack of knowledge regarding the true extent of the effort involved, inadequate reserves to account for technical challenges, as well as unwarranted optimism, which sometimes is needed in order to gain approval for the project to proceed. If a project is bound to deviate from its approved budget, then it is helpful to be able to predict the extent of the deviation. The sooner this information becomes available and the more accurate it is, the more useful it becomes for dealing with the various project stakeholders and for the organization’s financial planning. Literature offers several methods for forecasting final project cost, based on actual cost performance at intermediate points in time. These models vary in terms of their assumptions. They range from the naive belief that the project manager will be able to overcome all cost deviations experienced to date and will complete the project within the original budget, to the pessimistic argument that deviations will continue to accrue at the rate observed March 2000 so far. These two extreme approaches, as well as other intermediate methods, are presented in most project management textbooks—for example see Shtub, Bard, and Globerson (1994). However, there is no agreement regarding which forecasting model is the most accurate. The research of Fleming and Koppelman (1995a) suggests that the cost performance index (CPI), as calculated according to the earned value method, provides a good basis for cost forecasting models. The work of Beach (1990) and Christensen and Heise (1993) indicates that the CPI final value can be forecasted accurately in the early stages of the project, possibly as early as after 15% of the project duration. In this paper, the authors compare the performance of five models for forecasting final project cost. The comparison is based on data from 12 projects that were carried out in a high-tech company in Israel during a five-year period. The analysis identified one model that was clearly superior to the others, in terms of forecast accuracy. This model was further examined in order to determine the point in the project life cycle when the forecast the final cost. Forecasting Models The forecasting models selected differ in terms of the assumptions upon which they are based. The following earned value terminology is used to describe the models. BCWS Budgeted Cost of Work Scheduled ACWP Actual Cost of Work Performed BCWP Budgeted Cost of Work Performed Project Management Journal 53 CPI SPI BAC EAC Cost Performance Index (BCWP/ACWP) Schedule Performance Index (BCWP/BCWS) Budget at Completion (original planned cost of the project) Estimate at Completion (forecasted final cost of the project). The five forecasting models along with their respective sources and assumptions, are listed. 1. Constant budget—This model assumes that all cost deviations will be corrected by the time the project is completed (Fleming & Koppelman, 1994), implying that the final cost will be equal to the planned budget: EAC=BAC. 2. Constant cost deviation value—This model assumes that the remainder of the project will be executed according to the original plan (Fleming & Koppelman, 1996), implying that the numerical value of the budget deviation at the time of the forecast will not change: EAC = BAC + (ACWP–BCWP). ■ About the Authors Ofer Zwikael is a Ph.D. student in project management in the faculty of management at Tel-Aviv University, Israel. He has a B.Sc. in industrial engineering from Ben Gurion University and a MBA from Tel-Aviv University. He has been working in the Israeli Navy for the past six years. Shlomo Globerson, PMP, Ph.D., is an internationally known researcher, educator, and consultant in the fields of project management and operations management. A professor at the Graduate School of Business Administration, Tel Aviv University, he is extensively involved in developing new courses and workshops for MBA students, project managers, and top executives. He teaches project management courses in MBA programs, and runs project management workshops around the world. He holds a Ph.D. degree in industrial engineering from the University of California at Berkeley and has published over 60 refereed articles and six books. Tzvi Raz, PMP, holds B.Sc, M.A.Sc, and Ph.D. degrees in industrial and management engineering. He is on the faculty of the management of technology program of the Leon Recanati Graduate School of Business Administration at Tel Aviv University. Previously, he managed a technology insertion program at an IBM software development laboratory, and was on the industrial engineering faculties of the University of Iowa and Ben Gurion University. Dr. Raz is on the editorial review boards of Computers and Operations Research, the Project Management Journal, and the International Journal of Industrial Engineering. 54 3. Constant cost efficiency rate—This model assumes that the cost efficiency achieved so far in the project will remain through the remaining part (Shtub, Bard, & Globerson, 1994): EAC=BAC/CPI. 4. Constant cost and schedule efficiency rate—This model assumes that the final cost is affected by both the cost efficiency rate and the schedule efficiency rate (Fleming & Koppelman, 1994): EAC=BAC/(CPI*SPI). 5. Future constant cost and schedule efficiency rate—This model assumes that the cost deviation for the remainder of the project is a function of both the cost efficiency rate and the schedule efficiency rate. This deviation will be in addition to the deviation accumulated so far (Fleming & Koppelman, 1995b): EAC = ACWP+(BAC–BCWP)/(CPI*SPI). Data The 12 projects were all fixed-price and relatively low-risk projects, involving the production of a series of high-tech products, with little development work. Consequently, the expenditure rates were relatively stable over time, and there was a strong incentive not to exceed the planned budget. All 12 projects were carried out during a five-year period during 1993–1997. Budget and cost-control data were captured and processed by the same information system under the supervision of the same financial control manager. Cost and schedule data for the 12 projects in the sample appear in Table 1. The average planned cost was $1.3M, and the average duration was three years, with the vast majority of the projects ending with cost and schedule overruns. It is interesting to note that all projects in the sample were carried out for external customers under a fixed-price contract. Costs pertaining to scope changes introduced during the project execution were excluded from the analysis. The analysis was carried out in the following manner. Each project was divided into 10 segments of equal duration, each representing 10% of the actual duration. From historical records maintained in the financial control management system, the cumulative earned value (BCWP), planned cost (BCWS) and actual cost (ACWP) at the end of each of the 10 segments were calculated. Next, the performance indices CPI and SPI were calculated for each segment end point, and forecasts were obtained using the five methods described in the previous section. Since there is no point in forecasting the total cost at the end of the project, the end point of the last segment in the analysis was not included. The data set consisted of five forecasts at each of the nine intermediate points in time for each of the 12 projects. With this data, the performance of the five forecasting models was evaluated by considering the deviation between each forecast and the actual final cost. Project Management Journal March 2000 Project Actual Cost (k$) Cost Overrun (%) Planned Schedule (months) Actual Schedule (months) Schedule Overrun (%) 1 2 3 4 898 605 322 613 1,212 871 670 773 35% 44% 108% 26% 21 32 36 43 24 38 43 47 14% 19% 19% 9% 5 6 7 8 291 1,525 585 1,026 277 2,439 767 1,170 -5% 60% 31% 14% 24 50 46 29 24 59 54 30 0% 18% 17% 3% 9 10 2,223 6,077 2,979 6,988 34% 15% 45 44 55 50 22% 14% 353 1,305 1,319 646 2,099 1,733 83% 54% 31% 17 50 36 23 50 41 35% 0% 14% 11 12 Average Table 1. Planned Cost (k$) Cost and Schedule Data for the 12 Sample Projects Model 1. 2. 3. 4. 5. Constant Budget Constant Cost Deviation Value Constant Cost Efficiency Rate Constant Cost and Schedule Efficiency Rate Future Constant Cost and Schedule Efficiency Rate MSE (K $2) MAD ($) MAPE (%) 266 141 85 257 166 417 258 178 317 242 27 16 11 20 15 Table 2. Measures of Performance for the Five Forecasting Models Measures of Forecasting Performance Three measures of forecasting performance were used to evaluate the five models: ■ Mean squared error (MSE)—average of the square of the difference between the forecasted value and the actual value Mean absolute deviation (MAD)—average of the absolute value of the difference between the forecasted value and the actual value ■ Mean absolute percent error (MAPE)—average of the absolute value of the difference between the forecasted value and the actual value expressed as a percentage of the actual value. The average results for the 9 x 12 observations obtained for each forecasting model appear in Table 2. ■ It is apparent from Table 2 that the worst model is the one based on the assumption that the project will eventually recover and will complete within the original budget (#1). The two models that incorporate both the SPI and the CPI (#4 and #5) were inferior to the two models based on the CPI only (#2 and #3). This finding suggests that schedule performance is not truly relevant to final cost performance. Among the two March 2000 models based on the CPI only, the one based on the constant efficiency rate assumption (#3) gave the best results, while the next best model (#2) is at least 50% worse, according to any of the three performance measures. Overall, the results of the analysis based on this sample suggest that the most accurate forecasts are derived under the assumption that the cost efficiency observed from the beginning of the project up to the forecasting moment will stay constant through the end of the project. In order to apply this model, all we need is to calculate the value of the CPI, and divide the original budget by it. Of course, the actual value of the CPI is likely to change over time, depending on the specific problems encountered and the corrective actions taken. Stability Analysis For the purpose of forecasting the final cost, it is important to know at which point during the life of the project the CPI is sufficiently close to its final value. In Figure 1, the deviation of the CPI value calculated at the nine intermediate points during the life of the project from the final CPI value is plotted. Plot A shows the actual deviation, Project Management Journal 55 Actual Deviation 0.2 0.1 0.0 -0.1 -0.2 -0.3 -0.4 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 80% 90% 100% 80% 90% Percentage of Project Duration Elapsed Figure 1a. Actual Deviation 0.4 MAD 0.3 0.2 0.1 0.0 0% 10% 20% 30% 40% 50% 60% 70% Percentage of Project Duration Elapsed Figure 1b. Absolute Deviation 80% 70% MAPE 60% 50% 40% 30% 20% 10% 0% 0% 10% 20% 30% 40% 50% 60% 70% 100% Percentage of Project Duration Elapsed Figure 1c. Absolute Percentage Deviation Figure 1. Deviation From the Final CPI Over Time Plot B shows the absolute deviation, and Plot C shows the absolute deviation as a percentage of the final value. Each plot contains 12 data points for each time period, corresponding to the 12 projects in the sample. In many cases, two or more data values coincided and appear as a single 56 point on the plot. The solid line on each plot connects the averages calculated for the 12 projects. In order to study the trend in the CPI deviation, regression lines were fitted to the 12 x 9 points in each graph. The results are summarized in Table 3. Regardless Project Management Journal March 2000 Slope of Linear Regression Line Astatistical Significance 0.12 -0.13 -0.19 0.003 0.000 0.000 Actual Deviation Absolute Deviation Absolute Percent Deviation Table 3. Regression Coefficients and Statistics of whether we look at the actual deviation, the absolute value, or the percentage, there is a clear finding: The deviation decreases as we progress toward the end of the project. This is evident from the fact that all three regression lines are statistically significant. The positive slope of the regression on the actual deviations reflects the fact that, in this sample, the deviations are, in general, negative, and that as the project percentage elapsed increases, they tend to become less negative and come closer to the final CPI value. The meaning of the negative slopes of the other two regression lines is also clear: As we get closer to completion, the magnitude of the forecasting error, either in absolute or relative terms, decreases. This finding is hardly surprising. Intuition tells us that the further along we are in the project, the more difficult it is to implement corrective actions and to improve cost efficiency. However, it is still important to determine at which point in the project life cycle the CPI value and the resulting final cost estimate are sufficiently close to the true values. Visual examination of the plots in Figure 1 suggests that this happens at the 60% mark. This finding is more conservative than those of Beach (1990), who analyzed data from 700 projects and found that the final cost overrun will not be less than the overrun after the first 15%, and Christensen and Heise (1993), who reported that the difference between the CPI at 15% of the duration and the final CPI is no more than 10%. CPI stability depends to a great extent on the quality of the original budget and the ability of the project manager to correct deviations and stick to the plan. These two factors vary widely across industries, companies, and even teams and individuals. Therefore, the authors feel quite comfortable with their finding, which, in fact, states that one has to wait until after 60% of the project elapses in order to be fairly certain of what the final cost will be. Concluding Remarks In this paper, the issue of how to estimate the final project cost and when the estimate becomes accurate was addressed. Although the models considered have been mentioned in literature, this is the first empirical study that carried out a numerical comparison. The authors’ analysis showed clearly that the most accurate estimates are those March 2000 obtained under the assumption that cost deviations will continue at a constant rate. The subsequent stability analysis suggests that the accuracy of the final cost estimate improves after 60% of the project duration has elapsed. Organizations involved in multiple projects could benefit from carrying out the analysis reported here on their own projects in order to improve cost forecasting capability. The procedure is relatively simple and utilizes data that should be already available, since it is routinely collected as part of the earned value control methodology. Further, the analysis need not be restricted to the five models studied here and could include any number of segments, either duration based or progress based, as well as other organization-specific methods and variables. The analysis was based on a relatively small sample of projects carried out by a single organization. Although the results are consistent with intuition and do not negate previously obtained findings, one should be careful before generalizing them to projects that are very different from those in the sample. In fact, the same research methodology should be applied to larger, more diverse projects in order to validate the findings reported here. References Beach, C.P. (1990, November). A-12 administrative inquiry. Navy Memorandum, p. 6. Chen, M.T. (1996, April). An innovative project report. Cost Engineering, 38. Christensen, D.S., & Heise, S.R. (1993). Cost performance index stability. National Contract Management Association Journal, 25 (1). Fleming, Q.W., & Koppelman, J.M. (1994, November). The essence of evolution of earned value. Cost Engineering, 36 (11). Fleming, Q.W., & Koppelman, J.M. (1995a, May). The earned value body of knowledge. PM Network. Fleming, Q.W., & Koppelman, J.M. (1995b, October). Reengineering the earned value process: From government into the private sector. Proceedings of the 26th Annual Project Management Institute 1995 Seminars & Symposium, p. 7–12. Fleming, Q.W., & Koppelman, J.M. (1996, January). Forecasting the final cost and schedule result. PM Network. Shtub, A., Bard, J.F., & Globerson, S. (1994). Project management—Engineering, technology and implementation. Prentice Hall, p. 374–497. Project Management Journal 57
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Running head: KEY FINDINGS

1

Key Findings
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KEY FINDINGS

2

It is important to note that there are very interesting findings that have been addressed in
the article provided. The first and perhaps important is the fact that the issues of estimating the
cost of the final project and also the estimate being entirely accurate are the primary things that
have been addressed. The article has also carried out the first empirical study as far as the idea of
project management is concerned despite the fact that there is a mention of the models in the
literature part of the article in question. From the analysis that has been given by the author, the
most accurate analysis are those that are obtained particularly under the assumption of the cost
deviations and will continue at a given constant rate.
The stability analysis clearly suggests that the accuracy of the final cost estimate
essentially improves after 60 percent of the final project has actually been completed. All the
organizations that are actually involved in the multiple projects should essentially benefit from
carrying out the analysis reported in this analysis with an objective of improving the forecasting
cost capability. The procedure involved is essentially simple and it is able to utilize all the data
that should actually be readily available.
The...


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