International Conference on Advances in Structural

International Conference on Advances in Structural and Geotechnical Engineering

ICASGE’17 27-30 March 2017, Hurghada, Egypt

DO EARNED VALUE MANAGEMENT (EVM) ANALYSIS SCHEMES HAVE SPORADIC SIGNIFICANCE? Khaled Nofal 1, Tamer El-Korany 2, Emad E. Etman3 , Salah El-Din F. Taher 4 1

Practitioner Engineer, MS Candidate, Faculty of Engineering, Tanta University, Egypt E-mail: [email protected] 2 Assistant Professor, Faculty of Engineering, Tanta University, Egypt E-mail: [email protected] 3 Professor, Faculty of Engineering, Tanta University, Egypt E-mail: [email protected] 4 Professor, Faculty of Engineering, Tanta University, Egypt E-mail: [email protected]

ABSTRACT Many analysis schemes for Earned Value Management (EVM) were previously proposed by several investigators based on stochastic or deterministic basis. Although EVM was set up to follow time and cost, the majority of the researches have focused on the cost aspect alone. Nevertheless, EVM provides two well-known schedule performance indices: the schedule variance and the schedule performance index. These two measures are useful indicators to analyze a project’s performance. A comparative study presented earlier by the authors indicated sporadic predictions of some methods. The present study further examines the consistency of selective EVM analysis schemes for a case study. Method of Earned Schedule (ES), Fuzzy approach method, Planned Value method, Earned Duration method, the Linked EVM with time based schedule method and Active Floating Time method (AFT) have been implemented for this purpose. The predictions of the examined schemes showed that consistency of the EVM analysis schemes was not always guaranteed. Another prospective of the existing schemes is that consideration need to be further devoted to incorporate quality along with cost and time for a more comprehensive EVM for holistic performance measure. Keywords: Management, Construction projects, Earned value, Cost control, Fuzzy approach.

INTRODUCTION Constructions projects in over the world have faced many problems related to the planning, monitoring and controlling the projects which make many projects suffered from cost and time overruns. So the perfect management of the project cost is very important to ensure the projects remain on its planned schedule, within the planned budget and achieving other project objectives during any project implementation stages [1-10]. Project cost management consists of four major processes as shown in the following Figure (1) and may be interpreted as follows: • Resource Planning involves determining the physical resources like materials, equipment and labor and the quantities of each which should be used to perform project activities. This process requires the expert judgment which identifies the exact resources and its quantities for any element of the work breakdown structure.


•

• •

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Cost Estimating involves developing an approximation measurement of the costs of the resources whether materials, equipment and labor which are needed to complete the project tasks and activities. Comparing the project to previous efforts, historical data and statistical models are different methods to develop estimates but it remains that producing effective and realistic estimates can be reached by taking into consideration very important factors like risks related to the project tasks and activities, current project environment and customer psychology. Cost budgeting involves combining the overall cost estimates of individual work items to develop a cost baseline which is defined as a time-phased budget that will be used for measuring and monitoring the project cost performance. Cost Control is the most important process in the project cost management. It isn’t just the process of calculating the variances between actual cost of project activities and the assigned budget to accomplish the same activities. It is a comprehensive process which concerned with the feedback that create changes to any or all future plans and production methods and managed these changes when they occur whether have positive or negative influence on the project cost baseline.

Fig. (1): Project Cost Management Overview Earned Value Management (EVM) is a technique used for controlling and monitoring projects [9, 10]. EVM provides cost and schedule performance measurements by comparing actual accomplishment of scheduled work and associated cost against the planned schedule and approved budget and then computing the delays and the budget overruns [2,3]. Building blocks of all EVM metrics originally considered by Lipke [11] were the following three elements [12] and it is shown in the following Figure (2): • Earned value (EV) or budgeted cost of work performed (BCWP): it is the budgeted amount for the work actually completed on the schedule activity or work breakdown structure (WBS) component during a given time period. •Planned value (PV) or budgeted cost of work scheduled (BCWS): it is the budgeted cost for the work scheduled to be completed on an activity or WBS component up to a given point in time. •Actual cost (AC) or actual cost of work performed (ACWP): it is the total cost incurred in accomplishing work on the schedule activity or WBS component during a given time period.

Fig. (2): Detailing of Earned Value Management metrics [8]

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Previous study by the authors [1] indicated that the EVM analysis schemes led to different conclusion on their case study. This motivated for an extensive research on the significance of the predictions of the various EVM procedures which is the scope of the present work. • Method of Earned Schedule (ES) introduced by Walter Lipke [4] as an extension to the theory of EVM which measures the project schedule performance in time units rather than cost. • Fuzzy approach method introduced by Moslemi naeni et al [6,7] to evaluate the EVM indicators and make them more connected to the reality. In most EVM techniques the data of the activities are represented in a deterministic value without taking into consideration the uncertain nature of their progress and errors which is happened due to people’s biased judgments. • Planned value method described by Anbari [13]. This method provides the possibility to translate the schedule variance into time units by calculating time variance based on schedule variance, budget at completion and project duration. • Earned duration method described by Jacob [13] which is similar to the planned value method but the time variance (TV) is calculated based on the schedule performance index in the data date. • Linked EVM with time based schedule method introduced by Amir Najafi [2] for the connection between the EVM technique and a time based schedule method like the Critical Path Method (CPM). • Active Floating Time (AFT) method introduced by Amir Najafi [2] for the connection between EV, PV and total float time for the project activities A case study is considered herein for judging the significance of the predictions of these methods whether consistent or sporadic.

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EVM ANALYSIS SCHEMES Table (1) summarizes the particular analysis schemes for EVM considered in the present study. The Table gives brief description of the theory and procedure including the calculation equations. Table 1: Theory and procedure of selective EVM analysis scheme EVM Scheme

Theory

Procedure

Projecting the cumulative EV to the cumulative PV and getting the time related to EV form the PV S-curve which represents earned schedule. Earned Schedule: ES = N + (EV – PVn) / (PVn+1 – PVn) Schedule Variance: SV (t) = ES – AT. SV (t) is negative when the project is behind schedule and positive when the project is ahead schedule. Schedule Performance Index: SPI (t) = ES / AT. SPI (t) is the rate of project progress compared with the planned progress. Where AT is the actual time

Earned Schedule [4]

First: Estimate the percentage of work completion for each activity including uncertainty. Second: express this percentage by using linguistic terms. Third: transform the linguistic terms to a fuzzy number F (i) = [a, b, c, d].

Fuzzy approach [6, 7]

Fourth: use one of the following methods to compare the fuzzy performance indicators against 1 like the traditional EVM indicators. A - Alpha cuts (⍺ -cut) Estimate the value ⍺ which represents the gap between past estimates and the actual values of the project duration and cost and then The right-hand-side of ⍺-cut of fuzzy performance indicators are determined and compared against 1.

⍺SPI = [ E4 - ⍺ ( E4 - E3 ) ] / PV ⍺SPI < 1 ⍺SPI > 1

0≤ ⍺ ≤1 behind schedule Ahead of schedule

B - The degree of possibility table Case Explanation d 0 - So it is appeared that the project becomes ahead of schedule after 20 days.

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f. solution by Active Floating Time (AFT) After 4 days. Activity

EV

AFT Formula

AFT = TF – [(AS − PS) + (AT − AS) - ((EV/B)*D)] = 0 – [(3-3) + (4-3) – ((5000/50000)*6)] = - [ 0 + 1 – 0.6 ] = -0.4 AFT = TF – [(AS − PS) + (AT − AS) - ((EV/B)*D)] C 22500 = 4 – [(3-3) + (4-3) – ((22500/30000)*2)] = 4 – [ 0 + 1 – 1.5 ] = 4.5 AFT = TF – [(AS − PS) + (AT − AS) - ((EV/B)*D)] D 5000 = 2 – [(3-3) + (4-3) – ((5000/20000)*4)] =2 - [ 0 + 1 – 1 ] = 2 - So it is appeared that the project is behind schedule B

5000

AFT

AFT min

-0.4 4.5

-0.4

2

After 8 days. Activity

EV

B

41666.67

E

1000

AFT Formula AFT = TF – [(AS − PS) + (AT − AS) - ((EV/B)*D)] = 0 – [(3-3) + (8-3) – ((41666.67/50000)*6)] = -[ 0 + 5 – 5 ] = 0 AFT = TF – [(AS − PS) + (AT − AS) - ((EV/B)*D)] = 2 – [(7-7) + (8-7) – ((1000/10000)*4)] = 2 – [ 0 + 1 – 0.4 ] = 1.4

AFT

AFT min

0 0 1.4

- So it is appeared that the project is on schedule After 16 days. Activity

EV

G

13400

H

32000

AFT Formula AFT = TF – [(AS − PS) + (AT − AS) - ((EV/B)*D)] = 2 – [(13-13) + (16-13) – ((13400/20000)*3)] =2-[0+3–2]=1 AFT = TF – [(AS − PS) + (AT − AS) - ((EV/B)*D)] = 0 – [(13-13) + (16-13) – ((32000/80000)*5)] = 0 – [ 0 + 3 – 2 ] = -1

AFT

AFT min

1 -1 -1

- So it is appeared that the project is behind schedule After 20 days. Activity

EV

AFT Formula

AFT

AFT min

K

16000

AFT = TF – [(AS − PS) + (AT − AS) - ((EV/B)*D)] = 0 – [(18-18) + (20-18) – ((16000/40000)*6)] = 0 - [ 0 + 2 – 2.4 ] = 0.4

0.4

0.4

- So it is appeared that the project is ahead of schedule.

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SIGNIFICANCE OF PREDICTIONS OF EVM ANALYSIS SCHEMES The results of the case study is tabulated below, Table (3), where the inconsistency of the predictions of the considered analysis schemes is obvious. On the other hand the first four methods show agreement in prediction. This means that the analysis by a particular analysis scheme should be examined for practical implementation. Table 3: Predictions of EVM analysis schemes EVM Scheme

Case Study

Earned Schedule [4]

- Ahead of schedule (After 4 days). - Behind schedule (After 8, 16 days). - Ahead of schedule (After 20 days).

Fuzzy approach [6, 7]


Planned Value [13]


Earned Duration [13]


Linked the EVM technique with a time based schedule method like CPM [2]

Active Floating Time (AFT) [2]

- Behind schedule But for non-critical activities ahead of their schedule (After 4 days). - On schedule for critical activities But for non-critical activities too behind of their schedule. (After 8 days). - Behind schedule (After 16 days). - Ahead of schedule (After 20 days). - Behind schedule - On schedule - Behind schedule - Ahead of schedule

(After 4 days). (After 8 days). (After 16 days). (After 20 days).

Furthermore, the data presented earlier by the authors [1] showed more sporadic results among all of the considered EVM analysis schemes which further assured that consistency of predictions of various schemes are not always guaranteed. It has to be noted that none of the considered EVM analysis schemes took into account the quality issue. Therefore, a new trend is needed for a generalized performance measurement scheme in terms of cost, time and quality by incorporating a multiplier to the EVM reflecting the control of the project. This could be done through any method such as six-sigma, Alferado Pareto method or defects per million opportunities (DPMO).

CONCLUSION Earned Value Management is a method of performance measurement which reflects the real project cost and schedule but it has some limitations in analyzing the project schedule performance and measuring its actual progress. These limitations can be solved by using different EVM analysis schemes. From the calculations presented in

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this paper for a case study, it was shown that the methods did not give the same results for all time intervals of the considered case study. Predictions were sporadic for the Case Study. Using the linked between the EVM technique and a time based schedule method like CPM and Active Floating method (AFT) [2], the results were not consistent with other methods in all situations. The reason of this was because there were activities which were planned to be accomplished in the future but were performed ahead of other activities on the critical path so the earned schedule, fuzzy approach, planned value and earned duration methods didn’t provide accurate EVM schedule indicators. However, predictions of particular EVM analysis scheme should be verified in large projects. Another prospective of the existing schemes is that consideration need to be further devoted to incorporate quality along with cost and time for a more comprehensive EVM for holistic performance measure.

REFERENCES 1. Nofal K., El-Korany T. M., Taher S. E. F, 2016, "Comparative Study of Earned Value Analysis-Based Approaches". 9th Alexandria International Conference of Structural and Geotechnical Engineering, AICSGE9, 19-21 Dec. 2. Amir Najafi and Fatemeh Azimi, 2016, "An extension of the earned value management to improve the accuracy of schedule analysis results”, Iranian Journal of Management Studies (IJMS), Vol. 9, No. 1, Junuary2016. 3. Sandhya Suresh and Ganapathy Ramasamy N,2015, "Analysis of Project Performance Using Earned Value Analysis”, International Journal of Science, Engineering and Technology Research. 4. Walter Lipke, 2003, " Schedule is Different”m The Measurable News, 31-34. 5. Kym Henderson,2003, "Earned Schedule: A Breakthrough Extension to Earned Value Management”. 6. Leila Moslemi Naeni, Shahram Shadrokh and Amir Salehipour, 2011, "A fuzzy approach for the earned value management”, International Journal of Project Management. 7. Leila Moslemi Naeni and Amir Salehipour, 2011, "Evaluating fuzzy earned value indices and estimates by applying alpha cuts”, Expert Systems with Applications. 8. Antony Prasanth M and K Thirumalai Raja,2014, " Project Performance Evaluation By Earned Value Method”, International Journal of Innovative Research in Science, Engineering and Technology. 9. Javier Pajares and Adolfo López-Paredes,2011, "An extension of the EVM analysis for project monitoring The Cost Control Index and the Schedule Control Index”, International Journal of Project Management. 10. Abdel Azeem S.A. , Hossam E. Hosny and Ahmed H. Ibrahim,2013, "Forecasting project schedule performance using probabilistic and deterministic models”, Housing and Building National Research Center. 11. Lipke, W., 1999, “Applying management reserve to software project management”. Defense Software Engineering, March 17-21. 12. Kerzner, H.R., 2013, "Project Management: A Systems Approach to Planning, Scheduling, and Controlling", 11th ed. John Wiley & Sons. 13. Stephan Vandevoorde, Mario Vanhoucke, 2005, "A comparison of different project duration forecasting methods using earned value metrics ", International Journal of Project Management.

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