1 Page
The EVA Model of Performance     Measurement for Heterogeneous and     Multi Platform ProgramsAditya Rawal – Associate Con...
Contents 1. Abstract ........................................................................................................
1. AbstractWith a continuously changing business scenario, Enterprises are looking for operational efficiency toremain com...
(Mostly by way of depicting in % completion). At a higher level, project sponsors do not havenecessary visibility on the r...
A screen shot of the actual WBS for the entire program is given in the following figure:Figure 1: Work Breakdown Structure...
No           Task Completion = 0%                       Task Started?                                Yes                  ...
The “Testing” phase of the portal modules is explained as follows.In this case, the planned end date of the testing phase ...
Table 1: Earned Value Analysis for Consumer Portal    Indicators         Value                                   Explanati...
Cost Variance            = EV – AC                         = - 78.49 PMSchedule Variance        = EV -PV                  ...
Original       Actual        Expected     Actual      Cost        Sch.                 Estimate       Effort        %     ...
There could be the following inference from this:    1. The Module has been able to manage with less resource and at the s...
6. Author’s Profile:                  Aditya Rawal, PMP has 12 years of experience in managing                  many mediu...
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ISS_1

  1. 1. 1 Page
  2. 2. The EVA Model of Performance Measurement for Heterogeneous and Multi Platform ProgramsAditya Rawal – Associate Consultant, TATA Consultancy Services Limited
  3. 3. Contents 1. Abstract ........................................................................................................................................... 4 2. Business Challenge ........................................................................................................................ 4 3. Solution Approach ........................................................................................................................... 5 4. Conclusion..................................................................................................................................... 12 5. Bibliography................................................................................................................................... 12 6. Author’s Profile: ............................................................................................................................. 133 Page
  4. 4. 1. AbstractWith a continuously changing business scenario, Enterprises are looking for operational efficiency toremain competitive and to track the relative performance of modules/projects within a large program.The traditional method of comparing the performance of multiple projects or modules in aprogram has inherent flaws because of information inconsistency, heterogeneity and incompatibledata.This paper highlights the challenges in tracking the relative performance of multiple Modules orProjects in a large Program and discusses the practical implementation of the EVA Model andPerformance Dashboard for effective program performance monitoring and control aiding thesupervisor to take corrective action.The EVA Model and Performance Dashboard provide the framework to isolate and insulate thedifferences among modules by decoupling their intrinsic complexity and by providing commonplatform based on similarity or equivalence.The performance Dashboard ranks individual modules based on their performance on schedule andon cost budget front.The ranking of the modules is derived from the Earned Value Analysis (EVA) model based on effortconsumed, planned and actual completion.This entire model is based on actual verifiable data. The Four most important aspect of this are:1. Actual Cost: This is the cost which is actually consumed while performing a task.2. Budgeted Cost: The baselined cost budget defined at the time of project Start up3. Expected % Completion: This is derived from the Planned End date of each task4. Actual % Completion: This is derived from the actual end date of the task.2. Business ChallengeCase Scenarios which can be found commonly in heterogeneous programProject Manager A to Project Manager B: “I am managing a project having five Modules of varyingcomplexities and five leads reporting to me. All of them are doing good progress, but there is nocommon yardstick for me to measure their relative performance”Module Lead: “I am not comfortable with my team members sharing the status of their work by wayof “% completion”. I cannot rely on their value judgement.Project Sponsor: “I am not able to take any corrective action based on the project report that isgetting submitted by Project Manager. Instead of making any value judgement I wish there were toolsavailable to me to decide on my resource allocation priorities”Challenges of tracking performance in a ProgramThe biggest challenge which the Project Sponsors and Project Manager face today is to gauge therelative performance of multiple modules or Projects within a large Program. Project leads come upwith their own effort estimation and have their own method of arriving at the project completion status4 Page
  5. 5. (Mostly by way of depicting in % completion). At a higher level, project sponsors do not havenecessary visibility on the relative performance of the modules.Availability of this information can assist the stakeholders in taking timely corrective actions likeadding or withdrawing resources and or identifying training needs or technical help.Just as an athlete cannot make it to the Olympics without extraordinary physical ability, a goodManager must master the four dimensions of Project Management to be successful. (Gerstenmaier)3. Solution ApproachProgram Managers and Sponsers in today’s challenging business environment need a tool foreffective decision making. This tool could be in the form of a Dashboard, which can rank individualmodules or Projects based on their performance on schedule and cost budget front.The ranking is derived from the Earned Value Analysis (EVA) model based on actual effortconsumed, effort planned and actual completion of work.The entire model is based on actual verifiable data. Four most important aspects of this model are:1. Actual Cost: This is the cost which is actually consumed when the module or project gets completed.2. Budgeted Cost: The baselined cost budget defined at the time of project Start up.3. Expected % Completion: This is derived from the Planned End date of each task.4. Actual % Completion: This is derived from the actual end date of the task.The EVA model functions by breaking down each module into task and creating detailed WorkBreakdown Structure (WBS).While defining Work Breakdown Structure in a large program, the most important aspect is thegranularity to which tasks are to be defined. To put point in perspecive, we take the example of alarge Program for Utility domain. There were 15 modules. Each module was of a size of a smallproject (Around 200 Person Months)A detailed WBS is prepared for each module. In order to sufficiently decompose the work into propergranularity, inputs from the junior most team member is taken.5 Page
  6. 6. A screen shot of the actual WBS for the entire program is given in the following figure:Figure 1: Work Breakdown StructureAgainst each task, there is a Planned Start and Planned End Date and Actual Start and Actual Enddate arrived at based on the effort estimation. Each task has a field for “% completion”. There is acalculated field called “Expected % - Final”.The progress of the task is being tracked on a daily basis. Conventionally Project or Module leadsare used to taking “% completion” status from each team member for the task asigned to them.Thishas a inherent element of ambiguity and judgemental error.In the proposed model Instead of askingfor “% completion for each of the task from the task owner, binary questions are asked.The taskowner has only the option of answering in a “Yes” or a “No”. Based on the response only threediscrete values (0%, 33% and 100%) is entered against “Actual % Completion” column. Here6 Page
  7. 7. No Task Completion = 0% Task Started? Yes No Task Completed? Task Completion = 33% Yes Task Completion = 100%Figure 2: Binary logic for tracking Actual percentage completionHas the task started? Yes/No?If No, then assign the task a % completion of 0%If the answer is Yes, then another question, Has the task completed? Yes/No?If No, then assign the task a % completion of 33% else 100%Thus the “Actual % completion” having either 0%, 33% or 100% rolls up to a discrete figure at modulelevel based on the weighted average logic based on the planned start and end date. (The tool usedhere is Micrsoft Project). Based on the planned start and end date of each task, an “Expected %”completion is arrived automatically for each task by comparing the system date with the planned enddate. If the system date is greater or equal to the planned end date then the “expected %” completionfor that task turn to 100% which again rolls up to a discrete value at module level. Thus for eachmodule on a daily basis, there is expected and actual % completion data which keeps on changingThe advantage of this model is uniformity in gauging the % completion across every module. Allindividuals are different and have a unique view point with respect to assessing the % completion ofthe task he or she is performing. Somebody may be too conservative when sharing the % completionof a task while the others may be too aggressive.Since each module is broken up into the appropriate level of granularity into tasks and sub tasks, andeach task is assigned values based on binary feedback, the % completion when it rolls back at themodule level gives a fairly accurate picture of the module % completion.Consumer Portal Module is taken as an example for the case study.7 Page
  8. 8. The “Testing” phase of the portal modules is explained as follows.In this case, the planned end date of the testing phase for most of the tasks has gone past the currentdate which is the system date so the Expected % comletion is being shown as 100% for them. Onlyfor sub phase “Performance Testing”, the planned end date is a future date. Hence the expected %completion for that sub phase is shown as 0%.For the completed tasks a percentage completion of 100% is assigned.The rest are “work in progress” or have not started yet. Hence, a % completion of 33% and 0%respectively is assigned to those tasks. When the task rolls up at the phase level, we get a %completion is 34%.Figure 3: WBS for testing phase of Consumer PortalAt first sight it may look as though this may give erraneous result. Because if task definiation doesn’thave uniform granularity, the % completion may be deciptive. However this is not the case. Since theindividual tasks roll up to a discrete number.The detailed EVA analysis for the portal is explained in the following table:8 Page
  9. 9. Table 1: Earned Value Analysis for Consumer Portal Indicators Value Explanation We should have done 39.59 Person Months worth of Work till date (April Planned Value 39.56 2011 End) Earned Value 36.51 We have actually completed 36.51 Person Months worth of work till date Actual Cost 115 We have consumed 115 Person Months till date Budget at Completion 43 Our original Budget to complete the activity is 43 Person Months Cost Variance -78.49 We are lagging behind the schedule by 78 person months. Cost Performance Index (CPI) 0.31 We are only getting 31 Paisa out of every Rupee we put into the project Schedule Variance -3.04 We are lagging behind the schedule by 3.04 person months. Schedule Per. Index (SPI) 0.92 We are only progressing at 92% of the rate planned Estimate at Completion 135.45 At completion we would have spent close to 135 PM worth of effort. Estimate to Complete 20.45 We need further 20 PM worth of effort to reach to completion Variance at There is a variance of 92.45 PM worth of effort between our planned and Completion -92.45 actual effortPlanned value PV = Expected % completion X Budget at Completion (Effort Estimate) = 0.9199*43 = 39.56 PMEarned Value EV = Actual % completion X Budget at Completion (Effort Estimate) = 0.849*43 = 36.51 PMActual Cost AC = the total cost consumed in person months = (Number of associates X Elapsed days)/22 = (8 X 316)/22 = 115 PM9 Page
  10. 10. Cost Variance = EV – AC = - 78.49 PMSchedule Variance = EV -PV = -3.04 PMCost PerformanceIndex =EV/AC = 0.317Schedule PerformanceIndex =EV/PV =0.92Estimate atCompletion = Budget at completion/CPI = 135 PMThe Performance DashboardBoth Cost Performance Index and Schedule Performance Index are two sides of a same coin.Once the Actual and Expected % completion is calculated for all modules, the same is represented inthe Performance Dashboard as shown in the following table. An average of the same is arrived at,based on which, a Ranking is given to each module.Consumer portal is highlighted in Red in the following table: Table 2: Performance Dashboard Original Actual Expected Actual Cost Sch. Estimate Effort % % Perf. Perf. Modules (PM) cons.(PM) Comp. Comp. Index Index Average RANK Beehive 15 7 91.05% 85.62% 1.83 0.94 1.385 1 Unified Content Management 32 31 82.11% 82.11% 0.84 1 0.92 2 SOA 53 92 97.62% 86.51% 0.49 0.88 0.685 3 MDM 65 83 99.22% 77.12% 0.6 0.77 0.685 310 Page
  11. 11. Original Actual Expected Actual Cost Sch. Estimate Effort % % Perf. Perf. Modules (PM) cons.(PM) Comp. Comp. Index Index Average RANK Identity and Access Management 30 35 94.99% 71.72 0.61 0.75 0.68 5 Business Intelligence 45 104 98.56% 93.17% 0.403 0.94 0.6715 6 Consumer Portal 43 115 91.99% 84.90% 0.31 0.92 0.615 7 Data Center 89 179.167 100.00% 79.09% 0.39 0.79 0.59 8 GIS 184 285 65.05% 45.78% 0.29 0.7 0.495 9 High Within budget but Behind Within Budget and on Schedule Schedule. (Beehive) CPI Behind Schedule and On Schedule but budget Low budget overrun (GIS) over run. (Portal) Low High SPI Figure 4: Schedule-Cost Budget QuadrantIn this case, the Portal team had done extreme detailing in the WBS and following the philosophy of0, 33 and 100 arrived at actual work completion of 84.9% against expected work completion of91.99%.The team was more or less on target as far as meeting the deadline was considered. However, indoing so, it overshot the budget. By April 2011, the actual cost budget consumed was 115 PM againstthe actual work completion of 39.56 PM. Based on this, estimate at completion was calculated and itarrived at 135 PM. The portal module went live in August 2011. Till that time, the actual effortconsumed came to 139 PM. Thus, this model gave an accuracy of +/- 2%.There is another example of “Beehive” module explained as follows:In this module, the Cost Performance Index was 1.35, meaning the cost budget was grosslyunderutilized. Also the Schedule Performance Index was 0.94 indicating that the module was as perthe schedule.11 Page
  12. 12. There could be the following inference from this: 1. The Module has been able to manage with less resource and at the same time adhere to the schedule. 2. The Module has buffer available to take on additional tasks. 3. Effort estimation for the module was incorrect4. ConclusionThe EVA Model for Performance Dashboard can be used as an instrument for standardization forperformance management across a large program. The EVA Model is able to address variousaspects of dissimilarities in modules by using a common logic of schedule and cost tracking. Effectivemanagement, organizational discipline, and structured approach would be required to sustain thebenefits identified by the model.It can help the sponsors take decisions on: 1. Identifying the modules/projects which are adhering to the schedule but are over staffed. 2. Identifying the modules which have been slipping on both schedule and cost front. This will give fair amount of indication about the experience level and skill sets of resources, there aid in identifying the training needs for the team members. 3. Identifying the modules for which the effort estimation done in the beginning was flawed there by having either too much or too little buffer. 4. Giving a fairly accurate prediction of additional effort and cost needed to complete the Project or Program. 5. This model is applicable across various industry segments.5. Bibliography 1. A Guide to the Project Management Body Of Knowledge (PMBOK® Guide), Fourth Edition by Project Management Institute 2. Rita Mulcahy PMP Exam Preparation, Chapter on Time and Cost Management.12 Page
  13. 13. 6. Author’s Profile: Aditya Rawal, PMP has 12 years of experience in managing many medium and large projects in Utility domain. Currently he is managing a Large program for Power reforms in India. Email: aditya.rawal@tcs.com; adityarawal@gmail.com13 Page

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