LEADING EDGE FORUM CSC PAPERS 2012 EARNED SCHEDULE: FROM EMERGING PRACTICE TO PRACTICAL APPLICATION

Transcription

1 LEADING EDGE FORUM CSC PAPERS 2012 EARNED SCHEDULE: FROM EMERGING PRACTICE TO PRACTICAL APPLICATION Author Bill Mowery MPM, PMP Keywords Earned Value Management, Earned Schedule, Project Management, Cost Performance Index, Schedule Performance Index, Project Recovery, Earned Duration, Project Forecasting, Cost/Schedule Control System Criteria ABSTRACT As technology expands at an ever increasing pace we are challenged to efficiently apply and utilize it in ever shorter cycles of new products, processes, and methods, with the ultimate goal of achieving business objectives. Accurate project performance metrics provide not only vital data for project managers but also for business executives charged with managing complex portfolios of projects The ability to forecast conditions at project completion and to develop corrective actions facilitates the proper balance of schedule and budget priorities for the project manager and assists business leaders in achieving the optimum balance of risk, return, and value. All projects regardless of their industry or nature inevitably are faced with two fundamental questions: are we on budget, and are we on time? As technology and project complexity increased in the 1950 s it became apparent that traditional methods of measuring project progress were not working, particularly on large defense projects. The United States Department of Defense s efforts led to the birth of Earned Value Management, EVM, to measure and forecast project cost and schedule performance by using ratios of work value, work accomplished, and cost of work. While time has shown that EVM s budget metrics are very reliable, the same can t be said for corresponding schedule metrics. Earned Schedule, an extension to Earned Value Management, is an emerging practice in project management that offers significant improvement in monitoring and forecasting project schedule performance. Earned Schedule offers methods that are straightforward yet powerful while simplifying reporting and communication across all levels of an organization. Why do project managers at times say the project is x dollars behind schedule? The answer is simply because Earned Value techniques calculate time in terms of money. Earned Schedule provides schedule quantities directly in units of time. Why do some projects reach 99% complete and remain there forever? The answer is that Earned Value schedule measurements have limitations in measuring late projects and projects that are past their planned end dates, with subtleties that aren t always well understood by project managers. Earned Schedule provides improved accuracy over the project s entire duration, while overcoming a major limitation of EVM by providing data for projects that have exceeded their planned end date. This paper s target audience is the intermediate to advanced level project management practitioner, although the introduction to Earned Value Management and Earned Schedule is very useful for a more general audience. The paper provides background information on Earned Value Management (EVM) and introduces the Earned Schedule (ES) method. The two methods are compared with both theoretical and real world project data, demonstrating that Earned Schedule provides significant management benefits over EVM. Cautions and caveats for the project management practitioner for both methods are discussed. Improvements offered by Earned Schedule are shown with a retrospective analysis of an actual project. Equations and examples for enhanced project forecasting and recovery are given with example applications. 1 INTRODUCTION Earned Schedule (ES), an extension to Earned Value Management (EVM), is an emerging practice in project management that offers significant improvement in monitoring and forecasting project schedule performance. Among the benefits of Earned Schedule are the expressions of schedule variances directly in units of time, improved metrics accuracy in the latter periods of project 2012 Computer Sciences Corporation. All rights reserved.

2 performance, and meaningful project metrics for the entire period of project performance. Earned Schedule overcomes a major limitation of EVM analysis by providing meaningful metrics for late projects, i.e. projects that have exceeded their planned end date. The paper presents as background for the discussion of Earned Schedule a brief review of the evolution and use of Earned Value Management (EVM) and some of its limitations in measuring and predicting project schedule performance. The paper next provides an introduction to Earned Schedule theory and application, with example calculations and a comparison with EVM results for both example and actual project data, followed by a discussion of some challenges and issues with Earned Value methods that affect both traditional EVM and ES. A section on the integration of Earned Schedule methods with existing CSC project management tools demonstrates how to derive immediate benefit in a practical application of the new methods. The paper concludes with conclusions and directions for further research and application of Earned Schedule. 2 PROJECT SCHEDULE PERFORMANCE METRICS AND FORECASTING 2.1 EARNED VALUE MANAGEMENT OVERVIEW Project management emphasizes management of the classic triple constraints of scope, budget, and schedule. Budget and schedule management focus on monitoring status and forecasting future performance against an approved project plan. As projects in the defense industry in the 1950s increased in technical complexity and size, measuring progress using simple ratios of money and time expended versus money and time planned without considering the actual amount of project work accomplished resulted in some significant overruns of budget and schedule. The demand for more sophisticated management tools led to the Program Evaluation and Review Technique (PERT) in the defense industry and its related Critical Path Method (CPM) in the construction industry. With a focus on analysis of resource loaded networks, PERT and CPM proved to be important early methods to address management reporting concerns and provided the earliest methods of Earned Value Management. While providing a significant improvement, the challenges in the complexity of the PERT method before computerized tools were generally available, attempts to make one set of tools fit all project types, and administrative challenges within the government resulted in limited industry adoption of these tools. Building on experience gained from PERT, the United States Air Force formed the Cost / Schedule Planning and Control Specifications group in Shifting focus from requiring contractors to adopt a single method to requiring adherence to specific criteria led to the development of the Cost/Schedule Control System Criteria (C/SCSC) in 1967 (Fleming, 1988). In addition to establishing a new collaborative approach between the defense department and private industry, C/SCSC was a major factor in the beginning of the widespread application of EVM (Abba, 2000). There are many excellent references on Earned Value Management available (Fleming & Koppelman, 2000) so only a brief overview of EVM concepts is presented in this paper. In discussing EVM topics this paper uses the current terminology found in the latest version of the Project Management Institute s A Guide to the Project Management Body of Knowledge (Project Management Institute, 2008). Readers may be more familiar with older versions of terminology that are still commonly in use. Some of the terms where differences exist include: Budgeted Cost of Work Scheduled (BCWS) / Planned Value (PV) the amount of work planned to be accomplished at a point in time or cumulatively. Actual Cost of Work Performed (ACWP) / Actual Cost (AC) the amount of money or resources expended in order to accomplish the amount of work achieved for the reporting period. Budgeted Cost of Work Performed (BCWP) / Earned Value (EV) the amount of planned work actually accomplished or achieved, or the value of work earned. Earned Value Management establishes a time-phased Performance Measurement Baseline (PMB) for assessing the progress of project objectives and compares the resources expended at a point in 2012 Computer Sciences Corporation. All rights reserved. 2

3 time to the actual amount of work accomplished. While measurement may be defined in units appropriate for a particular project, the PMB is usually expressed in terms of money spent. When considering EVM techniques it is important to maintain the distinction between the value of work and the cost of work. Many projects express the value of work in terms of money, and of course, cost is likewise expressed in the same terms. But while it is logical to express cost as money, expressing the deliverable of a project in terms of money is in reality an indirect reference, since the actual project deliverable is a good or service, and that good or service tendered is in fact the objective of the project. In project planning there is a logical reason to consider the cost and value synonymous, but once the project is under way and attention turns to monitoring progress the difference between cost and value should be quickly apparent. Consider a hypothetical project with the objective of installing 18,000 feet of fiber optic cable over 18 months (Planned Duration) at a total cost (Budget at Completion) of $231,280. Table 1 shows the Performance Measurement Baseline for periodic and cumulative values for the example. Table 1. Example Performance Measurement Baseline Now let s determine the project s status after 12 months. Suppose that 12,326 feet of cable are installed at a total cost of $179,820. Earned Value is calculated as the ratio of work accomplished to total work planned: We also find that we have spent $179,820 (Actual Cost). Considering only cost alone would lead us to believe that the project is $179,820 / $231,280 = 77.75% complete without considering that at the current spending rate using the entire budget would not allow installation of all cable planned, but only ft. Looking strictly at work implies that the project is 12,326 / 18,000 = 68.48% complete without recognizing the unpleasant truth that installing the planned 18,000 feet of cable at current actual rates is likely to cost or $31,316 over budget. The integration of cost and schedule information relative to actual work accomplished is a major advantage of EVM over previous methods that considered the factors separately. As a project progresses EVM analysis compares the relationship of Planned Value (PV), Earned Value (EV), and Actual Cost (AC) over time as shown in Figure 1. The Planned Value curve as defined by the PMB terminates at Budget at Completion (BAC), the project s total planned value. In understanding the limitations and mechanisms of EVM schedule metrics it is important to keep in mind that project completion is defined as EV = PV = BAC Computer Sciences Corporation. All rights reserved. 3

4 Figure 1. Earned Value Performance Curves and Equations1 The Actual Cost and Earned Value curves are plotted based on actual measurement of project performance, and using these values, we begin to develop metrics for analysis and reporting. We find Cost Variance from Equation 1, with a positive result indicating under budget performance and we find Schedule Variance similarly by Equation 1. Earned Value Cost Variance Equation 2. Earned Value Schedule Variance EVM uses performance indices, ratios of actual and planned values, to describe efficiency of schedule and budget achieved and to forecast future performance. The most common of these indices are the Cost Performance Index (CPI), calculated by Equation 3. Equation 3. Earned Value Cost Performance Index and the Schedule Performance Index, shown in Equation 4. Equation 4. Earned Value Schedule Performance Index A performance index value greater than one indicates better than planned actual performance and a value less than one indicates less than planned performance. Using these performance indices, next 1 Figure 1 was updated July Computer Sciences Corporation. All rights reserved. 4

5 our attention turns to the questions of how much the project is likely to cost, what the total variance will be, and how long it will take to complete the project. These questions are answered by the Estimate at Completion the Variance at Completion Equation 5. Estimate at Completion and the Estimate at Completion for Time Equation 6. Variance at Completion Equation 7. Variance at Completion, Time Returning to the fiber optic cable installation project, we can see the results of the calculations for each of these equations applied to performance data in Table 2. The EVM equations offer calculations to answer each of the preceding questions in a simple and straightforward manner. Table 2. Example Project Actual Performance While performance indices may be used in various advanced calculations, the CPI value of 0.88 in Table 2 indicates that each dollar spent is producing only 88 cents worth of work, and the SPI value of 0.94 implies that each eight-hour day worked results in only 7.5 hours of effective work. What is the long-term effect of this less-than-optimal performance on project completion? VAC forecasts that the project will finish $31,310 over budget and EAC t predicts finishing late by 1.1 months. A metric related to CPI, the To Complete Performance Index (TCPI), defines the level of performance required to meet an objective and is calculated by with the p subscript indicating that the equation is for baseline plan analysis. In simple terms, what level of efficiency (CPI) must be reached in order to achieve the plan? In addition to defining a target performance level, TCPI has interesting characteristics of its own (Lipke, 2009c). If TCPI is less than 2012 Computer Sciences Corporation. All rights reserved. 5

6 1.0 then the forecast budget is considered achievable. If TCPI is greater than 1.10 then performance is most likely beyond recovery, and detailed reevaluation of the plan is probably in order. Intermediate values require the project manager s increased attention. For the example fiber optic project, Table 2 shows TCPI p is already at 1.42, indicating that it s highly unlikely the project will finish within its baseline budget with current performance levels. We have calculated EAC = $262,590, or $31,310 over budget, but is it probable that the project can complete in less than this latest forecast? Given current performance levels, is it likely we can complete the project for $10,000 less? To answer the question we use an alternate form of TCPI based on EAC: While this represents a significant improvement in current performance levels, TCPI e indicates that the reduced budget is at least a reasonable, if still difficult, goal. 2.2 EARNED VALUE ISSUES AND CONCERNS Although Earned Value Management represents a significant contribution to project management, from its earliest development and adoption limitations were apparent (Fleming, 1988). Experience and further research have shown that while EVM methods for cost are reliable (Fleming & Koppelman, 2003), schedule metrics are not as robust. Among the problems are schedule metrics expressed monetary terms, the lack of reliable metrics for projects whose duration has exceeded the baseline end date, and the unreliability of EVM schedule calculations in the latter stages of a project. While the logical measurement of schedule parameters should be in units of time, EVM expresses schedule variances in terms of money. This anomaly is due to the nature of the original composite view of schedule and budget as shown in Figure 1. The Performance Measurement Baseline (PMB) is represented by PV, terminating with the total planned budget, Budget at Completion (BAC). SV is the difference between points on the EV and PV curves at a given time, and since this difference is taken from the vertical axis, which is in dollars, SV is likewise expressed in dollars. It logically follows that EVM schedule ratios are also computed using dollars (e.g. Schedule Variance percentage, SV% = SV / PV, Schedule Performance Index, SPI = EV / PV). It is easy to see how those not well versed in EVM may not fully appreciate the significance of the phrase The project is x dollars behind schedule. Such a statement typically results in further questions, such as How many days late will the project be? Intuitively and logically, this is an appropriate question, since we think of schedules in terms of time. The answer to the question of how long is answered with ratios of money. As shown in the overview of EVM, converting money to a forecast of project duration uses the equation While the form of the equation above is the version given by the Project Management Institute (Project Management Institute, 2005) it s easy to see that the equation is simplified as 2012 Computer Sciences Corporation. All rights reserved. 6

7 Figure 2 Earned Value Project Completion which seems to be most commonly used. It is worthwhile to note that while the general form of the equation implies the use of EVM s SPI, we can substitute alternate or more complex / compound performance indices in this equation. While this ratio does provide a time based measure it is still limited by its dependence on SPI, a performance measure based on money and bound by the relationship of EV, PV, and BAC. We can see EVM s inability to provide meaningful schedule metrics for projects that have exceeded their planned duration by examining the schedule equations and by practical example. In analyzing the equation for EAC t above, recall that PD and BAC are fixed quantities defined during project planning, while SPI = EV/PV. At a project s baseline completion date PV = BAC as illustrated in Figure2. Likewise at project completion EV = BAC, and we begin to infer that there are limits on how project duration may be forecasted based on the relationship of SPI and BAC. We can analyze the equation using calculus to determine or by looking at the simplified version of the equation we see that when SPI = 1 then EAC t = PD. When a project s baseline end date has passed (PV = BAC) and the value of EV is approaching BAC, the forecasted duration begins to decrease, and eventually reaches its limit of PD once all work is complete (EV=PV=BAC) as illustrated in Figure Computer Sciences Corporation. All rights reserved. 7

8 Figure 3 - Earned Value Performance for Late Projects While the preceding discussion addresses the values that EAC t can have, it does not consider when those values are calculated. Let s examine a scenario for a late project. Assume a project has completed most of its planned work, with EV = 990, PV = 1,000, BAC = 1,000, and PD = 12. These metrics yield SPI = EV/PV = 0.99, which when applied in EAC t = BAC SPI BAC PD gives a final duration of months. Figure 2 shows that as EV approaches the final value for PV, forecasted project duration decreases because the limit for EV is always BAC. Regardless of whether we perform the calculation during performance period ten or fifteen, this cost-based ratio yields the same result, and can show that an in-progress project has a forecasted completion date in the past. Let s examine this inconsistency by returning to our struggling fiber optic cable installation project. The performance data in Table 3 includes EVM metrics and calculations for schedule and budget through month 22, indicating that the project has now completed four months after its originally planned 18-month duration. Cost and schedule variance data are graphed in Figure 3. Note that in month 18 schedule variance begins to improve as EV starts to converge on PV and BAC. Likewise, forecasted duration (EAC t) begins to improve from month 18 forward and indicates in month 21 that the forecasted end of the project, at months, should have been reached 2.68 months ago. The preceding analysis shows that for a late project EVM schedule data is unreliable, which leads to the conclusion that if the metrics are in error for a project that is late then the tendency toward inaccuracy must begin at some point before the completion date Computer Sciences Corporation. All rights reserved. 8

9 Table 3. Late Project EVM Metrics Example At what point, and to what degree, does this inaccuracy affect EVM metrics? Although the nature of the problem makes determining the period and magnitude of the potential error difficult, we can see the existence of inaccuracies in Figure 4. Figure 4. SV / SPI Divergence for a Late Project Typically, we display SPI with only two significant digits, but Figure 4 compares SPI with five significant digits to a corresponding value of SV. Note that in month 14 an interesting trend begins as Schedule Variance, in absolute terms, continues to show worsening performance while SPI begins to show improving performance as it converges to PV/BAC. This contradiction clearly indicates that EVM is providing inconsistent data beginning in month 14 of the project. 2.3 INTRODUCTION TO EARNED SCHEDULE EARNED SCHEDULE CONCEPTS AND CALCULATIONS The problems with Earned Value schedule metrics contributes to the tendency for practitioners to focus on the cost management tools provided by EVM while acknowledging the limitations of EVM s schedule components. The search for better schedule performance metrics led to the development of the Earned Schedule method. Based on Earned Value data, the goal of Earned Schedule is to have a set of schedule indicators which perform correctly over the entire period of project performance (Lipke, 2009a). Walter Lipke provided a formalized description of Earned Schedule and calculations for its use in 2003 (Lipke, 2003) while noting that time-based analysis of the Performance 2012 Computer Sciences Corporation. All rights reserved. 9

10 Measurement Baseline is not a new idea, citing Fleming s foundation book on Earned Value (Fleming, 1988). By utilizing existing Earned Value data for time based measurements, Earned Schedule exhibits consistent performance shown to deliver better results than previous EVM techniques in both a retrospective analysis of a small set of projects (Henderson, 2003) as well as in a study of a broad range of project conditions using computer generated schedule networks (Vanhoucke & Vandevoorde, 2007). In their extensive simulation study Vanhoucke and Vandevoorde compared Earned Schedule s performance to Anbari s Planned Value method (Anbari, 2003) and Jacob and Kane s Earned Duration method (Jacob, 2003) and found that on average Earned Schedule outperforms either method. The concept of Earned Schedule is relatively simple: derive a time based measurement of schedule performance by comparing a project s Earned Value today (Actual Time, AT) to the point on the Performance Measurement Baseline (Planned Value curve, PV) where it should have been earned. The difference between AT and PV represents a true time-based Schedule Variance, or in Earned Schedule notation, SV (t). The derivation of Earned Schedule metrics is shown in Figure 5. Figure 5. Earned Schedule, Adapted from Lipke (Lipke, 2003) In Figure 5 at the end of July (Actual Time, or AT = 7) measured EV actually should have been earned at some point in June, Period 6, seen by mapping Earned Value to its corresponding Planned Value point on the Performance Measurement Baseline. In other words, the portion of the schedule that is actually Earned Schedule consists of the work performed through all of the last full period (May, period 5) and a portion of June. Earned Schedule is calculated by the formula 2012 Computer Sciences Corporation. All rights reserved. 10

11 where C is the number of time increments on the PMB where EV PV, PV c is the value of PV at the last full performance period, and PV c+1 is the value of PV at the end of the partial performance period. Thus, Earned Schedule (ES) in Figure 5 becomes: Note that an interpolation is required to determine ES for partial time periods, represented by the ratio of the Earned Value to the Work Scheduled. It is an easily overlooked point that this ratio is implicitly multiplied by one time unit and is therefore is in units of time and not a simple ratio. This fact has resulted in some differing opinions in existing literature in interpretation of the algebra underlying Earned Schedule (Book, 2006a). Once ES is calculated, Schedule Variance, SV (t), is found by the equation and expressed directly in units of time. A Schedule Performance Index based on time, SPI (t), is found by which is analogous to its EVM counterpart. Returning to the example in Table 2 on page 8 we see that in month 12 work with a value of $168,030 should have been accomplished per plan but only $158,380 worth of work is complete. Let s use Earned Schedule to answer the common questions of What is the schedule status and when will we finish? Using the general form equation for ES we find actual progress as and applying the ES equation for Schedule Variance we find that the project is behind schedule by Although calculation of project duration can take different forms depending on the anticipated future performance of project work (Anbari, 2003), (Vanhoucke & Vandevoorde, 2007) most depend on a performance index. For Earned Schedule in this example we find a performance index by Applying this performance factor with the ES equation for Independent Estimate at Completion for Time (IEAC (t)), the total forecasted project duration is Another metric easily calculated with ES is the To Complete Schedule Performance Index, TSPI. TSPI corresponds to EVM s cost To Complete Performance Index, TCPI, and is used to determine efficiency required to complete according to either a plan or desired target duration. Like TCPI, if < Computer Sciences Corporation. All rights reserved. 11

12 the target is considered achievable, if > 1.10 it is generally believed that the schedule estimate is not achievable, and of course intermediate values would bear close scrutiny (Lipke, 2009a). The equation to determine TSPI for the planned duration is Calculating TSPI P for the example project yields And so it would seem that our example project is facing serious challenges in completing within its 18 month planned duration. The alternate form of the equation given by where ED is the Estimated Duration in question, is used to evaluate the probability of completing a project within an alternate planned duration. Examining the viability of completing the project with an additional month added to the schedule we find This implies an achievable goal, since TSPI e, the minimum performance level required, is less than the current value of Comparing schedule status derived from EVM and ES for the example provides the data shown Table 4. Metric Earned Value Earned Schedule Schedule Variance (SV) -$9, Months Schedule Performance Index (SPI) Forecast Duration (IEAC) Months Months Table 4. Comparison of Earned Value and Earned Schedule Metrics The elegance of Earned Schedule is its calculations based on Actual Time, and unlike Earned Value, will yield a valid result regardless of whether the project has exceeded its planned duration. An additional aspect of ES is that it does not suffer inaccuracies in the latter stages of a project, and in fact it has been demonstrated that as a project progresses the accuracy of ES forecasts improves (Vanhoucke & Vandevoorde, 2007). For comparison with EVM performance, we first revisit the final months of our sample project with Earned Schedule metrics included as shown in Table Computer Sciences Corporation. All rights reserved. 12

13 Table 5. Metrics Comparison for Late Project Unlike EVM s SPI and derived metrics, Earned Schedule s SPI (t) portrays a steadily declining performance trend from month 12 until project completion in month EARNED SCHEDULE CRITIQUES AND CONCERNS While Earned Schedule methods offer promising advances project management, as with any new technique caution is required. Considerations for the potential practitioner include apparent slow industry adoption, viewpoints on alternative methods, and the general caveats pertaining to Earned Value methods in general. Deemed an emerging practice by the Project Management Institute (Project Management Institute, 2005), Earned Schedule is a relatively new method that is not yet supported by the broad base of research and practical use that EVM enjoys. Despite the promising indications provided by existing data (Henderson, 2003), (Henderson, 2005), (Vanhoucke & Vandevoorde, 2007) continuing research, particularly with real world project data, will be required to establish Earned Schedule comfortably among proven project management techniques. Just as it has supporters, Earned Schedule also has its critics. In one of the better known critiques, Book (Book, 2006a), (Book, 2006b) offers some minor criticisms of Earned Schedule calculation that are overcome with a bit of further analysis (Lipke, 2006), but more importantly offers a general observation on Earned Value analysis. Book observes that when analyzing aggregate EVM data for a project individual tasks may be early or late, but cumulative values will not indicate a behind schedule condition. While Lipke s rebuttal to Book s objections (Lipke, 2006) resolves these questions the issues raised emphasize the importance of fully understanding the state of the art in Earned Schedule. As with any method, the practitioner would be well advised to evaluate Earned Schedule s applicability in a particular project environment. Earned Schedule is not the only proposed improvement to EVM methods. Jacob s Earned Duration method (Jacob, 2006) also presents an alternative to EVM schedule methods. Figure 6 shows Earned Duration calculated from the Performance Measurement Baseline Computer Sciences Corporation. All rights reserved. 13

14 Figure 6. Graphic Representation of the Earned Duration Method (Jacob, 2006) There is considerable similarity between Earned Schedule and Earned Duration. Both methods rely on creating a measurement yardstick by projecting current Earned Value to a point on the PMB where that amount of Earned Value should occur. The difference between the methods is that while Earned Schedule calculates variance from the present period to the yardstick, Earned Duration measures from the beginning of the project to the yardstick and the present performance period to calculate variances. Computer simulation has shown that Earned Duration provides valid data, but is not as robust as the Earned Schedule method for all project conditions (Vanhoucke & Vandevoorde, 2007). The Earned Duration method should not be dismissed, but evaluated in the context of a particular project environment and the data available for analysis. In evaluating the application of Earned Schedule, it is important to remember that calculations and metrics are derived from basic Earned Value data, and therefore results are subject to some of the same considerations and dependencies as any EVM method. Section 2.4 discusses some common cautions and considerations with Earned Value methods. 2.4 EVM AND ES: CAVEATS AND COMMENTS BASELINE DEFINITION AND CONTROL Regardless of the tracking and forecasting method selected, an accurate basis for measurement and comparison is required. Earned Value Management s Performance Measurement Baseline provides the intersection of two essential components, Planned Value and the period of performance. Planned Value at the task level is considered the measure of work to be accomplished. While most often expressed in monetary terms such as dollars, measures may be established for other units of work as appropriate to the task or project. The units of work must reflect and directly translate to the work defined for the task. For example, a project to wire a facility with network access might include a task to install 1,000 network jacks, at a cost of $80 per jack for a total cost of $80,000. While we can translate installation of jacks to cost terms, it is the installation of the jacks, not the expenditure of the money that determines the Earned Value for the task. If we find the actual cost of installing a jack is $90 each after installing 250 jacks for a total expenditure of $22,500, the budget expended is %28.1 of plan, while the measure of target work, the number of jacks installed, is only %25 complete Computer Sciences Corporation. All rights reserved. 14

15 The planned value of a task is scheduled for a period of performance with assigned start and end dates within the project network and project schedule, contributing to the overall PMB. Continuing with the network installation example in the previous paragraph, the installation of 1,000 network jacks might be evenly distributed across four workweeks to plan for installation of 250 per week, or 50 per day for a five-day workweek. Practical circumstances may not allow updating the task s Earned Value on a daily basis, but work accomplished may be measured at a very detailed level for any period of performance desired. The task could also be resource dependent, and planned for installation of 300 jacks in each of the first two weeks and the installation of 200 jacks in each of the remaining weeks. Again, total Planned Value will be accomplished in the required overall performance period but at a different point in time. Ensuring the proper understanding and definition of both time and work in the Performance Measurement Baseline are essential for reliable schedule metrics. Not all project types allow straightforward measurement of Earned Value. For example, software development projects can prove notoriously difficult to measure in the software coding and testing phases, requiring care in defining and measuring actual work accomplished. In misplaced enthusiasm for Earned Value techniques project work can be inappropriately structured in ways that can distort the view of true project performance. Creation of actual value, as viewed by the customer of the project, is through the realization of the project s product. While administrative, overhead, and project management functions certainly contribute to project delivery they are ancillary items that should not affect proper assessment of true project deliverables. Proper classification of Level of Effort and Apportioned type tasks (Project Management Institute, 2005) should be used to provide the proper structure of a project s work MEASUREMENT OF EARNED VALUE Once the project is underway progress toward objectives, the Earned Value, must be measured. As noted in the example in Section care is required in defining and measuring project work versus cost. In the installation of network jacks in that example, it is clear to see the implications of measuring progress strictly in terms of cost versus work accomplished, and the importance of accurate EV measurement and reporting cannot be overemphasized. Perhaps the easiest problems to avoid are those created by the lack of understanding of Earned Value Management fundamentals among project team members. Careful planning and a pristine measurement baseline are all for naught if project team members do not understand and appreciate the fundamentals of progress reporting. Simply asking team members what percent complete is task x without an appreciation for how such data is used is an almost certain path to failure. Team members do not have to be Earned Value Management experts, but should understand Earned Value, remaining work, and actual work in order to provide sufficient information to fulfill the project management plan s requirements for reporting. Every project should include team familiarization with reporting requirements as part of its startup plan, and EVM measurement techniques should be clearly and unambiguously defined in the project s governance plan. Without proper familiarization and training, team members are subject to multiple reporting errors and among these is the work equals progress syndrome. In its simplest form the team member s logic runs thus: My new project task is allocated 200 total hours, I was supposed to work on it for 20 hours this week, and I did... therefore my task is 10% complete. This variety of reporting indicates perfect performance until either the task is abruptly completed ahead of schedule or until the task is late and the team member attempts to report 110% complete NETWORK TOPOLOGY AND SCHEDULE PREDICTION RELIABILITY In their simulation study on the reliability of schedule prediction metrics Vanhoucke and Vandevoorde (Vanhoucke & Vandevoorde, 2007) found that the structure of the project schedule network affects forecast accuracy. The study shows that as a project becomes more serial, or has fewer parallel tasks, the better schedule prediction metrics perform. Intuitively it easy to appreciate that a project 2012 Computer Sciences Corporation. All rights reserved. 15

16 completed a single step or task at a time in sequence is easier to analyze than a project with multiple tasks in progress simultaneously. As a simple example of the potential effects of network topology, consider Figure 7 and project critical path. A project s critical path is the longest sequence of steps through a project s tasks, and thus determines the overall project duration. The path is considered critical since any delay of the component tasks of the critical path results in a corresponding delay in project completion. The six task Gantt chart in Figure 7 shows Tasks A & B as critical path tasks, while Tasks C G are sets of parallel non-critical tasks. Alternative points of view suggest that the project is not behind schedule if the non-critical tasks are delayed but the critical path is not affected. While EVM metrics would indicate that the project is falling behind schedule if the Planned Value for tasks C F is not accomplished at the planned time, nonetheless the project can finish on its baseline end date and therefore no real overall negative schedule variance exists as long as the critical path remains the same. Figure 7. Simple Network Topology The question of when a non-critical path task becomes a problem is an issue. If Tasks D through F are sufficiently delayed they could replace Task B as critical path tasks. This scenario has led to debate around the proper way to apply any EVM-based methods, from recommendations that EVM be applied at the discrete task level (Jacob, 2006) to the application of metrics to a subset of the PMB (Lipke, 2006). In either case, it is important to have an understanding of this area of concern, as well as to understand the measurement limitations in each project design AGGREGATE EARNED VALUE AND CRITICAL PATH DETECTION Another problem in accurate EVM schedule prediction concerns tasks completed ahead of schedule or out of sequence. Tasks completed ahead of schedule add Earned Value to the project s total, precluding detection of lagging critical path tasks since schedule performance is calculated based on aggregate Earned Value without regard to which tasks contribute that value (Vanhoucke & Vandevoorde, 2007). Tasks completed ahead of schedule but out of sequence are possibly completed without complete data provided by predecessor tasks and have greater potential for future rework (Lipke, 2009b). A relatively new technique called Schedule Adherence (Lipke, 2009b), (Lipke, 2011) offers methods to address these issues. 3 EARNED SCHEDULE IN THE REAL WORLD 3.1 OVERVIEW The introduction to Earned Schedule has shown the potential for significant improvement in schedule monitoring and forecasting in its extension to Earned Value Management. This section presents the retrospective application of Earned Schedule metrics to a real world completed project in order to demonstrate some of the practical applications of Earned Schedule. Of particular interest is the application of Earned Schedule to a project selected in a pseudo-random manner from a large portfolio of actual projects. While simulated data or carefully managed and controlled projects can provide pure data for evaluation, it is often the case in real project environments that variations in data collection and recording, accuracy of schedule metrics and maintenance, and the experience level of staff involved, among other factors, can result in less than textbook perfect analysis. A 2012 Computer Sciences Corporation. All rights reserved. 16

17 measure of the utility of any method is how well it can perform in less than ideal conditions, and how it applies to real world situations. The following example compares Earned Schedule to the Earned Value metrics as actually recorded and used to manage the example project. 3.2 PROJECT DESCRIPTION The example project s objective was to provide user interface enhancements for a commercial software product. Project metrics were collected weekly via a proprietary system used in conjunction with Microsoft Project. Actual labor hours were collected from an employee time accounting system, transferred directly to the Microsoft Project plan, and translated to Earned Value with an established algorithm. The project plan had an original baseline duration of 73.6 weeks and 14,757 labor hours. To coordinate with other projects competing for testing infrastructure and resources, during week 40 of project execution the baseline duration was reduced to 55.3 weeks without a reduction in planned effort. Typical projects in the performing organization employ a Project Control Officer (PCO) who works with the project manager to collect, analyze, and report on project metrics. The PCO was interviewed to gain insight into the basis of the decision to reduce project duration. The PCO related that the decision to reduce duration was based on analysis of Earned Value schedule metrics and the consensus of the project team that the shorter schedule was achievable. 3.3 PROJECT DATA ANALYSIS Figure 8 shows schedule metrics through week 40 / 51.9% complete, the point at which the decision to reduce the project s duration was made. The graph depicts SPI and SPI (t) schedule performance indices as well as the schedule variance in days calculated with each metric in addition to the To Complete Schedule Performance Index for the planned duration (TSPI (p)). Note that in week 40 SPI has a value of 1.18, producing a forecasted 55 days ahead of schedule. While showing very good project performance, Earned Schedule metrics do not present a picture as favorable, with SPI (t) = 1.08 and forecasting days ahead of schedule. The TSPI (p) =.90 metric indicates a high degree of confidence in an on time finish for the original baseline duration when compared to the SPI (t) current value of While both metrics show good project performance to date, the trend of the data from week 31 reveals cause for concern. While SPI is trending positive despite the range of variation displayed, SPI (t) declines steadily over the 9-week period from week 31 to week 40, indicating declining project performance. Recall from the overview of Earned Schedule that the TSPI equation can be used to evaluate the likelihood of completion within estimated project duration. Applying the equation to the metrics shown yields which produces a value well above the 1.0 level that indicates a schedule is reasonably achievable and the SPI (t) current value of Computer Sciences Corporation. All rights reserved. 17

18 Days Early (Late) EARNED SCHEDULE: FROM EMERGING PRACTICE TO PRACTICAL APPLICATION % Complete 51.9% Complete (20) (40) EV Schedule Variance Schedule Variance, Days ( SV(t) ) SPI SPI(t) TSPI(p) Week Number Figure 8. Schedule Performance through Period Computer Sciences Corporation. All rights reserved. 18

19 After week 40 the baseline duration was reduced to 55.4 weeks. Comparing Figure 9 with Figure 8 shows that rebaselining the project resulted in a proportional shift in Earned Value, and while the magnitude of the performance curves shifted, the overall trend and data indications remained intact. For example, in the original baseline the value of SPI(t) at week 40 is 1.08 while after rebaselining SPI(t) at week 40 is This curious effect is the result of shifting Earned Value to past periods rather than distributing remaining work to future periods within the reduced baseline duration. Following project performance in Figure 9 from week 40 shows a continuing negative performance trend to week 48, where TSPI (p) exceeds 1.10, indicating that the schedule has virtually reached the point of no recovery only 8 weeks after replanning and rebaselining. At week 53 we see SPI exhibit its classical unreliability as it levels off and begin its convergence to BAC, while Earned Schedule s SPI (t) continues to show deteriorating schedule performance. TSPI (p) also provides an additional visual status at the project baseline duration as its value assumes a negative value and appears as a vertical marker. Perhaps the most significant display of project performance is the steadily declining values of SPI(t) from week 31 through week 63. Although brief periods of improvement can be seen over the course of 2 to 3 weeks, the long-term trend clearly indicates worsening schedule performance over half the project s duration and clearly provides a better indication of true project performance than EVM s SPI counterpart provides Computer Sciences Corporation. All rights reserved. 19

20 Days Early (Late) EARNED SCHEDULE: FROM EMERGING PRACTICE TO PRACTICAL APPLICATION (10) (20) (30) EV Schedule Variance Schedule Variance, Days ( SV(t) ) SPI SPI(t) TSPI(p) Week Number Figure 9. Schedule Performance through Project Completion 2012 Computer Sciences Corporation. All rights reserved. 20

21 4 EARNED SCHEDULE ADOPTION IN CSC 4.1 BACKGROUND CSC has been recognized as a successful adopter of Earned Value cost management methods in information technology projects (Fleming & Koppelman, 2003). Through commitments in internal training programs, establishment of consistent processes, and development of custom tools and systems CSC continues to enhance organizational maturity in project management and Earned Value Management. It is the environment and culture of continuous evolution that provides CSC the opportunity to adopt, quickly and efficiently, Earned Schedule methods into regular practice by building on an established foundation of tools and methods. 4.2 INTEGRATION VIA THE CSC TOOLKIT The CSC Toolkit is a proprietary custom extension to Microsoft Project that supports tracking, reporting, auditing, and exporting project performance data. The CSC Toolkit used in conjunction with the Earned Schedule Workbook, a Microsoft Excel workbook developed by the author, can provide Earned Schedule data for any CSC project manager using Microsoft Project with the CSC Toolkit extension. The Earned Schedule Workbook calculates not only Earned Schedule values and metrics but also conventional Earned Value budget and schedule metrics, including budget TCPI indicators. The Workbook provides graphs for schedule and budget to allow easy interpretation of project performance and to compare EVM and ES metrics. More reliable schedule metrics provided by Earned Schedule provide the basis for advanced schedule and budget control tools as described in the following sections. Figure 10. CSC Toolkit Earned Value Export Earned Schedule information is available by 3 simple steps: 1. Using the CSC Toolkit s Analyze Earned Value function, export actual performance data to an Excel spreadsheet. The appropriate data fields as documented in the Earned Schedule Workbook are selected and the CSC Toolkit exports data to Excel. 2. Paste the exported data sheet to the Earned Schedule Workbook 3. Enter the project s baseline start, baseline finish, current status date and Budget at Completion in the Earned Schedule Workbook. Optionally an Estimate at Completion (EAC) may be entered for calculation of the TCPIe metric Computer Sciences Corporation. All rights reserved. 21

22 Following the three steps above Earned Schedule and Earned Value metrics are presented in a summary page, shown in Figure 11. The summary page includes a comments section, allowing the project manager to prepare a single sheet overview of project status that includes both technical performance data and a narrative. Figure 11. Earned Schedule Workbook Summary Page The Earned Schedule Workbook provides graphs for both budget (as shown in Figure 12) and schedule (as shown in Figure 13) to aid in project performance analysis. Since Earned Schedule is an emerging practice, the schedule performance graphs include standard Earned Value performance metrics to allow easy data comparison. Figure 12. Earned Schedule Workbook Budget Performance Graph 2012 Computer Sciences Corporation. All rights reserved. 22

23 Figure 13. Earned Schedule Workbook Schedule Performance Graph 4.3 FROM PERFORMANCE MONITORING TO PERFORMANCE ADJUSTMENT Calculating and interpreting reliable project performance metrics is certainly a challenge, and determining an appropriate course of action for effective project control is an important step. Schedule often seems to be the driving factor among the triple constraints of schedule, scope, and budget. How do the metrics presented provide practical help in finishing a project on time? Once a more reliable schedule metric via Earned Schedule is achieved to complement the CPI cost metric, multiple techniques based on balancing performance indicators become available. Lipke provides a good overview of project recovery techniques (Lipke, 2009c) and his work serves as a basis for examining some proposed project recovery data that can be useful in CSC s project portfolio. Recall from Section 2.1 that Earned Value uses the Schedule Performance Index, SPI, and from Section 2.3 that Earned Schedule uses the Schedule Performance Index for Time, SPI (t), as indicators of project performance, with a value of 1.0 indicating on time performance while values less than 1.0 show that a project is behind schedule. We have also seen that the Earned Schedule metric To Complete Schedule Performance Index, TSPI p, indicates the target value that SPI (t) must achieve for on time performance. While it is unlikely that any equation will ever provide a universal method for schedule recovery under all circumstances, it is possible to gauge a relative level of resources required for schedule recovery based on present resource usage and efficiency. The author developed the Schedule Recovery Level metric, SRL, for integration into the Financial Services Group s project reporting methods for trial and evaluation. SRL defines the percentage of the project s planned resources required to achieve on time performance. Note that Figure 11 shows a calculated value for Resource Ratio. Resource Ratio is defined as Given the CSC convention of measuring cost in true labor hours instead of actual dollars we have a ratio of actual labor hours expended to labor hours planned. We deduce that the actual progress efficiency against planned schedule (SPI (t)) was achieved using this proportion of planned. This interrelationship between these metrics serves as the basis for computing Schedule Recovery Level. We achieve on time schedule performance when the Schedule Performance Index, SPI (t), is greater than or equal to TSPI p. Therefore, the goal of driving the schedule toward on time completion is to apply sufficient resources to transform SPI (t) from its current level to the value of TSPI p. Using the equation for the Schedule Performance Index for Time 2012 Computer Sciences Corporation. All rights reserved. 23

24 and TSPI p we can use the ratio of the normalized values of the equations multiplied by the Resource Ratio to define Schedule Recovery Level as the percentage of planned labor hours required to achieve on time performance. Obviously, the SRL metric only offers guidance to the project manager, since applying the calculated resource level may prove too expensive to improve the project s schedule to be practical given the project s budget. Conversely, SRL may help in avoiding unnecessary project expense. In the circumstance where project metrics indicate a significantly earlier than required finish it is possible that the project may be overspending for no benefit. Rather than finish early with a budget overrun, SRL may guide the project manager to a better conclusion through on time, on budget completion. Evaluating SRL in the example data in Figure 11 we find that the project is 1.8 weeks ahead of schedule with a comfortable SPI (t) of CPI shows good cost performance with a value of The indicators suggest that the project could spend less and still finish on time, with SRL showing we could use 98% of originally planned resources to reduce expense. The prudent project manager would not reduce resources in a single step, but gradual reduction in resources while monitoring the resulting metrics could result in a better balance between schedule and budget. The logical extension of the Schedule Recovery Level metric is a corresponding measure for budget. Using the same rationale, a Budget Recovery Level (BRL) is found by With the foundation of the Earned Schedule technique and simple algebraic manipulation of the metrics presented, CSC practitioners can easily add valuable tools to their project management repertoire. As an example, substituting TSPI e in the SRL equation allows evaluation of the resource level required to achieve a revised forecasted project duration. 5 CONCLUSIONS AND FURTHER RESEARCH 5.1 CONCLUSIONS This paper has presented an overview of the history and application of Earned Value Management (EVM) methods in predicting project schedule performance. An introduction to the theory and application of Earned Schedule has been presented along with a simple example of its application. Earned Schedule was applied retroactively to analyze its predictive capabilities when compared to classical Earned Value schedule metrics, showing that ES and its associated metrics provide both a 2012 Computer Sciences Corporation. All rights reserved. 24

25 more reliable present status and forecast of project performance. From the theory, example, and foundation of the literature cited it appears that Earned Schedule offers significant potential improvement in project schedule reporting and forecasting. Reviewing the application of standard Earned Value techniques shows converting budget variance to time, lack of metrics for projects past their planned baseline, and the unreliability of EVM schedule data in the final third of a project s execution present serious limitations. While formulas and techniques exist and have been used to convert dollars to time, the counterintuitive nature of the method illustrates the need for a more straightforward technique. When considering a standard normal distribution of possible project outcomes it is inevitable that some projects are destined to finish later than planned, and for these projects, Earned Value schedule methods provide no useable data when project status and forecasting are essential to minimize adverse outcomes. Likewise, as projects move into the final third of their planned duration, Earned Value perhaps does a greater disservice to the project based organization by providing data that, while available and theoretically useable, is shown to be inaccurate with errors that are difficult to quantify. As discussed, the inaccuracies of Earned Value are amplified in late projects, again, failing to provide dependable information at the very point when accurate data can assist in effecting project recovery. These limitations have motivated the search for other methods, resulting in the evolution of Earned Schedule. Earned Schedule provides a simple and straightforward method for schedule analysis that overcomes the limitations in Earned Value methods. The evolution of commodity computer technology in conjunction with ever more common project scheduling tools in the desktop environment are providing the automated analytical platforms required for widespread adoption of Earned Schedule. This paper shows that Earned Schedule extensions are easily applied to existing tools, techniques, and data to provide an enhanced dimension to project schedule analysis and management. As shown in the retroactive project analysis provided, Earned Schedule can offer not only a better view of a project s current status, but perhaps more importantly it offers predictive methods that can help avoid missteps in project planning as well as assist in developing project recovery plans. Using the To Complete Schedule Performance Index (TCPI) equations to analyze the probability of success provides valuable insight in evaluating alternative project completion scenarios. Reliable performance indices calculated by CPI and SPI (t) allow derivative metrics that may include, among others, Schedule Recovery Level and Budget Recovery Level, to enable better control and more predictable results in project execution. The advantages of Earned Schedule methods are readily available to the CSC project management community. Leveraging the corporate investment in the CSC Toolkit with the author s Earned Schedule Workbook enables evaluation and practical application of advanced project metrics. The infrastructure of collaboration and training that CSC applies throughout the organization is well suited to promote quick evaluation and adoption of these promising new methods. 5.2 FURTHER RESEARCH Research and validation of ES metrics have been conducted retrospectively on individual projects (Vandevoorde & Vanhoucke, 2006), a small sample of six actual projects (Henderson, 2003) as well as on an extensive set of generated project data (Vanhoucke & Vandevoorde, 2007). Between these boundaries of existing research lies the need and opportunity to evaluate the performance of Earned Schedule on a larger sample set of actual projects. While time and coordination challenges precluded conducting this broader based research as the subject of this paper, the research and findings presented here indicate that further analysis based on available project data is warranted, with the objective of validating the application of Earned Schedule in an established project environment. Just 2012 Computer Sciences Corporation. All rights reserved. 25

26 as a single design for a project organization or a single method can t be used for all scenarios, tools and metrics must be tailored for and perform in real world environments. In considering project environments, it can t be assumed that what works in one industry, geographical region, or technical infrastructure will work in another. Differences in organizational maturity, infrastructure, tools, and reporting methods can have a significant impact on the viability of any given method. Based on the research and findings presented here CSC s Financial Services Group is currently working to integrate Earned Schedule metrics into its normal project reporting methods. Given the significant size of FSG s project portfolio, collecting and analyzing resulting data will provide an opportunity not only to advance CSC s best practices in project management but also to make a significant contribution to the project management community at large by contributing to the Earned Schedule body of knowledge with a large sample of real world data. The results of this effort should be presented in a subsequent paper. As demonstrated via the derivation of Schedule Recovery Level, reliable schedule metrics provided by Earned Schedule facilitate the creation of advanced budget and schedule metrics and techniques. Enhancements of existing tools to automate the calculation of optimal resource levels to achieve a desired schedule outcome using Monte Carlo simulation could provide a family of performance curves at any point during project execution, allowing the project manager to evaluate multiple project recovery strategies. Earned Schedule as an emerging practice is not an end goal, but rather a building block to improve CSC s core competency in project management Computer Sciences Corporation. All rights reserved. 26

27 6 WORKS CITED Abba, W. (2000). How Earned Value Got to Primetime: A Short Look Back and Glance Ahead. Paper presented at the PMI Seminars & Symposium. Proceedings, 2000, PDF, Houston, TX. Anbari, F. T. (2003). Earned Value Project Management Method and Extensions. [Article]. Project Management Journal, 34(4), Book, S. A. (2006a). Earned Schedule and Its Possible Unreliability as an Indicator. The Measurable News, 7. Book, S. A. (2006b). Earned Schedule and Its Possible Unreliability as an Indicator: Correction Note. The Measurable News, 3. Fleming, Q. W. (1988). Cost/Schedule Control Systems Criteria : The Management Guide to C/SCSC. Chicago, Ill.: Probus Pub. Co. Fleming, Q. W., & Koppelman, J. M. (2000). Earned Value Project Management (2nd ed.). Newton Square, Pa., USA: Project Management Institute. Fleming, Q. W., & Koppelman, J. M. (2003). What's Your Project's Real Price Tag? Harvard Business Review, 81(9), 20. Henderson, K. (2003). Earned Schedule: A Breakthrough Extention to Earned Value Theory? A Retrospective Analysis of Real Project Data. The Measurable News, 6. Henderson, K. (2005). Earned Schedule in Action. The Measurable News, 7. Jacob, D. (2003). Forecasting project schedule completion with earned value metrics. The Measureable News, 2003(3), 3. Jacob, D. (2006). Is Earned Schedule an Unreliable Indicator? No, but It s Not Necessarily the Premier Indicator for Assessing Schedule Performance. The Measurable News, 6. Lipke, W. (2003). Schedule is Different. The Measureable News, March 2003, 5. Lipke, W. (2006). Applying Earned Schedule to Critical Path Analysis and More. The Measurable News, 3. Lipke, W. (2009a). Earned Schedule (First ed.): Lulu Publishing. Lipke, W. (2009b). Schedule Adherence A Useful Measure for Project Management. The Measureable News, 2009(3), 6. Lipke, W. (2009c). The TCPI Indicator Transforming Project Performance. Projects & Profits, March Lipke, W. (2011). Schedule Adherence and Rework. The Measureable News, 2011(1), 6. Project Management Institute. (2005). Practice Standard for Earned Value Management. Newtown Square, PA: Project Management Institute, Inc. Project Management Institute. (2008). A Guide to the Project Management Body of Knowledge (PMBOK Guide), Fourth Edition (4th ed.). Newtown Square, Pa.: Project Management Institute Computer Sciences Corporation. All rights reserved. 27

28 Vandevoorde, S., & Vanhoucke, M. (2006). A Comparison of Different Project Duration Forecasting Methods Using Earned Value Metrics. International Journal of Project Management, 24(4), doi: /j.ijproman Vanhoucke, M., & Vandevoorde, S. (2007). A Simulation and Evaluation of Earned Value Metrics to Forecast the Project Duration. The Journal of the Operational Research Society, 58(10), DISCLAIMER The information, views and opinions expressed in this paper constitute solely the authors views and opinions and do not represent in any way CSC s official corporate views and opinions. The authors have made every attempt to ensure that the information contained in this paper has been obtained from reliable sources. CSC is not responsible for any errors or omissions or for the results obtained from the use of this information. All information in this paper is provided as is, with no guarantee by CSC of completeness, accuracy, timeliness or the results obtained from the use of this information, and without warranty of any kind, express or implied, including but not limited to warranties of performance, merchantability and fitness for a particular purpose. In no event will CSC, its related partnerships or corporations, or the partners, agents or employees thereof be liable to you or anyone else for any decision made or action taken in reliance on the information in this paper or for any consequential, special or similar damages, even if advised of the possibility of such damages Computer Sciences Corporation. All rights reserved. 28