Better Schedule Performance Assessments Derived From Integrated Master Plan-Referenced Schedule Metrics

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Author: David C. Bachman

The integrated master plan (IMP) provides a better structure than either the work breakdown structure (WBS) or organizational breakdown structure for measuring actual integrated master schedule (IMS) progress. The author posits that improved understanding of schedule performance and better identification of program risks result when an IMP structure is evaluated in addition to the earned value management-mandated IMS WBS structure. The article examines how the “Hit-Miss” index, baseline execution index, and critical path length index (CPLI) were used to evaluate the life-cycle performance of a 12-month, 900-task IMP program event. CPLI, the author concludes, is subject to interpretation and must be evaluated against four caveats: duration remaining, total float including schedule margin, schedule compression, and schedule avoidance.

Management (EVM), Integrated Master Plan (IMP), Integrated Master Schedule (IMS)
Since 2005, the National Geospatial-Intelligence Agency (NGA) has been tasked under the Base Realignment and Closure Act to consolidate all Washington, DC, metropolitan area facilities to a standalone campus currently called NGA Campus East (NCE) by September 11, 2011. The U.S. Army Corps of Engineers is currently managing the construction of NCE at the Fort Belvoir, Virginia, Engineering Proving Grounds (NGA, 2010). In addition to the NCE facilities, NGA has awarded additional contracts for the installation of communication, hardware, and software systems necessary to support the NGA mission at the new facility. This article examines the schedule performance reported from one of these contracts. The contract included full earned value management (EVM) implementation, and the reported data came from an Electronic Industries Alliance (EIA)-748B-compliant earned value management system (EVMS) (Government Electronics and Information Technology Association [GEIA], 2007).

The NGA program management team has included EVM as one tool to effectively manage program risk, technical scope, cost, and schedule. This contract’s effort is built around an integrated master plan (IMP)1 consisting of 11 major program events (PE),2 43 significant accomplishments,3 and 201 accomplishment criteria.4 From this IMP, a 6,000-line integrated master schedule (IMS) has evolved and continues to grow each month as the contract matures. A product-oriented work breakdown structure (WBS) and a corresponding EVM performance measurement baseline (PMB) resulted from the IMS. Integrated baseline reviews (IBR) were held in month 2 and month 6 of the program, and all IBR-related issues were resolved by month 13. The NGA EVM Center of Excellence (EVM COE) is responsible for program oversight and was challenged to create a set of pure, “straightforward” IMS metrics unrelated to EVM that would provide NGA leadership with accurate assessments of schedule progress.

The EVM COE augmented the contract-level acquisition, technology and logistics (AT&L) tripwire schedule metrics to improve their utility for assessing NCE contract schedule progress. Initially, the EVM COE examined reporting on all 14 of the Defense Contract Management Agency (DCMA)’s 14 point schedule assessment5 metrics (Treacy, 2010) using the proposed 62-element Generally Accepted Scheduling Principle (GASP),6 a quick-look schedule assessment (Meyer, 2010). Both proved to be far too detailed and intricate to address the straightforward challenge from NGA leadership. In 2006, DCMA standardized a set of EVM and schedule metrics for the Defense Acquisition Executive Summary (DAES) process known as the AT&L tripwire metrics. A subset of DCMA’s 14 point schedule assessment metrics is included as AT&L tripwire metrics. The two primary and seven secondary metrics are designed to surface problems early for effective issue resolution (Kester, 2007). The baseline execution index (BEI) (which measures work progress) and the critical path length index (CPLI) (which measures efficiency associated with completing a milestone) are two of the secondary AT&L tripwire metrics directly related to schedule performance. Although not directly reported as a tripwire metric, the DCMA BEI tripwire briefing also reports the closely related “Task Hit/Miss Percentage” or the “Hit-Miss” index. These three metrics were the starting point for NGA’s straightforward schedule assessment. Initially, the EVM COE computed these metrics only at the contract level and found them to have limited utility. Department of Defense (DoD) policy and the IMS data item description require the IMS to be delivered in a product-oriented WBS format (DoD, 2006). Since most of the contract’s PEs cut across multiple WBS elements, computing WBS-related BEIs and Hit-Miss indexes revealed little about progress to the next PE. Considering that September 2011 was many months away, the critical path to that date was mostly controlled by EVM summary-level planning packages7 or external milestones, making a contract-level CPLI at best misleading and unreliable. To accurately assess contract progress, the EVM COE computed IMS schedule metrics using an IMP structure and redefined the CPLI tripwire metric to include schedule margin.8

After reviewing an actual 12-month IMP PE life cycle, five metrics emerged that best defined schedule performance and status: contract-level Hit-Miss index, PE Hit-Miss index, contract-level BEI, PE BEI, and the PE CPLI. The EVM product-oriented WBS provided little insight into actual schedule performance because each reporting-level WBS element supported multiple PEs. The IMS data item description requires the IMS to be vertically traceable to the IMP, but it includes the caveat “(if applicable)” (DoD, 2005b). NGA contractors are required to map their IMS tasks and milestones to the IMP. This allows the schedule to be sorted by IMP PEs, IMP significant accomplishments, as well as the EVM product-oriented WBS and contract organization structures. Because the contract PEs were sequential in nature, the program management team’s assessment focused on the next IMP PE or, in one case, the next two PEs because they were being completed in parallel. The EVM COE Hit-Miss index uses the AT&L Hit-Miss equation to measure the percentage of the current month baseline tasks/activities actually completed (or Hit) on or ahead of their baseline schedule (Figure 1). The EVM COE uses the AT&L tripwire BEI equation to measure the cumulative efficiency with which actual work is accomplished when measured against the baseline (Figure 2). Different from the AT&L tripwire equation, the NGA CPLI equation includes recognition of IMS schedule margin.9 By dissecting the CPLI over the entire life cycle of an IMP PE, the program management team discovered that unlike the Hit-Miss and BEI metrics, they could easily influence the value of the CPLI metric (Figure 3). To truly understand a CPLI, four schedule caveats must be determined: duration remaining, float10 and margin, schedule compression,11 and schedule avoidance.12 The following discussion presents a hypothetical schedule to review and explain these metrics and CPLI schedule caveats.

Figure 1. “Hit-Miss” Index Equation

Figure 2. Baseline Execution Index (BEI) Equation

Figure 3. Critical Path Length Index (CPLI) Equation


The NGA schedule metrics and rationale for the four CPLI caveats can best be explained by reviewing the progress of a hypothetical schedule. Figure 4 is a hypothetical baseline schedule consisting of summary-level tasks, work tasks, one margin task, two PE milestones, and a start milestone. The network13 schedule to PE No. 1 includes a critical path string and a high-risk path string, each networked to the PE No. 1’s multistring margin task. Remember that the critical path is the longest path through a network schedule and may not represent the high-risk path. The high-risk string in this schedule has 5 days of total float. To demonstrate the NGA metrics and the CPLI caveats observed on the NGA contract, all tasks associated with the critical path will be completed on schedule, and the high-risk tasks will finish late to demonstrate how the metrics change as the schedule slips. These metrics and their color coding are summarized in Table 1.

Figure 4. Baseline Schedule

Table 1. Schedule Metrics for Hypothetical Schedule


NGA Schedule Metrics

Hit-Miss Index, BEI, and CPLI. The January 29, 2010, schedule (Figure 5) and the January 2010 data (Table 1) highlight the basic calculations associated with the NGA schedule metrics. At the end of the first reporting period (January 29, 2010), two tasks were scheduled to be completed and both were completed. Since Task A1 finished on schedule, it counts as a “Hit” for the Hit-Miss index and as a completed task for the BEI. Task B2 finished 3 workdays late, but still during the reporting period so it counts as a “Miss” for the Hit-Miss and as a completed task for the BEI. The Hit-Miss index is 1 ÷ 2 = 0.50, and the BEI is 2 ÷ 2 = 1.00. Although the high-risk path slipped, it has not yet slipped onto the critical path. So the IMS critical path to PE No. 1 is still controlled by the critical path string (0 float days) and the margin task (10 days). The January 29, 2010, schedule task—duration remaining check—shows that 65 workdays remain until the PE No. 1 baseline finish date, resulting in a 1.15 CPLI [(65 + 0 + 10) ÷ 65 = 1.15] for the first reporting period.

Figure 5. January 29, 2010, Schedule: “Hit-Miss” Index, BEI, & CPLI

CPLI Schedule Caveats

CPLI—duration remaining and total float. The February 26, 2010, schedule (Figure 6) indicates the high-risk path tasks have redefined the schedule’s critical path. The February 2010 line entry in Table 1 lists the February 26, 2010, metrics. At the end of the second reporting period, all five tasks scheduled to be completed have been completed, resulting in a 1.00 BEI. Remember that the Hit-Miss index is a current period metric so the 0.67 value represents Task A2 and Task A3 finishing on schedule (“Hit”) and Task B2 finishing 8 days late. With 45 days’ duration remaining, the schedule’s critical path is now defined by the slipping high-risk path tasks. To maintain the April 30, 2010, PE No. 1 baseline finish date, the margin task was reduced from 10 days to 7 days, resulting in a 1.16 CPLI [(45 + 0 + 7) ÷ 45 = 1.16]. Despite the fact that the high-risk path continues to slip, the CPLI showed a slight improvement, thereby demonstrating the need for the duration remaining caveat and total float caveat.

Figure 6. February 26, 2010, Schedule: CPLI-Duration and Total Float

CPLI—schedule compression. The March 26, 2010, schedule (Figure 7) introduces schedule compression and an unfavorable BEI. The March 2010 line entry in Table 1 lists the March 26, 2010, metrics. The high-risk path tasks slipped an additional 6 days in March, and only Task A4 finished on schedule. Neither Task B3 nor Task B4 was completed, resulting in the Table 1 March 2010 Hit-Miss index of 0.33 and BEI of 0.75. If the schedule margin task’s duration was reduced for the entire March 26, 2010, schedule slip (6 days), a yellow 1.045 CPLI would result. By compressing the Task B5’s schedule by 2 days, the CPLI stays green because the float + margin remain at plus 3 days. This raises an obvious question: Can Task B5 be completed in 8 versus 10 days? It also highlights why schedule compression is a dimension that must be considered when evaluating the CPLI metric.

Figure 7. March 26, 2010, Schedule: CPLI-Schedule Compression

CPLI—schedule avoidance. The April 23, 2010, schedule (Figure 8) documents the final CPLI schedule caveat observed on the contract. The April 2010 line entry in Table 1 lists the April 23, 2010, metrics. Schedule avoidance occurs when a task is eliminated from the schedule, the IMS network logic is changed to allow a task to be deferred to a later program event milestone, or the IMS logic is changed to allow sequential tasks to be done in parallel. During April 2010, the overall schedule slipped an additional 7 days, making an on-time delivery of PE No. 1 impossible. To support an on-time delivery of PE No. 1, completion of Task B5 is deferred to PE No. 2. This is done by deleting Task B5’s task relationship with the schedule margin task and linking it only to the PE No. 2 milestone. Task B5’s logic now bypasses PE No. 1, resulting in the avoidance of 9 days of duration remaining, 1 additional day of duration for the margin task, and a very favorable 1.80 CPLI [(5 + 0 + 4) ÷ 5 = 1.80] for PE No. 1. Without the Task B5 schedule avoidance, the PE No. 1 milestone slips 4 days, and the CPLI would be 0.56 = [{9 + (-4) + 0}/ 9]. This highlights why schedule avoidance is a CPLI metric caveat that must be evaluated.

Figure 8. April 23, 2010: CPLI – Schedule Avoidance

Results and Discussion

The metrics and CPLI caveats discussed hypothetically in this article are computed and documented for the actual program in Tables 2, 3, and 4 at both contract and IMP PE levels. Tables 2, 3, and 4 document the contract schedule metrics for IMP PE-E, PE-F, and PE-G, respectively. The data reflect month 24 for PE-E, and month 25 for PE-F and PE-G. PE-G is the immediate successor event to PE-E and is being completed in parallel with PE-F, which is unrelated. Both PE-G and PE-F are baselined to finish during the first 2 weeks of month 35. The contract-level data tasks only include tasks through day 15 of month 35.

Table 2. Schedule Metrics for Program Event E


Table 3. Schedule Metrics for Program Event F


Table 4. Schedule Metrics for Program G


Many conclusions can be drawn from the data contained in Tables 2, 3, & 4 and the IMP strategy used to collect them. Conclusions associated with Table 2 are historic in nature and were confirmed with the NGA program manager and contractor. It is important to note that PE-E was completed on time. This was achieved by deferring some PE-E tasks to PE-G, and by eliminating other PE-E tasks that were not required. In the author’s opinion, identification of the deferred and eliminated tasks may not have been discovered using traditional EVM WBS or Organizational Breakdown Structure (OBS) analysis strategies. Applying what the program management team learned from PE-E, pertinent questions arise regarding PE-F and PE-G trends and overall schedule performance. Seven key conclusions supported by this contract’s data, metrics, and the IMP analysis strategy follow:

Unfavorable schedule trends. In all cases, unfavorable schedule trends are first manifested in the Hit-Miss index, then the BEI, and lastly in the CPLI.

Identification of work scope changes. IMP strategy clearly identifies work scope changes. The total number of PE–E tasks gradually increased from 1,109 tasks in month 14 to 1,171 tasks in month 21. The program management team confirmed that these changes resulted from the conversion of EVM planning packages to EVM work tasks, and to the addition of new work resulting from contract changes. In month 22, the total tasks associated with PE-E were abruptly reduced to 924 tasks. The IMP strategy analysis approach clearly identified the descoping, and it was confirmed that the 257 associated tasks were either deferred or eliminated to support PE-E’s month 24 delivery date (Figure 9). Additionally, comparing the Table 2 (based on month 24 data) contract cumulative tasks for months 22–24 with the same months on Tables 3 and 4 (based on month 25 data), the program management team can account for the permanent elimination of 90 PE-E tasks from the schedule.

Figure 9. PE-E Work Scope Changes

IMP synergistic planning focus. The IMP structure allows for the current month contract-level tasks to be associated with the current IMP program focus. Tables 3 and 4 and Figure 10 show a total of 99 contract tasks baselined for month 25; 83 percent of these tasks are associated with either PE-F (22 tasks) or PE-G (60 tasks). This leaves 17 current month tasks that are not associated with either of the next two PEs. In and of themselves, the WBS and OBS structures would not easily support this type of assessment.

Figure 10. IMP Synergistic Metrics

IMP synergistic performance focus. Closely related to conclusion 3, comparing the current month “Contract Tasks Hit” and the corresponding PE-F and PE-G “Hits” task values provides insight on the contract-level metric. Figure 10 shows 30 of the 99-month, 25 contract-level schedule tasks were completed. Subtracting the 7 Hits from PE-F and the 7 Hits from PE-G leaves 16. So 16 of the 17 tasks not associated with either of the next two milestones were completed. This suggests the contract-level Hit-Miss index by itself may not be a good indicator for assessing PE-F or PE-G progress.

BEI measures schedule performance trends. Evaluation of the PE BEI trend can indicate if a schedule is improving. Review of the PE-E (Table 2) BEI metric from month 16 to month 18 indicates the number of cumulative tasks increased at a rate faster than the finished tasks were being completed. The month 16 BEI of 0.57 deteriorated to 0.48 by month 18, indicating a slipping schedule. This fact is also supported by the total float value, which eroded by 8 days during the same period. Like the EVM schedule performance index metric, the BEI eventually must improve to 1.00. This is clearly shown in the BEI improving from 0.48 in month 18 to 0.98 in month 24.

CPLI schedule compression an “early” tripwire. Schedule compression was first observed after the PE-E CPLI metric went from green to yellow. In month 18, PE-E’s CPLI went from 1.06 (an NGA green value) to 1.03 (an NGA yellow value). In the following month’s schedule, the metric returned to green (1.11) when the baseline durations of three future critical path tasks were reduced by a total of 17 days. In many instances, the schedule compression magnitude was greater on the near-critical path strings than on the critical path string. The author attributes this to control account managers wanting to avoid the extra scrutiny associated with being on the critical path. The EVM COE makes no value judgment on the validity of the schedule compression; as such, the NGA CPLI equation (Figure 3) does not consider it. Schedule compression is a key caveat the program manager must consider when evaluating schedule risk. The author believes schedule compression is a critical indicator of pending schedule issues. In the PE-E schedule, schedule compression was first observed in month 19—3 months before the month 22 adjustment discussed in conclusion 2.

CPLI schedule avoidance “too late” tripwire. Schedule avoidance first appeared when it became apparent that PE-E, as it was originally baselined, could not be completed on schedule. During the final 30 workdays leading up to PE-E, 39 tasks originally associated with PE-E were remapped to PE-F. This allowed for the on-time delivery of PE-E. The impact of this can be observed in the month 23 “PT” column (Table 4), which denotes PE-G Tasks Baselined. The contractor confirmed that the disproportionate number of month 23 tasks (152) resulted from tasks being transferred from PE-E. Like schedule compression, the EVM COE does not consider schedule avoidance in the CPLI computation, but does report it as a CPLI schedule metric caveat. The program manager must make the final decision on the potential cost, schedule, and programmatic risk associated with eliminating work scope altogether or deferring work scope to a later IMP PE milestone.

Summary and Concluding Discussion

The EVM COE was challenged to create a set of schedule metrics that provide NGA leadership with accurate assessments of schedule progress. A better understanding of schedule performance and improved program risk identification were realized on this contract when NGA focused its schedule metrics on the IMP structure. While this study’s positive results are based on a single contract, they justify additional research with a larger data set. The CPLI caveats qualify the CPLI tripwire metric, which could be easily misinterpreted without them. Considering this finding, additional research is warranted to justify requiring CPLI metric reports to include the caveats. NGA program managers have embraced the IMP structure and the metrics discussed in this article because they are straightforward, focus on near-term problems, and identify specific tasks needed to assess programmatic and schedule risk. This IMP approach, however, does not address cost. The EVM COE found that the IMP structures were so different from the contract’s corresponding WBS and OBS structures that EVM cost and schedule data could only be correlated at the total contract level.

NGA has applied the techniques discussed in this article with mixed success on other NGA contracts. When the IMP and schedule margin were well-defined, the resulting schedule metrics were easily computed and meaningful. However, often the required IMP was poorly constructed and schedule margins were ill-defined. Schedule compression and schedule avoidance, to a lesser extent, were observed on many NGA contracts. The 438-page Government Accountability Office (GAO) Cost Estimating Assessment Guide lists only three IMP references and contains no discussion on how IMP is to be used (GAO, 2009). The EVM EIA-748B standard does not reference the IMP at all (GEIA, 2007). For the past 6 years, the National Defense Industrial Association (NDIA, n.d.) has identified schedule margin as an unresolved issue (Treacy, 2009; Berkey 2004). The community needs to create an IMP data item description and establish a best practice for implementing schedule margin to universally realize meaningful, straightforward schedule metrics based on this article’s IMP approach.

The IMP structure augmented and provided more meaningful metrics for measuring near-term schedule performance then either the WBS or OBS structures; however, integrated program management requires cost, schedule, and performance metrics. This study highlights the value of adding an IMP structure to the IMS. The logical extension of this study would be to also require EVM data be mapped to the IMP structure.

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Author Biography

Mr David C BachmanMr. David C. Bachman is currently an EVM facilitator for the National Geospatial-Intelligence Agency. He was the U.S. Air Force Nuclear Detonation Detection System program manager, a senior program analyst with Engineering Management Concepts, Inc., and a previous Defense Acquisition University professor. Mr. Bachman holds a master’s in Education from Marymount University, an MBA from Rutgers University, and a BS in Ceramic Engineering from Penn State.

(E-mail: David.C.Bachman@nga.mil)


Berkey, W. (n.d.). Schedule management. Paper presented at the National Defense Industrial Association (NDIA), Program Management Systems Committee Meeting, Columbus, OH, August 2004. Retrieved from http://www.ndia.org/Divisions/Divisions/Procurement/Documents/Content/ContentGroups/Divisions1/Procurement/PDFs10/BerkeySchedule_Management.pdf

Defense Contract Management Agency. (2010). DCMA schedule margin position paper (Document Control ID: EVC-106). Unpublished white paper. Alexandria, VA: Author.

Department of Defense. (2005a). Integrated Master Plan and Integrated Master Schedule preparation and use guide (Version 0.9). Retrieved from http://www.acq.osd.mil/se/docs/IMP_IMS_Guide_v9.pdf

Department of Defense. (2005b). Integrated Master Schedule (Data Item Description: DI-MGMT-81650). Retrieved from http://assistdocs.com/search/document_details.cfm?ident_number=217578&StartRow=1&PaginatorPageNumber=1&title=IMS&status%5Fall=ON&search%5Fmethod=BASIC

Department of Defense. (2006). Earned Value Management implementation guide. Retrieved from http://guidebook.dcma.mil/79/EVMIG.doc

Government Accountability Office. (2009). GAO cost estimating and assessment guide: Best practices for developing and managing capital program costs (Report No. GAO-09-3SP). Retrieved from http://www.gao.gov/new.items/d093sp.pdf

Government Electronics and Information Technology Association. (2007). GEIA standard earned value management systems (Publication No. EIA-748-B). Arlington, VA: Author.

Hurley, C. (2007). Earned Value Management (EVM) tripwires: DCMA’s new approach for assessing program performance. Unpublished presentation at the Defense Contract Management Agency EVM Functional Integrated Product Team meeting, Fort Belvoir, VA, May 2007.

Kester, D. (2007). USD(A&T) DAES review tripwires. Paper presented at the 19th Annual International Integrated Program Management Conference (Document ID: 1363), Alexandria, VA, November 5–7, 2007. Retrieved from: http://www.pmi-cpm.org/members/library/WS06%20PART%202.pdf

Meyer, F. (2010). Air Force program management and acquisition excellence initiatives. Presentation at the National Defense Industrial Association, Program Management Systems Committee Meeting, Los Angeles, CA, February 2010. Retrieved from http://www.ndia.org/Divisions/Divisions/Procurement/Documents/Feb2010_Presentations/Meyer_NDIA_04_February_2010.pptx

National Defense Industrial Association. (n.d.). Depicting schedule margin in Integrated Master Schedules (Draft white paper). Retrieved from http://www.ndia.org/Divisions/Divisions/Procurement/Documents/PMSCommittee/CommitteeDocuments/WhitePapers/NDIAScheduleMarginIssueWhitePaperDraft.pdf

National Geospatial-Intelligence Agency. (2010). NGA Campus East: The story behind NGA. Retrieved from https://www1.nga.mil/About/nce/Pages/default.aspx

Price, R. (2008). The missing link: Schedule margin management. Paper presented at the Project Management Institute–College of Performance Management Earned Value Management World 2008 (Document ID: 1426), Clearwater Beach, FL, May 15, 2008. Retrieved from http://www.pmi-cpm.org/members/library/PS%2010.pdf

Stover, T. S. (2007). Checking and adjusting the project plan. In V. Thuman, S. Hayes, & J. Atkinson (Eds), Microsoft Office Project 2007 Inside Out (pp. 307–365). Redmond, WA: Microsoft Press.

Treacy, D. (2009). Schedule working group. Presentation at the National Defense Industrial Association, Program Management Systems Committee Meeting, Bethesda, MD, May 2009. Retrieved from http://www.ndia.org/Divisions/Divisions/Procurement/Documents/TreacyNDIA%20PMSCSWGReportMay2009.ppt


1. Integrated Master Plan (IMP)—The IMP is an event-based plan consisting of a hierarchy of program events, with each event being supported by specific significant accomplishments, and each accomplishment is associated with specific accomplishment criteria to be satisfied for its completion (DoD, 2005a).

2. Program Event—A program event is a program assessment point that occurs at the culmination of significant program activities: significant accomplishments and accomplishment criteria (DoD, 2005a).

3. Significant Accomplishment—A significant accomplishment is the desired result(s) prior to or at completion of a program event that indicates a level of the program’s progress (DoD, 2005a).

4. Accomplishment Criteria—Accomplishment criteria provide definitive evidence that a specific significant accomplishment has been completed (DoD, 2005a).

5. Defense Contract Management Agency 14 Point Schedule Assessment—A set of standardized schedule heath and performance metrics used to evaluate integrated master schedules. The metrics included: logic, leads, lags, task relationships, constraints, high float, negative float, high duration, invalid dates, resources, missed tasks, critical path, CPLI, and BEI.

6. Generally Accepted Scheduling Principle (GASP)—A defense industry–Department of Defense initiative to produce valid and effective schedules. To meet GASP tenets, a schedule must be complete, traceable, transparent, statused, predictive, usable, resourced, and controlled.

7. Summary-Level Planning Package (SLPP)—An aggregation of work for far-term efforts, not comprised of detailed planning nor able to be identified at the control account level, which can be assigned to reporting-level WBS elements (DoD, 2006).

8. Schedule Margin—A management method for accommodating schedule contingencies. It is a designated buffer and shall be identified separately and considered part of the baseline. Schedule margin is the difference between contractual milestone date(s) and the contractor’s planned date(s) of accomplishment (DoD, 2005b).

9. IMS Schedule Margin—There are differing opinions in the EVM community on the proper use and interpretation of IMS schedule margin8 (DCMA, 2010; Price, 2008; NDIA, n.d.). Because most NGA contracts include schedule margin strategies, the EVM COE metrics include the use of schedule margin.

10. Float—Also known as total float and total slack. The amount of time a task/activity or milestone can slip before it delays the contract or project finish date (DoD, 2005b).

11. Schedule Compression—Schedule Compression is the difference between a task’s baseline duration and the task’s current duration. Microsoft Project calls this duration variance (Stover, 2007).

12. Schedule Avoidance—Schedule Avoidance occurs when a task’s baseline logic is changed to bypass a measured milestone.

13. Network—A schedule format in which the activities and milestones are represented along with the interdependencies between work tasks and planning packages (or lower level tasks or activities). It expresses the logic (i.e., predecessors and successors) of how the program will be accomplished. Network schedules are the basis for critical path analysis, a method for identification and assessment of schedule priorities and impacts. At a minimum, all discrete work shall be included in the network (DoD, 2005a).


List of Abbreviations and Acronyms

AT&L Acquisition, Technology and Logistics
BEI Baseline Execution Index
CPLI Critical Path Length Index
DAES Defense Acquisition Executive Summary
DCMA Defense Contract Management Agency
DoD Department of Defense
EIA Electronic Industries Alliance
EVM Earned Value Management
EVM COE NGA Earned Value Management Center of Excellence
GAO Government Accountability Office
GASP Generally Accepted Scheduling Principle
GEIA Government Electronics and Information Technology Association
IBR Integrated Baseline Review
IMP Integrated Master Plan
IMS Integrated Master Schedule
NASA National Aeronautics and Space Administration
NCE NGA Campus East
NDIA National Defense Industrial Association
NGA National Geospatial-Intelligence Agency
OBS Organizational Breakdown Structure
PE Program Event
PMB Performance Measurement Baseline
WBS Work Breakdown Structure



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