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Previous research by Moore and White (2005) demonstrated the validity of a two-step regression approach to estimate total procurement cost growth. Although this study produced statistically significant findings, the resultant regression models relied on a predictor variable, First Unit Equipped, that appeared from a historic standpoint to be marginally populated in the database. To mitigate this limitation, this study addresses just two components of possible procurement cost growth for major DoD acquisition programs. These two areas consist of cost growth due to either physical changes to the system or program slippage or acceleration. In narrowing the scope of procurement cost growth, this study discovers more universal explanatory variables that can be used to predict whether specific types of cost growth will occur, and if so, by what expected percentage.

Within earned value management, the cost performance index (CPI) and the critical ratio (CR) are used to generate the estimates at completion (EACs). According to the research in the 1990s, estimating the final contract’s cost at completion (CAC) using EAC_CR is a quicker predictor of the actual final cost versus using EAC_CPI. This paper aims to investigate whether this trend stills holds for modern department of defense contracts.

Accessing the Cost Assessment Data Enterprise (CADE) database, 451 contracts consisting of 863 contract line item numbers (CLINs) were initially retrieved and analyzed in three stages. The first replicated the work conducted in 1990s. The second stage entailed calculating 95 per cent confidence intervals and hypothesis tests regarding percentage accuracy of EACs for a contract’s final CAC. Lastly, regression analysis was conducted to characterize major, moderate and minor influencers on EAC reliability.

For modern contracts, EAC_CR aligns more with EAC_CPI and no longer demonstrates early accuracy of a contract’s final CAC. Contract percentage completion strongly reduced the per cent error of estimating CAC, while cost-plus-fixed-fee contracts and those with no work breakdown structure greater than Level 2 negatively affected accuracy.

To militate against optimism of early assessment of a contract's true cost.

This paper provides empirical evidence that EAC_CR behaves more like EAC_CPI with respect to modern contracts, suggesting that today’s contracts have relatively high SPI. Therefore, caution is warranted for program managers when estimating the CAC from contract initiation up to and slightly beyond the mid-point of completion.

Cost growth in Department of Defense (DoD) weapon systems continues to be a scrutinized area of concern. One way to minimize unexpected cost growth is to derive better and more realistic cost estimates. In this vein, cost estimators have many analytical tools to ply. Previous research has demonstrated the use of a two-step logistic and multiple regression methodology to aid in this endeavor. We investigate and expand this methodology to cost growth in procurement dollar accounts for the Engineering and Manufacturing Development phase of DoD acquisition. We develop and present two salient statistical models for cost estimators to at least consider if not use in mitigating cost growth for existing and future government acquisition programs.

The purpose of this paper is to demonstrate an improved method for forecasting the US Army recruiting.

Time series methods, regression modeling, principle components and marketing research are included in this paper.

This paper found the unique ability of multiple statistical methods applied to a forecasting context to consider the effects of inputs that are controlled to some degree by a decision maker.

This work will successfully inform the US Army recruiting leadership on how this improved methodology will improve their recruitment process.

Improved US Army analytical technique for forecasting recruiting goals..

This work culls data from open sources, using a zip-code-based classification method to develop more comprehensive forecasting methods with which US Army recruiting leaders can better establish recruiting goals.

The purpose of this study was to investigate the stability of the cost performance index (CPI) for environmental remediation projects as the topic is not addressed in the literature. CPI is defined as the earned value of work performed divided by the actual cost of the work, and CPI stability represents the point in time in a project after which the CPI varies by less than 20 percent (measured in different ways).

After collecting monthly earned value management (EVM) data for 136 environmental remediation projects from a United States federal agency in fiscal years 2012 and 2013, the authors used the nonparametric Wilcoxon signed-rank test to analyze CPI stability. The authors also used nonparametric statistical comparisons to identify any significant relationships between CPI stability and independent variables representing project and contract characteristics.

The CPI for environmental projects did not stabilize until the projects were 41 percent complete with respect to project duration. The most significant factors contributing to CPI stability were categorized into the following managerial insights: contractor qualifications, communication, stakeholder engagement, contracting strategy, competition, EVM factors, and macro project factors.

As CPI stability for environmental remediation projects has not been reported in the literature, this research provides new insights to help project managers understand when the CPIs of environmental remediation projects stabilize and which factors have the most impact on CPI stability.

This paper examines the opportunity cost of applying simple averages in formulating the Department of Defense (DoD) budget for foreign exchange rates. Using out-of-sample validation, we evaluate the status quo of a center-weighted average against a Random Walk model, ARIMA, forward rates, futures contracts, and a private firm's forecasts over two time periods extending from Fiscal Year (FY) 1991 to FY 2014. The results strongly indicate that four of the alternative methods outperform the status quo over the shorter time period, and three methods for both time periods. Furthermore, a non-parametric comparison of the median error demonstrates statistical similarities between the four alternative methods over the short term. Overall, the paper recommends using the futures option prices to decrease forecast error by 3.23% and avoiding a $34 million opportunity cost.

Delineating where stability occurs in a contract provides the window of opportunity for procurement officials to positively affect cost and schedule outcomes. While the concept of a Cost Performance Index (CPI) "stability rule" has been routinely cited by Earned Value Management (EVM) authors since the early 1990's, more recent research questions the veracity of this stability rule. This paper resolves the controversy by demonstrating that the definition of stability matters. We find a morphing of the stability definition over time, with three separate definitions permeating the literature. Next, an analysis of Department of Defense contracts for both cost and schedule stability properties finds that the veracity of the stability rule is intricately tied to the definition used.

The most common technique to determine the predicted final cost of a Department of Defense (DoD) acquisition contract, or the Estimate at Completion (EAC), involves the use of performance indices to adjust the EAC. Other methods including simple linear regression and time series analysis have been developed to predict the final cost, but these methods are not widely publicized or have limited applicability. As a potential remedy, this research utilizes the historical contract data reported in the Defense Acquisition Executive Summary database and provides to the analyst a set of five working multiple regression models. Useful over the life of the contract, they accurately predict the final cost of the average major weapons system contract using contractor Cost Performance Report data.

Of all oil products consumed by the Argentine Air Force (AAF), jet fuel is the resource with highest demand and at the end of the day the most expensive support item procured by the AAF. Accurate predictions of Argentine jet fuel prices are necessary to improve AAF financial and logistics planning. Multiple regression analysis is one such tool that can aid in accurately forecasting the amount required when procuring this valuable commodity. Using this methodology, we develop and illustrate a highly predictive model that has an adjusted R2 of 0.98 and an average percentage absolute error of 4%.

The introduction of “should cost” in 2011 required all Major Defense Acquisition Programs (MDAP) to create efficiencies and improvements to reduce a program’s “will-cost” estimate. Realistic “will-cost” estimates are a necessary condition for the “should cost” analysis to be effectively implemented. Owing to the inherent difficulties in establishing a program’s will-cost estimate, this paper aims to propose a new model to infuse realism into this estimate.

Using historical data from 73 Departments of Defense programs as recorded in the selected acquisition reports (SARs), the analysis uses mixed stepwise regression to predict a program’s cost from Milestone B (MS B) to initial operational capability (IOC).

The presented model explains 83 per cent of the variation in the program acquisition cost. Significant predictor variables include: projected duration (months from MS B to IOC); the amount of research development test and evaluation (RDT&E) funding spent at the start of MS B; whether the program is considered a fixed-wing aircraft; whether a program is considered an electronic system program; whether a program is considered ACAT I at MS B; and the program size relative to the total program’s projected acquisition costs at MS B.

The model supports the “will-cost and should-cost” requirement levied in 2011 by providing an objective and defensible cost for what a program should actually cost based on what has been achieved in the past. A quality will-cost estimate provides a starting point for program managers to examine processes and find efficiencies that lead to reduced program costs.