Exploring Performance in Air Force Science and Technology Programs

Science and technology (S&T) programs serve an important function in the defense acquisition process as the initial phase leading to discovery and development of warfighting technology. The results of these programs impact the larger major defense acquisition programs, which integrate the technologies in subsequent phases of the life cycle. Despite this important role, little prior research has examined the performance of S&T programs. In this study, the authors investigate the impact of technological maturation as a critical success factor in Air Force S&T programs. The results suggest that S&T programs with mature technologies are more likely to experience above average cost growth and larger contract values while less likely to experience schedule growth. Additionally, the authors find the partnership method between the government and contractor matters for both technological maturation and schedule growth. Lastly, the nature of the S&T program is important, with aerospace programs more likely to technologically mature than human systems programs

Analyzing the Efficiency of Horizontal Photovoltaic Cells in Various Climate Regions

This research presents the development of linear regression models to predict horizontal photovoltaic power output. We collected a dataset from 14 global Department of Defense (DoD) installations over a timeframe of one year using an experimental apparatus, resulting in 24,179 usable data points. We developed a linear model to predict power output, which incorporated site-specific weather and geographical characteristics, along with Köppen-Geiger climate classifications in order to determine the effect of adding climate to the model. After performing a Wald test between the full model and a reduced model without Köppen-Geiger climate variables, it was determined that including Köppen-Geiger climate variables improved the model’s ability to account for horizontal photovoltaic power variation by 3%. Although adding Köppen-Geiger variables provided added value when modeling the training dataset, these variables were less effective in predicting the validation dataset. From the analysis, the ideal Köppen-Geiger region was determined to be a warm temperate main classification, a fully humid precipitation classification and a warm summer temperature classification. This region possessed a 30% greater average power production than the mean value of the base climate classification. We found that the cost-effectiveness of a photovoltaic array depends on Köppen-Geiger climate regions, in addition to weather characteristics and the orientation of the array

Sum-Based Scoring for Dichotomous and Likert-scale Questions

In this article we investigate how to score a dichotomous scored question when co-mingled with a typically scored set of Likert scale questions. The goal is to find the upper value of the dichotomous response such that no single question is overly weighted when analyzing the summed values of the entire set of questions. Results demonstrate that setting the upper value of the dichotomous value to the max value of the Likert scale question scale is inappropriate. We provide a more appropriate value to use when considering Likert scale questions up to the max value of 10.Comment: 7 pages, 1 Tabl

A Panel Data Regression Model for Defense Merger and Acquisition Activity

Excerpt: This paper examines the relationship between a prime contractor\u27s financial health and its mergers and acquisitions (M&A) spending in the defense industry. It aims to provide models that give the United States Department of Defense (DoD) indications of future M&A activity, informing decision-makers and contributing to ensuring competitive markets that benefit the consumer. The results show a significant relationship between efficiency and M&A spending, indicating that companies with lower efficiency tend to spend more on M&As. However, there was no significant relationship between M&A spending and a company\u27s profitability or solvency. These results were consistent with previous research and the study\u27s hypotheses for profitability and solvency. However, the effect of liquidity was the opposite of the expected result, possibly due to the defense industry\u27s different view on liquidity compared to previous research

Estimating an Acquisition Program’s Likelihood of Staying within Cost and Schedule Bounds

Program managers use prior experience to spot potential programmatic areas of concern. Augmenting this experience, the authors present an empirical procedure to estimate the likelihood of a program not exceeding two schedule and cost thresholds: (a) 15 percent of the initial total acquisition cost estimate from Milestone (MS) B to Initial Operating Capability (IOC); and (b) 15 percent of the estimated length (in months) between MS B and IOC—the second bound being 25 percent of the cost and schedule estimate. Using logistic regression and odds ratios, the authors analyze 49 Department of Defense programs and generally find that electronic system programs, extremely large programs (exceeding $17.5 billion in Base Year 2017 dollars), programs procuring smaller quantities of units, and programs with shorter schedules (less time from MS A to MS B and projected time from MS B to IOC) experience smaller percentages of cost growth and schedule slippage

A Learning Curve Model Accounting for the Flattening Effect in Production Cycles

We investigate production cost estimates to identify and model modifications to a prescribed learning curve. Our new model examines the learning rate as a decreasing function over time as opposed to a constant rate that is frequently used. The purpose of this research is to determine whether a new learning curve model could be implemented to reduce the error in cost estimates for production processes. A new model was created that mathematically allows for a “flattening effect,” which typically occurs later in the production process. This model was then compared to Wright’s learning curve, which is a popular method used by many organizations today. The results showed a statistically significant reduction in error through the measurement of the two error terms, Sum of Squared Errors and Mean Absolute Percentage Error

Cost Estimating Using a New Learning Curve Theory for Non-Constant Production Rates

Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced. However, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases. These diminishing learning rates cause traditional learning curves to underestimate required resources, potentially resulting in cost overruns. A diminishing learning rate model, namely Boone’s learning curve, was recently developed to model this phenomenon. This research confirms that Boone’s learning curve systematically reduced error in modeling observed learning curves using production data from 169 Department of Defense end-items. However, high amounts of variability in error reduction precluded concluding the degree to which Boone’s learning curve reduced error on average. This research further justifies the necessity of a diminishing learning rate forecasting model and assesses a potential solution to model diminishing learning rates

Improving Acquisitions In Science And Technology Programs: Creating Unique Cost Factors To Improve Resource Allocation Decisions

Acquisition Research Program Sponsored Report SeriesSponsored Acquisition Research & Technical ReportsCost factors are a common technique employed in Major Defense Acquisition Program (MDAP) cost estimating. The extant suite of available factors, however, primarily consists of development factors from the Engineering and Manufacturing Development (EMD) phase of the life cycle. This study expands the set of factors available to analysts by producing cost factors germane to programs early in the life cycle (i.e. Science and Technology (S&T) programs) and also creates factors for the Production phase of the life cycle. Cost factor development in S&T programs provides unique challenges due to non-standard reporting requirements. To meet these challenges, this study first mapped S&T cost data to create a new, suggested Work Breakdown Structure (WBS) that mirrors the WBS structure utilized in MDAPs via Mil-Std-881. From this, it was determined that two cost factors commonly utilized in MDAP estimates, Systems Engineering/Program Management (SE/PM) and Systems Test and Evaluation (ST&E) could be derived for the S&T programs. The creation of factors for the production phase of the life cycle resulted in 1033 new cost factors from a multitude of diverse programs. Factors were developed by commodity type (aircraft, missile, UAV, space, and ship), contract type (various), contractor type (prime and sub), and Service (Air Force, Army, and Navy). Combining the results of the previous EMD factors developed (Markman et al., 2019) with the two new phases developed here (S&T; Production) results in a robust cost factor toolkit across the acquisition life cycle spectrum.Approved for public release; distribution is unlimited.Approved for public release; distribution is unlimited

A New Learning Curve for Department of Defense Acquisition Programs: How to Account for the “Flattening Effect”

Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced; however, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases. These diminishing learning rates cause traditional learning curves to underestimate required resources, potentially resulting in cost overruns. A diminishing learning rate model, Boone’s Learning Curve (2018), was recently developed to model this phenomenon. This research confirmed that Boone’s Learning Curve is more accurate in modeling observed learning curves using production data of 169 Department of Defense end-items. However, further empirical analysis revealed deficiencies in the theoretical justifications of why and under what conditions Boone’s Learning Curve more accurately models observations. This research also discovered that diminishing learning rates are present but not pervasive in the sampled observations. Additionally, this research explored the theoretical and empirical evidence that may cause learning curves to exhibit diminishing learning rates and be more accurately modeled by Boone’s Learning Curve. Only a limited number of theory-based variables were useful in explaining these phenomena. This research further justifies the necessity of a diminishing learning rate model and proposes a framework to investigate learning curves that exhibit diminishing learning rates