Edward D. White

Estimation of Cost Contingency for Air Force Construction Projects

Risk and associated cost overruns are critical problems for construction projects, yet the most common practice for dealing with them is the assignment of an arbitrary flat percentage of the construction budget as a contingency fund. Therefore, our goal was to identify significant variables that may influence, or serve as indicators of, potential cost overruns. We analyzed data from 203 Air Force construction projects over a full range of project types and scopes using multiple linear regression to develop a model to predict the amount of required contingency funds. The proposed model uses only data that would be available prior to the award of a construction contract. The variables in the model were categorized as project characteristics, design performance metrics, and contract award process influences. Based on the performance metric used, the model captures 44% of actual cost overruns versus the 20% captured by the current practice. Furthermore, application of the model reduces the average contingency budgeting error from 11.2 to only 0.3%.

A Multiple-Task Measurement Framework for Assessing Maximum-Typical Performance

This study presents a novel measurement framework for assessing and predicting maximum and typical performance. The proposed measurement approach addresses the need for organizations to assess maximum and typical performance changes over time in complex job settings requiring coordination of multiple tasks with changing priorities. We present results of an experiment in which participants engaged in a complex task with multiple task elements and instructions to either maximize a different task element in each of four performance blocks (variable-priority condition) or treat all task elements with equal priority (stable-priority condition). We estimated growth curves corresponding to each task element and calculated the area under each growth curve as a summary performance index. Growth curves corresponding to the maximized, high-priority task element in the variable-priority condition reflected maximum performance, whereas those corresponding to the deemphasized, lower priority elements reflected typical performance. We compared the shape of the maximum and typical growth curves in the variable-priority condition to their corresponding performance trajectories in the stable-priority condition. In addition, we tested the moderating influence of individual differences in action-state orientation on the obtained maximum and typical performance estimates. Results indicated support for the proposed measurement framework in terms of its usefulness for inducing sustained levels of maximum performance and for identifying and correcting sources of the maximum-typical performance discrepancy.

Improved tools for indoor ZigBee warwalking

Secure ZigBee wireless sensor and control networks use 128-bit AES encryption to defend against message sniffing and unauthorized access. However, the low cost and low complexity of ZigBee devices makes them vulnerable to physical attacks such as tampering and network key extraction. Network administrators and penetration testers require tools such as Zbfind to accurately locate ZigBee hardware and evaluate physical security. The open source Zbfind tool estimates distance to ZigBee devices in real time using received signal strength and a distance prediction model. We collect 4500 signal strength measurements along nine walking paths toward ZigBee transmitters in three office buildings. We find that the log-distance path loss model used by Zbfind predicts transmitter distance with 92.5% mean absolute percentage error. We construct an alternative linear model that reduces error to 21%.

Minimum sample sizes for identifying chromosomal fragile sites from individuals: Monte Carlo estimation

Abstract A Monte Carlo simulation procedure was used to estimate the exact level of the standardized X2 test statistic (Xs2) for randomness in the FSM methodology for the identification of fragile sites from chromosomal breakage data for single individuals. A random-number generator was used to simulate 10 000 chromosomal breakage data sets, each corresponding to the null hypothesis of no fragile sites for numbers of chromosomal breaks (n) from 1 to 2000 and at three levels of chromosomal band resolution (k). The reliability of the test was assessed by comparisons of the empirical and nominal α levels for each of the corresponding values of n and k. These analyses indicate that the sparse and discrete nature of chromosomal breakage data results in large and unpredictable discrepancies between the empirical and nominal α levels when fragile site identifications are based on small numbers of breaks (n < 0.5 k). With n≥ 0.5 k, the distribution of Xs2 appears to be stable and non-significant differences in the empirical and nominal α levels are generally obtained. These results are inherent to the nature of the data and are, therefore, relevant to any statistical model for the identification of fragile sites from chromosomal breakage data. For FSM identification of fragile sites at α = 0.05, we suggest that n≥ 0.5 k is the minimum reliable number of mapped chromosomal breaks per individual.

Using Logistic and Multiple Regression to Estimate Engineering Cost Risk

Abstract This study explores a two-step regression procedure for assessing defense acquisition program cost growth using programmatic data from the Selected Acquisition Reports (SARs) between 1990 and 2000. We focus our analysis on cost growth in research and development dollars for the Engineering Manufacturing Development phase of the acquisition life cycle, specifically engineering cost growth. We illustrate the use of logistic regression in cost analysis to predict whether cost growth will occur. Given a program has a high likelihood of cost growth, we then use a log-transformed model to predict the amount of cost growth. Using this methodology, we produce statistically significant models highlighting the viability of this technique for cost analysts to consider and to adopt for future uses.

Weibull-Based Forecasting of R&D Program Budgets

Abstract Norden (1970) demonstrates that the Rayleigh function can model manpower on research and development (R&D) programs. Several research efforts extend his work to modeling R&D program expenditures. The Rayleigh distribution, which is a degenerative of the Weibull distribution, suffers from two theoretical limitations that make the Weibull function a better model for R&D program expenditures. Using 128 completed R&D programs, we develop regression models to predict the requisite Weibull scale and shape parameters. To determine the Weibull models budget profile forecasting capability, we compare the completed R&D program budget profiles to Weibull modeled budget profiles and report an average correlation of 0.607. To determine the significance of our results we compare the same 128 completed program budget profiles to Rayleigh modeled budget profiles. Using the Weibull in lieu of the Rayleigh model we improve initial budget profile projections on average 60 percent.

Investigating the correlation of the U.S. Air Force Physical Fitness Test to combat-based fitness: a women-only study.

The primary objective in this research involves determining the Air Force Physical Fitness Tests (AFPFT) predictability of combat fitness and whether measures within the AFPFT require modification to increase this predictability further. We recruited 60 female volunteers and compared their performance on the AFPFT to the Marine Combat Fitness Test, the proxy for combat fitness. We discovered little association between the two (R(2) of 0.35), however, this association significantly increased (adjusted R(2) of 0.56) when utilizing the raw scores of the AFPFT instead of using the gender/age scoring tables. Improving on these associations, we develop and propose a simple ordinary least squares regression model that minimally impacts the AFPFT testing routine. This two-event model for predicting combat fitness incorporates the 1.5-mile run along with the number of repetitions of a 30-lb dumbbell from chest height to overhead with arms extended during a 2-minute time span. These two events predicted combat fitness as assessed by the Marine Combat Fitness Test with an adjusted R(2) of 0.82. By adopting this model, we greatly improve the Air Forces ability to assess combat fitness for women.

Augmenting supersaturated designs with Bayesian D-optimality

A methodology is developed to add runs to existing supersaturated designs. The technique uses information from the analysis of the initial experiment to choose the best possible follow-up runs. After analysis of the initial data, factors are classified into one of three groups: primary, secondary, and potential. Runs are added to maximize a Bayesian D -optimality criterion to increase the information gained about those factors. Simulation results show the method can outperform existing supersaturated design augmentation strategies that add runs without analyzing the initial response variables.

R&D Budget-Driven Cost and Schedule Overruns

Abstract In this article, we postulate and test that, if the initial budget supports Rayleigh-distributed expenditures, then less cost or schedule growth occurs. Norden (1970) shows that, if effort on a project is a function of linear improvement in skills and diminishing remaining tasks, then the cumulative efforts follows with the Rayleigh cumulative distribution function, which is a special case of the Weibull distribution. Norden demonstrates that manpower on research and development (R&D) programs can be modeled with the Rayleigh distribution. Numerous researchers successfully fit Rayleigh models to completed R&D program expenditures for a variety of technologies. Even if the initial program budget does not support the rapid increase and long tail of Rayleigh-distributed expenditures, then the program may still finish with Rayleigh-distributed expenditures through budget increases or program extensions. In this article, we evaluate whether the cost and schedule growth for R&D programs can be determined by how well the initial R&D program budget supports Rayleigh-distributed expenditures. We measure how well the expenditures from the initial budgets follow a Rayleigh distribution in two ways, by the values of the least squares Weibull parameters and by several goodness-of-fit statistics. We regress the values for 37 completed R&D defense programs and find our models explain 53.4% of cost-overrun and 50.5% of percent schedule-slip variations. Considering that funding is only one of many factors that can result in program growth, we contend these results demonstrate the significant impact of the proposed budget for completing R&D programs in their scheduled time and at their projected cost.

A Two-Pronged Approach to Estimate Procurement Cost Growth in Major DoD Weapon Systems

Abstract This study builds on the research previously conducted by White et al. (2004), who demonstrated the use of a two-step logistic and multiple regression methodology to predict research and development cost growth. This research uses compiled programmatic data from the Selected Acquisition Reports (SARs) between 1990 and 2002 for programs covering all defense departments. The analysis concentrates on cost growth in the procurement appropriations of the Engineering and Manufacturing Development phase of acquisition. This study investigates and confirms the usefulness of using logistic and multiple regression models to predict whether or not cost growth will occur in a major DoD weapon system program and if so how much cost growth will occur. The analysis focuses on the estimating and support SAR cost variance categories within the procurement appropriations and develops predictive models for each.