Bradley C. Boehmke

Tooth-to-Tail Impact Analysis: Combining Econometric Modeling and Bayesian Networks to Assess Support Cost Consequences Due to Changes in Force Structure

Current constraints in the fiscal environment are forcing the Air Force, and its sister services, to assess force reduction considerations. With significant force reduction comes the need to model and assess the potential impact that these changes may have on support resources. Previous research has remained heavily focused on a ratio approach for linking the tooth and tail ends of the Air Force cost spectrum and, although recent research has augmented this literature stream by providing more statistical rigor behind tooth-to-tail relationships, an adequate decision support tool has yet to be explored to aid decision-makers. The authors of this research directly address this concern by introducing a systematic approach to perform tooth-to-tail policy impact analysis. First, multivariate linear regression is applied to identify relationships between the tooth and tail. Then, a novel decision support system with Bayesian networks is introduced to model the tooth-to-tail cost consequences while capturing the uncertainty that often comes with such policy considerations. Through scenario analysis, the authors illustrate how a Bayesian network can provide decision-makers with (i) the ability to model uncertainty in the decision environment, (ii) a visual illustration of cause-and-effect impacts, and (iii) the ability to perform multi-directional reasoning in light of new information available to decision-makers.

The influence of operational resources and activities on indirect personnel costs: A multilevel modeling approach

ABSTRACT Indirect activities often represent an underemphasized, yet significant, contributing source of costs for organizations. In order to manage indirect costs, organizations must understand how these costs behave relative to changes in operational resources and activities. This is of particular interest to the Air Force and its sister services, because recent and projected reductions in defense spending are forcing reductions in their operational variables, and insufficient research exists to help them understand how this may influence indirect costs. Furthermore, although academic research on indirect costs has advanced the knowledge behind the modeling and behavior of indirect costs, significant gaps in the literature remain. Our research provides important and timely advances to the indirect cost literature. First, our research disaggregates the indirect cost pool and focuses on indirect personnel costs, which represent 33% of all Air Force indirect costs and are a leading source of indirect costs in many organizations. Second, we employ a multilevel modeling approach to capture the hierarchical nature of an enterprise, allowing us to assess the influence that each level of an organization has on indirect cost behavior and relationships. Third, we identify the operational variables that influence indirect personnel costs in the Air Force enterprise, providing Air Force decision-makers with evidence-based knowledge to inform decisions regarding budget reduction strategies.

Bending the Cost Curve: Moving the Focus from Macro-level to Micro-level Cost Trends with Cluster Analysis

“Bending the cost curve” has become the ambiguous jargon employed in recent years to emphasize the notion of changing unwanted cost trends. In response to the planned

Cyber anomaly detection: Using tabulated vectors and embedded analytics for efficient data mining

1 trillion Department of Defense budget reduction over the next six years, the Air Force has launched its own Bending the Cost Curve initiative in an effort to reduce cost growth. A principal concern with Bending the Cost Curve initiatives and research to date is the central focus on aggregate cost trajectories which can obscure the true underlying growth curves which require attention. In response, the authors apply a novel growth curve clustering approach to identify underlying cost curve behavior across the Air Force enterprise. They find that micro-level growth curves vary greatly from the aggregate cost curves. Furthermore, they illustrate how this approach can help decision-makers to direct their focus, proposals, and policy actions toward specific growth curves that must be “bent.”

learningCurve: An implementation of Crawford's and Wright's learning curve production functions

Firewalls, especially at large organizations, process high velocity internet traffic and flag suspicious events and activities. Flagged events can be benign, such as misconfigured routers, or malignant, such as a hacker trying to gain access to a specific computer. Confounding this is that flagged events are not always obvious in their danger and the high velocity nature of the problem. Current work in firewall log analysis is manual intensive and involves manpower hours to find events to investigate. This is predominantly achieved by manually sorting firewall and intrusion detection/prevention system log data. This work aims to improve the ability of analysts to find events for cyber forensics analysis. A tabulated vector approach is proposed to create meaningful state vectors from time-oriented blocks. Multivariate and graphical analysis is then used to analyze state vectors in human–machine collaborative interface. Statistical tools, such as the Mahalanobis distance, factor analysis, and histogram matrices, are employed for outlier detection. This research also introduces the breakdown distance heuristic as a decomposition of the Mahalanobis distance, by indicating which variables contributed most to its value. This work further explores the application of the tabulated vector approach methodology on collected firewall logs. Lastly, the analytic methodologies employed are integrated into embedded analytic tools so that cyber analysts on the front-line can efficiently deploy the anomaly detection capabilities.

KraljicMatrix: An R package for implementing the Kraljic Matrix to strategically analyze a firm’s purchasing portfolio

learningCurve is an R package (R Core Team (2016)) that implements common learning curve production functions. It incorporates Wright’s (Wright (1936)) and Crawford’s (Crawford (1944)) learning curve functions to compute unit and cumulative block estimates for time (or cost) of units along with an aggregate learning curve. It also provides delta and error functions and some basic learning curve plotting functions.along with functions to compute aggregated learning curves, error rates, and to visualize learning curves.

Transforming Your Data with dplyr

KraljicMatrix is an R package (R Core Team (2016)) that implements a quantified approach to the Kraljic Matrix (Kraljic (1983)) introduced by Montgomery et al. (Montgomery, Ogden, and Boehmke (2017)). It allows a firm to strategically analyze its purchasing portfolio with singleand multi-attribute value analysis to measure purchasing characteristics. In addition KraljicMatrix also provides useful functions to identify the preferred single utility slope based on subject matter expert inputs, assign and place purchases within the Kraljic Matrix, and perform sensitivity analysis.

The Role of Data Wrangling

Transforming your data is a basic part of data wrangling. This can include filtering, summarizing, and ordering your data by different means. This also includes combining disparate data sets, creating new variables, and many other manipulation tasks. Although many fundamental data transformation and manipulation functions exist in R, historically they have been a bit convoluted and lacked a consistent and cohesive code structure. Consequently, Hadley Wickham developed the very popular package to make these data processing tasks more efficient along with a syntax that is consistent and easier to remember and read.

Simplify Your Code with

Synonymous to Samuel Taylor Coleridge’s quote in Rime of the Ancient Mariner, the degree to which data are useful is largely determined by an analyst’s ability to wrangle data. In spite of advances in technologies for working with data, analysts still spend an inordinate amount of time obtaining data, diagnosing data quality issues and pre-processing data into a usable form. Research has illustrated that this portion of the data analysis process is the most tedious and time consuming component; often consuming 50–80 % of an analyst’s time (cf. Wickham 2014; Dasu and Johnson 2003). Despite the challenges, data wrangling remains a fundamental building block that enables visualization and statistical modeling. Only through data wrangling can we make data useful. Consequently, one’s ability to perform data wrangling tasks effectively and efficiently is fundamental to becoming an expert data analyst in their respective domain.

Dealing with Dates

Removing duplication is an important principle to keep in mind with your code; however, equally important is to keep your code efficient and readable. Efficiency is often accomplished by leveraging functions and control statements in your code. However, efficiency also includes eliminating the creation and saving of unnecessary objects that often result when you are trying to make your code more readable, clear, and explicit. Consequently, writing code that is simple, readable, and efficient is often considered contradictory. For this reason, the package is a powerful tool to have in your data wrangling toolkit.