Authors: Ian D. MacLeod and Capt Robert A. Dinwoodie, USMC
A prime managerial concern is how to decide which investment alternatives provide the greatest return with least risk of loss. In civilian organizations, numerous methods and formulas assist these decisions. However, in military and other governmental agencies, these methods often fall short because typical governmental investments do not have a monetary return. The processes underpinning governmental resource allocation and acquisition decisions are often cumbersome and time consuming. In this article, the authors present a unique application of composite indexing methods to compare the return on investment in military equipment. They posit that this analytical method can improve government agencies’ investment decisions for capital equipment, especially when methods that are more laborious cannot be executed in the allotted time frame.
Authors: William F. Kramer, Mehmet Sahinoglu, and David Ang
This research article aims to identify and introduce cost-saving measures for increasing the return on investment during the Software Development Life Cycle (SDLC) through selected quantitative analyses employing both the Monte Carlo Simulation and Discrete Event Simulation approaches. Through the use of modeling and simulation, the authors develop quantitative analysis for discovering financial cost and impact when meeting future demands of an organization’s SDLC management process associated with error rates. Though this sounds like an easy and open practice, it is uncommon for most competitors to provide empirical data outlining their error rates associated with each of the SDLC phases nor do they normally disclose the impact of such error rates on the overall development effort. The approach presented in this article is more plausible and scientific than the conventional wait-and-see, whatever-fate-may-bring approach with its accompanying unpleasant surprises, often resulting in wasted resources and time.
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Authors: Reginald U. Bailey, Thomas A. Mazzuchi, Shahram Sarkani, and David F. Rico
The U.S. Department of Defense endorsed and later mandated the use of Technology Readiness Assessments (TRAs) and knowledge-based practices in the early 2000s for use as a tool in the management of program acquisition risk. Unfortunately, implementing TRAs can be costly, especially when programs include knowledge-based practices such as prototyping, performance specifications, test plans, and technology maturity plans. What is the economic impact of these TRA practices on the past and present acquisition performance of the U.S. Army, Navy, and Air Force? The conundrum today is that no commonly accepted approach is in use to determine the economic value of TRAs. This article provides a model for the valuation of TRAs in assessing the risk of technical maturity.
Authors: Ivar Oswalt, Tim Cooley, William Waite, Elliot Waite, Steve “Flash” Gordon, Richard Severinghaus, Jerry Feinberg, and Gary Lightner
As budgets decrease, it becomes increasingly important to determine the most effective ways to invest in modeling and simulation (M&S). This article discusses an approach to comparing different M&S investment opportunities using a return on investment (ROI)-like measure. The authors describe methods to evaluate “benefit” (i.e., increased readiness, more effective training, etc.) received from an investment and then use those metrics in a decision analysis framework to evaluate each M&S expenditure. Finally, they conclude by discussing the importance of viewing M&S investments from a Department of Defense (DoD) Enterprise view, evaluating investment over multiple years, measuring well-structured metrics, and using those metrics in a systematic way to produce an ROI-like result that DoD can use to evaluate and prioritize M&S investments.