RISK MODELING


FIGURE 3


involved in the complexities of acquisi- tion, but it resonates particularly with the AMSAA risk IPT. Team members try continuously to enhance their risk assess- ment approaches to ensure that they are reporting the significant ramifications of all critical sources of schedule risk in a realistic, unbiased and rational manner.


Any method of executing a risk assessment must be supportable in the time frame allotted by AoA guidance. Methods also must be consistent and repeatable for each new AoA. Te main objective is to deliver the most useful risk information possible to decision-makers so they can make fully informed decisions that will lead to bal- anced costs, benefits and prudent risks.


Te risk team faces the challenges of increasing the quantity of objective data available for risk assessments, and ensur- ing the data’s quality with respect to using the information within a model. Te team continues its research to establish a better understanding of the critical factors that create schedule risk and a clearer picture of how best to assess those risks through the use of historical data and SME judgments.


Te risk IPT continuously seeks to tap the expertise of other acquisition orga- nizations to help develop risk assessment models. With this kind of collaboration, the team will proceed toward its goal of providing the best possible product—an independent, honest and accurate sched- ule risk assessment.


CONCLUSION David Vose, a consultant in risk analy- sis, noted in his book “Risk Analysis: A Quantitative Guide” that “Te biggest uncertainty in risk analysis is whether we started off analyzing the right thing and in the right way.” Te AMSAA risk team, accustomed to addressing this uncer- tainty, is preparing to embark on more


74 Army AL&T Magazine April–June 2014


1.0 0.9


0.8 0.7 0.6 0.5 0.4 0.3 0.2


0.1 0.0


0 12 24 36 PM Estimate 48 60 72 84 96 Months SRDDM AND SREDM MODEL OUTPUTS


This conceptual plot shows the probability that a notional new-vehicle program will complete its EMD phase within the 50 months allotted in the PM’s program schedule. Each curve plotted in the graph represents a different model and excursion. Model 1 represents simulations using only historical event data. The three excursions within Model 1 (Levels 1, 2 and 3) refer to the schedule events included in the modeling; each increase in level represents the addition of more detailed schedule events. Model 2 used historical delay data to show the effect of potential delays on a schedule. Of the two notional Model 2 excursions, delays had no chance of occurring in one, whereas each delay had a 50 percent chance of occurring in the other. Model 3 represents simulations that combined SME input and historical event data. (SOURCE: AMSAA risk IPT)


108 120 132 144 156 168 180 SRDDM


SRDDM lower confidence bound SREDM Model 1 (Level 1) SREDM Model 1 (Level 2) SREDM Model 1 (Level 3) SREDM Model 2 (0% Delays) SREDM Model 2 (50% Delays) SREDM Model 3


model research and development activi- ties to improve the quality of its methods.


Many challenges still need to be addressed. In due course, the AMSAA team aims to be a strong link in the chain of all the dedicated analysts who are serving to make better acquisition decisions to safeguard and equip the warfighter.


For more information, go to http://web. amsaa.army.mi l /RiskAs sessment . html or contact the leader of AMSAA’s risk team, Suzanne Singleton, at Suzanne.r.singleton.civ@mail.mil or 410-278-2049.


MR. TIMOTHY E. BISCOE is an operations


Maryland. He is Level II certified in engi- neering and is a member of the U.S. Army Acquisition Corps (AAC).


MR. ANDREW B. CLARK is a computer scientist at AMSAA. He holds an M.S. in computer science from Johns Hopkins Uni- versity and a B.S. in computer science from Towson University. He is Level II certified in engineering and is a member of the AAC.


research analyst at AMSAA,


Aberdeen Proving Ground (APG), MD. He holds an M.S. in project management with a concentration in operations research from the Florida Institute of Technology and a B.S. in mathematics with a concen- tration in statistics from the University of


MR. JOHN S. NIERWINSKI is a math- ematician and statistician at AMSAA. He is also an adjunct professor at the Florida Institute of Technology, APG campus. He holds an M.S. in operations research from the Florida Institute of Technology and a B.S. in mathematics with a concentra- tion in actuarial sciences from Towson University. He also holds U.S. Patent No. 8,335,660, issued Dec. 18, 2012. He has authored professional journal articles and numerous technical reports on various research topics and methodologies. He is Level II certified in engineering and is a member of the AAC.


Probability of Completing EMD Phase


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