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Experimentation Estimating Toolkit

Models Track

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Experimentation is a discovery process that evaluates benefits of new technologies or concepts by simulating operations and assessing the outcomes. This process provides a deeper understanding of issues surrounding the problem area, as well as more insight into cause and effect. Experimentation efforts can range from relatively small-scale efforts to highly complex events involving large numbers of participants. In order to estimate the cost of an experimentation effort, the scope of the experiment must be well understood. In our Experimentation Estimating Project, we developed a high level WBS to be used for scoping the experimentation effort as well as estimating the cost. Data was collected from various sources, often in diverse formats, representing different experimentation efforts ranging from simple to complex endeavors. Cost estimating relationships (CERs) were developed and calibrated against additional experimentation efforts. A model to estimate future experimentation efforts was developed based on historical data and analyses. This presentation provides a background on experimentation, an approach to estimating experimentation efforts, the data collection and analysis methodologies, and experiment cost model development, as well as any hurdles experienced and lessons learned.


Karen Mourikas
Karen Mourikas is an Operations and Systems Analyst at The Boeing Company in Huntington Beach, CA. Currently, she supports the Analysis, Modeling, Simulation and Experimentation (AMSE) group in Phantom Works focusing on Experimentation. This group designs and conducts experiments to explore and analyze questions of interest to customers. Previously, Karen worked in the Affordability organization within Boeing Research and Technology estimating Life Cycle Costs, analyzing Cost Uncertainty, and performing Cost Effectiveness Trades. Combining these past experiences lead to the development of a cost model to help scope and estimate Experimentation costs. Karen holds a BA in Mathematics and Computer Science from Connecticut College, and two MS degrees from University of Southern California, one in Applied Mathematics and the other in Operations Research Engineering.

Denise Nelson
Denise Nelson has been an Affordability analyst at the Boeing Company in Huntington Beach, CA since 2004. As part of Boeings Research and Technology division, Denise works multiple programs and projects with the bulk of her experience being GPS systems. She is active in the Boeing System Engineering Function and serves on the training committee. Prior to her work at Boeing, Denise taught math courses at Cal Poly Pomona, first as a Teaching Associate, then as an Adjunct Faculty member. Denise holds a BS in Statistics and a MS in Pure Math.