Abstract
The timely and predictable cost of the Lynx x-ray mirror assembly (XMA) is an essential element of the mission concept. We present an analytic model for the cost, schedule, and risk for the manufacture of a generalized system of many parts, and apply it to preliminary data of the manufacturing process for the XMA. The manufacturing process is modeled as a series of G / G / w queues. The optimization of the manufacturing process, to minimize total process time, comes from the selection of the value of w, the number of servers performing each step in the manufacturing process, to avoid bottlenecks and minimizing idle servers. This analysis also includes the effects of finite process yield on cost and schedule. The cost model is parameterized by the various elements of cost, including the production time, thus linking the cost and schedule models. The system of coupled equations is the cost and schedule model. The process data that must be collected on the manufacturing process during the ongoing technology development process such as process times, yields, and distributions is identified. We conclude with the next steps that will be taken to make this analysis more complete.
Highlights
Each of the four major decadal missions currently underway to support the Astro 2020 decadal review[1] has a major challenge to overcome for that mission to be determined to be executable
The big fundamental problem (BFP) for Lynx is the cost-effective manufacture of the Lynx x-ray mirror assembly (XMA) of suitable quality to meet the science objectives of the mission
This paper presents the derivation of the system of equations that predict the cost and duration of manufacture of the XMA
Summary
Each of the four major decadal missions currently underway to support the Astro 2020 decadal review[1] has a major challenge to overcome for that mission to be determined to be executable. This paper will show that there is a solid analytic framework for establishing a rigorous management process to produce the XMA in a cost-efficient and effective manner. Failure to imagine this analytic model makes the analysis of strategic options and risks challenging at best and cost and schedule an exercise in reporting, not proactive management. The model development begins by building on some initial results presented in earlier work expanding the illustrative, but nonrealistic, example of an entirely deterministic XMA manufacturing process. The process used to develop and optimize the AXAF (Chandra) x-ray calibration test schedule and efficiency[7] is very similar to the approach discussed in this paper
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