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Delivery speed and product cost are critical to both our customers and our shareholders. Test cost has historically represented a third or more of overall product cost. Testing requires considerable time investments as well, especially given the nature of products in the aerospace domain, and their safety demands. In this paper we describe work in use today at a large aerospace manufacturer to optimize test and inspection operations in complex engineering products. We extend Deming's work from the theoretical to application by applying a decision tree and data analytics to test information, resulting in significant savings in dollars and time for test and inspection operations. A bill-of-materials plus operations visualization is employed to initially identify test and inspection operation candidates for removal, and then Deming's work is extended in this paper to determine the business case for removal, resulting in a final approval by experts driven by the underlying data. The decision tree is described, as well as algorithms to estimate failure rate and rework costs that are integral to applying Deming's analysis. A small set of business case results for removing an inspection and a test operation using the applied analysis are shared.

Manufacturing, in general, creates a finished good from a set of simpler supplied parts. Supplied parts are installed into higher assemblies, higher assemblies move into even higher assemblies, and eventually this terminates at the finished good. Delays or variation during the manufacturing process ripple all the way to the finished good, possibly from different branches of the build and possibly magnifying any individual effect. There is extensive literature regarding Lean Manufacturing and it provides strategies and business philosophy to deal with variation, however it offers little in the way of quantitative analysis on the effects of that variation upon the whole. Digital Twins and discrete event simulations can and have been used to model the impact of variation in its totality. Various papers on Digital Twins have explored how to model manufacturing, but very little on generalized behavior. (i.e. How schedule slips at the subassemblies impacts the delivery dates / quantities at the finished good level). This paper explores the analytical quantitative effects of input/sales variation through the manufacturing cycle and the resultant effect on the finished good manufacturing schedule/cycle. We demonstrate that even small random variations/interruptions propagate up the build chain, get reduced in magnitude and end up producing predictable reductions in the average build rate of the final product. Additionally, it is shown that the more supplied parts that comprise a finished good the greater the expected reduction in average build rate.

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