Abstract
Businesses that produce many complex products inevitably have disparate test requirements. Over time test solutions diverge despite having similarities contributing to increases in test cost. Mergers and acquisitions further complicate having common, well-understood, low-cost test platforms. By comparing the population of parameters tested across all products insight can be gained as to what groupings of parameters could have shared, common test platforms. The method described in this paper, in use today, describes a data mining and statistical approach to identify groups of test capability by testing ranges and limits, leveraging decades of historical testing data across a wide range of commercial products to design common test equipment at a large aerospace manufacturing organization. Clustering methods were used and evaluated to determine common factory test platform requirements, and compare them against existing test platform vendor capabilities. In conclusion, the construction of a database of historical testing data has become a foundational tool to develop a design for a common test platform.
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