Product life cycles are becoming shorter, especially in the optoelectronics industry. Shortening production cycle times using knowledge obtained in pilot runs, where sample sizes are usually very small, is thus becoming a core competitive ability for firms. Machine learning algorithms are widely applied to this task, but the number of training samples is always a key factor in determining their knowledge acquisition capability. Therefore, this study, based on box-and-whisker plots, systematically generates more training samples to help gain more knowledge in the early stages of manufacturing systems. A case study of a TFT-LCD manufacturer is taken as an example when a new product was phased-in in 2008. The experimental results show that it is possible to rapidly develop a production model that can provide more information and precise predictions with the limited data acquired from pilot runs.
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