A tree-based-trend-diffusion prediction procedure for small sample sets in the early stages of manufacturing systems

Product life cycles are becoming shorter, especially in the electronics industry. The issue of time to market has thus become a core competency for firms to increase market share. In order to shorten the cycle time from product design to mass production, engineers must often make decisions under uncertain conditions with limited information. Although machine learning algorithms can help derive useful information, the smallest training sample size required to establish robust models is important to know, as with insufficient data size the models produced may be unreliable. This research develops a two-phase procedure for small-data-set learning problems at pilot run stage and takes the multi-layer ceramic capacitor case as an example to figure out a precise model which concretely represents the learned process knowledge to help shorten the lead-time before mass production. The results reveal that it is possible to rapidly develop a model of production with limited data from pilot runs.