Prediction of per-batch yield rates in production based on maximum likelihood estimation of per-machine yield rates

The demand for high-quality customized products compels manufacturers to adopt batch production. With the ability to accurately estimate batch production yield rates in advance, manufacturers can effectively plan the batch production process and control the production risk based on the estimated values. The per-batch production yield rates can be directly predicted by multiplying the accurately estimated per-machine yield rates corresponding to a batch. Unfortunately, for most manufacturers, the actual per-machine yield rates are difficult to estimate owing to a variety factors. Moreover, per-batch yield-rate prediction has received little attention because recent studies only focused on yield-rate prediction methods for single/continuous production systems. To address this, we propose an expectation-maximization-based approach to predict per-batch yield rates by estimating the per-machine yield rates. Based on the data from T-company, the proposed method could predict the per-batch yield rates for the subsequent week with an average accuracy of 91.86 %, and for five consecutive weeks with an average accuracy of more than 90 %. To further evaluate the performance of the proposed method with different batch production patterns, we conducted simulations to obtain the average accuracy of the estimated per-machine yield rates. In the simulations, the average prediction accuracy of the per-batch yield rates was 91.29 % in the batch production pattern, as in the case of T-company (similar to 250 machines and similar to 1000 batches per week), and it increased as the number of batches increased.

Automated summary

The demand for high-quality customized products drives manufacturers to adopt batch production. This study proposes an expectation-maximization-based approach to predict per-batch yield rates and evaluates its performance in different batch production patterns through simulations.