Detecting Low-Yield Machines in Batch Production Systems Based on Observed Defective Pieces

In batch production systems, detecting low-yield machines is essential for minimizing the production of defective pieces, which is a complex problem that currently requires multiple experts, considerable capital, or a combination of both to overcome. To solve this problem, we proposed a cost-efficient and straightforward method that involves using maximum likelihood estimation and bootstrap confidence intervals to estimate per-machine yield; this method enables identification of low-yield machines and generation of a list of these machines. Manufacturing engineers can use the list to perform necessary verification and maintenance processes. Before implementing this method, a manufacturer with 50–500 machines should build a dataset containing approximately 6–20 times as many batches as there are production machines. When this condition is met, the proposed method can be used effectively to detect up to five low-yield machines.