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

Abstract

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.