Methods for Predicting the Repair Stack in an Electronics Module Manufacturing Company

This article addresses the problem of predicting the workload of the repair department in a company manufacturing electronic modules. The number of modules needing repair is called a repair stack. A deterministic algorithm and a machine learning-based algorithm are proposed to predict the repair stack for subsequent weeks based on historical data, current yield data, and planned production. These methods allow for estimation of the repair stack and appropriate selection of repair department staff to ensure the ongoing repair of defective products. The proposed algorithms are described and the results of their practical verification based on historical data from a large enterprise are presented. The practical utility of both algorithms is demonstrated and the impact of selected factors on their accuracy is analyzed. It is shown that using the proposed algorithms, it is possible to predict the repair stack for the coming week with a relative error not exceeding a few percentages on the basis of historical data from the previous 8 weeks. These algorithms were successfully implemented in industrial practice.