Deep Forest-based Product Completion Time Prediction Method in Discrete Manufacturing Industry: A Case Study

摘要

Product completion time prediction has important implications for make-to-order discrete manufacturing firms. In this paper, the product completion prediction problem coming from a practical company is considered, where the process data are with poor-quality, small number of features, category-dominated features, and low correlation between features and labels. Firstly, the characteristics of the provided process data is mined and preprocessed; and then a Deep Forest-based method is presented to predict the product completion time. Experimental results show that the method proposed in this paper achieves good performance.