Hierarchical ensemble deep learning for data-driven lead time prediction

Aslan, Ayse and Vasantha, Gokula and El-Raoui, Hanane and Quigley, John and Hanson, Jack and Corney, Jonathan and Sherlock, Andrew (2023) Hierarchical ensemble deep learning for data-driven lead time prediction. The International Journal of Advanced Manufacturing Technology, 128 (9-10). pp. 4169-4188. ISSN 1433-3015 (https://doi.org/10.1007/s00170-023-12123-4)

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Abstract

This paper focuses on data-driven prediction of lead times for product orders based on the real-time production state captured at the arrival instants of orders in make-to-order production environments. In particular, we consider a sophisticated manufacturing system where a large number of measurements about the production state are available (e.g. sensor data). In response to this complex prediction challenge, we present a novel ensemble hierarchical deep learning algorithm comprised of three deep neural networks. One of these networks acts as a generalist, while the other two function as specialists for different products. Hierarchical ensemble methods have previously been successfully utilised in addressing various multi-class classification problems. In this paper, we extend this approach to encompass the regression task of lead time prediction. We demonstrate the suitability of our algorithm in two separate case studies. The first case study uses one of the largest manufacturing datasets available, the Bosch production line dataset. The second case study uses synthetic datasets generated from a reliability-based model of a multi-product, make-to-order production system, inspired by the Bosch production line. In both case studies, we demonstrate that our algorithm provides high-accuracy predictions and significantly outperforms selected benchmarks including the single deep neural network. Moreover, we find that prediction accuracy is significantly higher in the synthetic dataset, which suggests that there is complexity (i.e. subtle interactions) in industrial manufacturing processes that are not easily reproduced in artificial models.

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