Customer Order Behavior Classification Via Convolutional Neural Networks in the Semiconductor Industry

Abstract

In the operational processes of demand planning and order management, it is crucial to understand customer order behavior to provide insights for supply chain management processes. Here, advances in the semiconductor industry have emerged through the extraction of important information from vast amounts of data. This new data and information availability paves the way for the development of improved methods to analyze and classify customer order behavior (COB). To this end, we develop a novel, sophisticated yet intuitive image-based representation for COBs using two-dimensional heat maps. This heat map representation contributes significantly to the development of a novel COB classification framework. In this framework, we utilize data enrichment via synthetical training samples to train a CNN model that performs the classification task. Integrating synthetically generated data into the training phase allows us to strengthen the inclusion of rare pattern variants that we identified during initial analysis. Moreover, we show how this framework is used in practice at Infineon. We finally use actual customer data to benchmark the performance of our framework and show that the baseline CNN approach outperforms all available state-of-the-art benchmark models. Additionally, our results highlight the benefit of synthetic data enrichment.