Characterizing Customer Ordering Behaviors in Semiconductor Supply Chains with Convolutional Neural Networks

Abstract

Advancements in the semiconductor industry have resulted in the need for extracting vital information from vast amounts of data. In the operational processes of demand planning and order management, it is important to understand customer demand data due to its potential to provide insights for managing supply chains. For this purpose, customer ordering behaviors are visualized in the form of two-dimensional heat maps. The goal is to classify the customers into predefined ordering patterns on the example of a semiconductor manufacturing, namely Infineon Technologies. Therefore, a convolutional neural network is used. By classifying the customers into preselected ordering patterns, a better understanding on how the customer demand develops over time is achieved. The results show that customers have a certain ordering pattern, but their behavior can be meaningfully classified only to a certain extend due to unidentified behaviors in the data. Further research could identify additional ordering patterns.