Semantic Context Information Modeling With Neural Networks in Customer Order Behavior Classification

Demand planning in the semiconductor industry can be complicated due to challenges such as extended cycle times, rapid innovation cycles, and the Bullwhip Effect. Approaches that provide a deeper understanding of customer orders and their associated demand are crucial to enhance demand planning accu...

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Bibliographic Details
Published in:IEEE transactions on semiconductor manufacturing 2023-11, Vol.36 (4), p.570-577
Main Authors: Ulrich, Philipp, Ramzy, Nour, Ratusny, Marco
Format: Article
Language:English
Subjects:
Artificial neural networks
Classification
Context
Customers
Cycle time
Data models
Demand
Graphical representations
Heat maps
Image classification
Knowledge graphs
Knowledge representation
Modelling
Neural networks
Ontologies
Performance evaluation
semantic networks
Semantics
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Summary:Demand planning in the semiconductor industry can be complicated due to challenges such as extended cycle times, rapid innovation cycles, and the Bullwhip Effect. Approaches that provide a deeper understanding of customer orders and their associated demand are crucial to enhance demand planning accuracy. Previous studies have employed convolutional neural networks (CNNs) on heat map representations of customer order transactions to effectively classify customer order behaviors (COBs), leading to improved insights into customer behavior. However, these approaches have primarily focused on analyzing customer order patterns without considering contextual information, such as financial or market-related data, which can benefit the classification process. Therefore, we propose a Semantic Context Information Modeling methodology for Neural Networks (SCIM-NN) based on ontologies, knowledge graph embeddings, and multi-stream neural networks to include context information for a classification task. We show the application of SCIM-NN on a use case in the domain of COB and evaluate the performance of the context-aware model on customer data of Infineon Technologies AG. Results indicate that including context information improves the overall classification performance compared to a benchmark CNN.
ISSN:0894-6507
1558-2345
DOI:10.1109/TSM.2023.3320870