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A Puzzle-Based BiLSTM Model for Accurate Supply Chain Demand Forecasting
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DOI：10.1587/transfun.2025EAP1113.png)
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Abstract
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Accurately predicting demand and adapting to rapid market changes are common difficulties for enterprise supply chain management. Traditional forecasting methods and even many existing deep learning models often fail to capture complex dependencies across multiple temporal features. This results in limited accuracy and delayed adjustments, which directly impact inventory, logistics, and profitability. To overcome these issues, we introduce a puzzled BiLSTM (PZ-BiLSTM) model, a specialized deep learning architecture designed for supply chain forecasting. Instead of traditional BiLSTM, our approach integrates structured feature blocks and temporal alignment techniques, which allows the model to identify both short-term fluctuations and long-term trends more effectively. The simulation of the model is performed using the publicly available Walmart sales dataset. When compared with existing demand forecasting models, our puzzled BiLSTM achieved an R2 of 0.9708, demonstrating its superior predictive performance. This model not only improves forecast precision but also enables real-time adjustment in supply chain decisions by combining the strengths of bidirectional sequence modelling with a dynamic puzzle-based feature integration strategy.
Keywords:
supply chain forecasting
demand prediction
deep learning
real-time adjustment
bidirectional long-term short-term memory
temporal feature alignment
predictive performance
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