Scholarlink

<p>Accurate demand forecasting is vital for optimizing supply chain management and enhancing organizational resilience. Traditional forecasting methods, relying on simple arithmetic, often fail to capture complex patterns caused by seasonal variability and special events. Although deep learning techniques have advanced, the lack of interpretable models hampers understanding and explaining predictions. We introduce the Multi-Channel Data Fusion Network (MCDFN), a novel hybrid deep learning architecture integrating multiple data modalities for superior demand forecasting. MCDFN utilizes Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs) to extract spatial and temporal features from time series data. Comparative benchmarking against seven other deep-learning models validates MCDFN’s efficacy, showing it outperforms its counterparts across key metrics with a mean squared error (MSE) of 23.5738, root mean squared error (RMSE) of 4.8553, mean absolute error (MAE) of 3.9991, and mean absolute percentage error (MAPE) of 20.1575%. Theil’s U statistic of 0.1181 (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12065_2025_1053_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="46" /> </InlineMediaObject> <EquationSource Format="TEX">\(U&lt;1\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>U</mi> <mo>&lt;</mo> <mn>1</mn> </mrow> </math></EquationSource> </InlineEquation>) of MCDFN indicates its superiority over the naive forecasting approach, and a 10-fold cross-validated statistical paired <i>t</i>-test with a <i>p</i>-value of 5% indicated no significant difference between MCDFN’s predictions and actual values. To address the “black box” nature of MCDFN, we employ explainable AI techniques such as ShapTime and Permutation Feature Importance, offering insights into model decision-making processes. This research advances demand forecasting methodologies and provides practical guidelines for integrating MCDFN into existing supply chain systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月30日

查看全文
  • Md Abrar Jahin,
  • Asef Shahriar,
  • Md Al Amin

摘要

Accurate demand forecasting is vital for optimizing supply chain management and enhancing organizational resilience. Traditional forecasting methods, relying on simple arithmetic, often fail to capture complex patterns caused by seasonal variability and special events. Although deep learning techniques have advanced, the lack of interpretable models hampers understanding and explaining predictions. We introduce the Multi-Channel Data Fusion Network (MCDFN), a novel hybrid deep learning architecture integrating multiple data modalities for superior demand forecasting. MCDFN utilizes Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs) to extract spatial and temporal features from time series data. Comparative benchmarking against seven other deep-learning models validates MCDFN’s efficacy, showing it outperforms its counterparts across key metrics with a mean squared error (MSE) of 23.5738, root mean squared error (RMSE) of 4.8553, mean absolute error (MAE) of 3.9991, and mean absolute percentage error (MAPE) of 20.1575%. Theil’s U statistic of 0.1181 ( \(U<1\) U < 1 ) of MCDFN indicates its superiority over the naive forecasting approach, and a 10-fold cross-validated statistical paired t-test with a p-value of 5% indicated no significant difference between MCDFN’s predictions and actual values. To address the “black box” nature of MCDFN, we employ explainable AI techniques such as ShapTime and Permutation Feature Importance, offering insights into model decision-making processes. This research advances demand forecasting methodologies and provides practical guidelines for integrating MCDFN into existing supply chain systems.