MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network model
<p dir="ltr">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. A...
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2025
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| _version_ | 1864513533570449408 |
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| author | Md Abrar Jahin (20108252) |
| author2 | Asef Shahriar (22504103) Md Al Amin (20923805) |
| author2_role | author author |
| author_facet | Md Abrar Jahin (20108252) Asef Shahriar (22504103) Md Al Amin (20923805) |
| author_role | author |
| dc.creator.none.fl_str_mv | Md Abrar Jahin (20108252) Asef Shahriar (22504103) Md Al Amin (20923805) |
| dc.date.none.fl_str_mv | 2025-05-30T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s12065-025-01053-7 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/MCDFN_supply_chain_demand_forecasting_via_an_explainable_multi-channel_data_fusion_network_model/30455849 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Commerce, management, tourism and services Transportation, logistics and supply chains Information and computing sciences Artificial intelligence Data management and data science Supply chain demand forecasting Explainable artificial intelligence Deep learning Convolutional neural networks Long short-term memory Gated recurrent units |
| dc.title.none.fl_str_mv | MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network model |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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 ( <i>U</i><1 <i>U</i> < 1 ) of MCDFN indicates its superiority over the naive forecasting approach, and a 10-fold cross-validated statistical paired t-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><h2>Other Information</h2><p dir="ltr">Published in: Evolutionary Intelligence<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s12065-025-01053-7" target="_blank">https://dx.doi.org/10.1007/s12065-025-01053-7</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_0195e39952b5ac8f0edb9a2933e0be1c |
| identifier_str_mv | 10.1007/s12065-025-01053-7 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30455849 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network modelMd Abrar Jahin (20108252)Asef Shahriar (22504103)Md Al Amin (20923805)Commerce, management, tourism and servicesTransportation, logistics and supply chainsInformation and computing sciencesArtificial intelligenceData management and data scienceSupply chain demand forecastingExplainable artificial intelligenceDeep learningConvolutional neural networksLong short-term memoryGated recurrent units<p dir="ltr">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 ( <i>U</i><1 <i>U</i> < 1 ) of MCDFN indicates its superiority over the naive forecasting approach, and a 10-fold cross-validated statistical paired t-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><h2>Other Information</h2><p dir="ltr">Published in: Evolutionary Intelligence<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s12065-025-01053-7" target="_blank">https://dx.doi.org/10.1007/s12065-025-01053-7</a></p>2025-05-30T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s12065-025-01053-7https://figshare.com/articles/journal_contribution/MCDFN_supply_chain_demand_forecasting_via_an_explainable_multi-channel_data_fusion_network_model/30455849CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/304558492025-05-30T09:00:00Z |
| spellingShingle | MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network model Md Abrar Jahin (20108252) Commerce, management, tourism and services Transportation, logistics and supply chains Information and computing sciences Artificial intelligence Data management and data science Supply chain demand forecasting Explainable artificial intelligence Deep learning Convolutional neural networks Long short-term memory Gated recurrent units |
| status_str | publishedVersion |
| title | MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network model |
| title_full | MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network model |
| title_fullStr | MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network model |
| title_full_unstemmed | MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network model |
| title_short | MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network model |
| title_sort | MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network model |
| topic | Commerce, management, tourism and services Transportation, logistics and supply chains Information and computing sciences Artificial intelligence Data management and data science Supply chain demand forecasting Explainable artificial intelligence Deep learning Convolutional neural networks Long short-term memory Gated recurrent units |