Deep learning framework for interpretable supply chain forecasting using SOM ANN and SHAP.

Khandakar Rabbi Ahmed, Md Eahia Ansari, Md Naimul Ahsan, Arafat Rohan, Md Borhan Uddin, Mir Araf Hossain Rivin

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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in

2citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

O²RDL-net for joint risk classification and delay forecasting in logistics systems using interaction amplified deep.Scientific reports · 2026
Article
Explainable dual LSTM-autoencoders with exogenous features for anomaly detection and supply chain forecasting.Scientific reports · 2025
Article

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5 · Who and what money

Authors and funding

6 authors.

Khandakar Rabbi AhmedMiyan Research Institute, International University of Business Agriculture and Technology, Dhaka, Bangladesh. khandakarrabbiahmed@gmail.com.

Md Eahia AnsariDepartment of Management, International American University, Los Angeles, USA.

Md Naimul AhsanDepartment of Technology and Engineering, Westcliff University, Irvine, USA.

Arafat RohanDepartment of Management, International American University, Los Angeles, USA.

Md Borhan UddinDepartment of Business Administration and Management, International American University, Los Angeles, USA.

Mir Araf Hossain RivinDepartment of Mathematics, Louisiana Tech University, Ruston, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Contemporary supply chain networks in the context of the era of Industry 4.0 are becoming more erratic and complex, and have an influx of structured and unstructured data. Conventional practices of supply chain management (SCM) cannot overcome real-time uncertainties, and it is time to orient the SCM toward AI-guided predictive modeling. This research contains a suggestion of a deep learning (DL) framework that combines Self-Organizing Maps (SOMs), Principal Component Analysis (PCA), and Artificial Neural Networks (ANNs) to predict more accurately the supply chain shipping timing and delivery risk. Applying the DataCo Smart Supply Chain dataset, the offered SOM+ANN model proved much more accurate than conventional Machine Learning (ML) procedures, e.g., Random Forest (RF), XGBoost, or Decision Tree (DT), to address the tasks of predicting the shipping time and categorizing the risk of late delivery. The R

Identifiers

PMID40685434

DOI10.1038/s41598-025-11510-z

PMCPMC12277392

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.