Articlehybrid2024

Deep learning approaches to identify order status in a complex supply chain

Mahmoud M. Bassiouni · Ripon Kumar Chakrabortty · Karam M. Sallam · Omar Khadeer Hussain

47 citations0/0 files storedExpert Systems with Applications

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Impact of AI and Big Data on Business and Society Forecasting Techniques and Applications Supply Chain Resilience and Risk Management

Abstract

The emergence of artificial intelligence (AI) and its related capabilities has led industries to rethink the existing practices of conventional supply chain management and data analysis. Machine learning (ML), Deep Learning (DL) and their unique ability to predict future data and classify data have led to important research in the supply chain (SC) domain, particularly in identifying and prioritising supply chain risks. This paper proposes several DL methodologies to exploit the benefit of DL, particularly to identify whether any product will be delivered late due to any unforeseen reason in a complex SC system. Four different DL architectures (Simple-LSTM, Deep-LSTM, 1D-CNN, and TCN-1DSPCNN models) are proposed to extract features, while six variant classifiers: Softmax, random trees (RT), random forest (RF), K-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM), were used to classify delay or non-delay information. By seamlessly capturing intricate temporal dependencies, these DL models enhance accuracy in robustly identifying supply chain late orders. Leveraging their hierarchical feature learning, these proposed DL models excel in recognizing subtle patterns and correlations, making them ideal for classifying late orders within the supply chain. Their parallel processing prowess facilitates real-time decision support, allowing organizations to address potential delays and allocate resources effectively and proactively. Five-fold cross-validation is presented to avoid over-fitting and to prove the efficiency of the proposed DL models. The total accuracies of the six ML classifiers are 74.03, 75.81, 93.35, 87.72, 93.59, and 95.10, respectively, while the maximum accuracies obtained from four proposed DL methodologies obtained an accuracy of 97.6, 98.63, 100, 100% respectively using the SVM classifier for predicting late orders based on five-fold cross-validation.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 47 citations exist upstream.

References

2018Supply chain risk management and artificial intelligence: state of the art and future research directions 2015OR/MS models for supply chain disruptions: a review 2017Agribusiness supply chain risk management: A review of quantitative decision models 2019Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach 2019Predicting supply chain risks using machine learning: The trade-off between performance and interpretability 2014An analytical framework for supply network risk propagation: A Bayesian network approach 2020An Integrative approach to supply chain disruption risk and resilience management: a literature review 2014An accelerated Benders decomposition algorithm for sustainable supply chain network design under uncertainty: A case study of medical needle and syringe supply chain

Cited by

2024Forecasting supply chain disruptions in the textile industry using machine learning: A case study 2025Causal machine learning for supply chain risk prediction and intervention planning 2025Deep learning framework for interpretable supply chain forecasting using SOM ANN and SHAP 2025Beyond human-in-the-loop: Sensemaking between artificial intelligence and human intelligence collaboration

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.