ENHANCING SUPPLY CHAIN RESILIENCE : RIME-CLUSTERING AND ENSEMBLE DEEP LEARNING STRATEGIES FOR LATE DELIVERY RISK PREDICTION
(2024) In Logforum 20(1). p.55-70- Abstract
Background: Global supply chains are confronted with the challenge of ensuring on-time deliveries while simultaneously enhancing supply chain resilience. Conventional methods aim to address the complexities of modern supply chains, promoting the transition to intelligent and data-driven strategies. Methods: This research represents an innovative methodology for predicting the risk of late deliveries in supply chains. The presented framework combines clustering and multiclassification techniques, where the clustering phase is executed through hyperparameter optimization and a novel metaheuristic called RIME. In the multiclassification phase, five distinct deep learning models are employed, namely, Generative Adversarial Network (GAN),... (More)
Background: Global supply chains are confronted with the challenge of ensuring on-time deliveries while simultaneously enhancing supply chain resilience. Conventional methods aim to address the complexities of modern supply chains, promoting the transition to intelligent and data-driven strategies. Methods: This research represents an innovative methodology for predicting the risk of late deliveries in supply chains. The presented framework combines clustering and multiclassification techniques, where the clustering phase is executed through hyperparameter optimization and a novel metaheuristic called RIME. In the multiclassification phase, five distinct deep learning models are employed, namely, Generative Adversarial Network (GAN), Convolutional Neural Network Long Short-Term Memory (CNN-LSTM), within Ensemble learning via bagging, Ensemble learning stacking, and Ensemble learning within boosting. The three ensemble learning models are based in GAN and CNN-LSTM. Result: This paper presents a systematic evaluation of diverse models in a risk of late delivery prediction framework. This evaluation demonstrates that Ensemble learning stacking provides the higher accuracy by 0.926, showcasing its prowess in precise predictions. Notably, Ensemble learning bagging and Ensemble learning boosting exhibit strong precision. Regression metrics reveal Ensemble learning stacking and Ensemble learning bagging's superior error minimization (MSE 0.11, MAE 0.09). This metric demonstrates that the proposed model can predict the risk level of late delivery in a supply chain with high precision. Conclusion: This paper introduces an innovative clustering and multiclassification-based framework for predicting the risk of late deliveries. The ability of prediction late deliveries risk helps organizations to enhance supply chain resilience by adopting a proactive management risks strategy, optimizing operational processes, and elevating customer satisfaction.
(Less)
- author
- Douaioui, Kaoutar ; Oucheikh, Rachid LU and Mabrouki, Charif
- organization
- publishing date
- 2024-01-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Artificial Neural Networks (ANNs), Clustering, Deep Learning Models, Late Delivery Risk, Multiclassification, RIME Optimization Algorithm, Supply Chain Management
- in
- Logforum
- volume
- 20
- issue
- 1
- pages
- 16 pages
- publisher
- Poznan School of Logistics
- external identifiers
-
- scopus:85197684051
- ISSN
- 1895-2038
- DOI
- 10.17270/J.LOG.001007
- language
- English
- LU publication?
- yes
- id
- fa14a5c3-58c3-49ed-a077-682f5074f3ea
- date added to LUP
- 2024-12-03 11:21:16
- date last changed
- 2025-04-04 14:16:18
@article{fa14a5c3-58c3-49ed-a077-682f5074f3ea,
abstract = {{<p>Background: Global supply chains are confronted with the challenge of ensuring on-time deliveries while simultaneously enhancing supply chain resilience. Conventional methods aim to address the complexities of modern supply chains, promoting the transition to intelligent and data-driven strategies. Methods: This research represents an innovative methodology for predicting the risk of late deliveries in supply chains. The presented framework combines clustering and multiclassification techniques, where the clustering phase is executed through hyperparameter optimization and a novel metaheuristic called RIME. In the multiclassification phase, five distinct deep learning models are employed, namely, Generative Adversarial Network (GAN), Convolutional Neural Network Long Short-Term Memory (CNN-LSTM), within Ensemble learning via bagging, Ensemble learning stacking, and Ensemble learning within boosting. The three ensemble learning models are based in GAN and CNN-LSTM. Result: This paper presents a systematic evaluation of diverse models in a risk of late delivery prediction framework. This evaluation demonstrates that Ensemble learning stacking provides the higher accuracy by 0.926, showcasing its prowess in precise predictions. Notably, Ensemble learning bagging and Ensemble learning boosting exhibit strong precision. Regression metrics reveal Ensemble learning stacking and Ensemble learning bagging's superior error minimization (MSE 0.11, MAE 0.09). This metric demonstrates that the proposed model can predict the risk level of late delivery in a supply chain with high precision. Conclusion: This paper introduces an innovative clustering and multiclassification-based framework for predicting the risk of late deliveries. The ability of prediction late deliveries risk helps organizations to enhance supply chain resilience by adopting a proactive management risks strategy, optimizing operational processes, and elevating customer satisfaction.</p>}},
author = {{Douaioui, Kaoutar and Oucheikh, Rachid and Mabrouki, Charif}},
issn = {{1895-2038}},
keywords = {{Artificial Neural Networks (ANNs); Clustering; Deep Learning Models; Late Delivery Risk; Multiclassification; RIME Optimization Algorithm; Supply Chain Management}},
language = {{eng}},
month = {{01}},
number = {{1}},
pages = {{55--70}},
publisher = {{Poznan School of Logistics}},
series = {{Logforum}},
title = {{ENHANCING SUPPLY CHAIN RESILIENCE : RIME-CLUSTERING AND ENSEMBLE DEEP LEARNING STRATEGIES FOR LATE DELIVERY RISK PREDICTION}},
url = {{http://dx.doi.org/10.17270/J.LOG.001007}},
doi = {{10.17270/J.LOG.001007}},
volume = {{20}},
year = {{2024}},
}