Machine Learning Applications in the Supply Chain, a Literature Review

Rosenberg-Vitorica, Walter; Salais-Fierro, Tomás Eloy; Marmolejo-Saucedo, José Antonio; Rodríguez-Aguilar, Román

doi:10.1007/978-3-031-09753-9_58

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…By restricting the number of features considered at each split, the base estimators are forced to make more independent decisions, which can lead to a more robust The BBC combines the advantages of bagging and sampling techniques to address the issue of imbalanced datasets [20]. Several researchers in recent times and the past have recommended fuzzy logic (e.g., [11], [20], [21], [22], [23], [24], [25]). The objective is to add human-centric design along with advanced machine-learning algorithms.…”

Section: Modeling and Evaluationmentioning

confidence: 99%

Backorder Prediction in Inventory Management: Classification Techniques and Cost Considerations

Maitra,

Kundu

2022

ECTI-CIT Transactions

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This article introduces an advanced analytical approach for predicting backorders in inventory management. Backorder refers to an order that cannot fulfilled immediately due to stock depletion. Multiple classification techniques, including Balanced Bagging classifiers, Fuzzy Logic, Variational Autoencoder (VAE) - Generative Adversarial Networks, and Multilayer Perceptron classifiers, are assessed in this work using performance evaluation metrics such as ROC-AUC and PR-AUC. Moreover, this work incorporates a profit function and misclassification costs, considering the financial implications and costs associated with inventory management and backorder handling. The study suggests a hybrid modelling approach, which includes ensemble techniques and VAE, which effectively addresses imbalanced datasets in inventory management. This approach emphasizes interpretability and reduces false positives and false negatives. This research contributes to the advancement of predictive analytics and offers valuable insights for future investigations in backorder forecasting and inventory control optimization for decision-making.

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Section: Modeling and Evaluationmentioning

confidence: 99%

Backorder Prediction in Inventory Management: Classification Techniques and Cost Considerations

Maitra,

Kundu

2022

ECTI-CIT Transactions

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“…These benefits are resulting in cost savings and increased competitiveness for manufacturing companies. [16][17][18][19][20] In healthcare, machine learning is used to improve patient outcomes and reduce costs. For example, machine learning algorithms can be used to envisage patient outcomes, identify patients at high risk of complications, and optimize treatment plans.…”

Section: Introductionmentioning

confidence: 99%

Potable Water Identification with Machine Learning: An Exploration of Water Quality Parameters

Mohan¹,

Dileep

Bhuria

et al. 2023

IJRITCC

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In this research, we aim to determine the water potability using three machine learning classification algorithms: decision tree, gradient boosting and bagging classifier. These algorithms were trained and tested on a dataset of water quality measurements. The outcomes of the experiment showed that the gradient boosting algorithm achieved the highest F1-score of 0.78 among all the algorithms. This indicates that the gradient boosting algorithm was most effective in correctly identifying both the safe and contaminated water samples. The results of this study demonstrate that gradient boosting is a promising approach for determining water potability and can be used as a reliable method for water quality assessment.

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Backorder Prediction in Inventory Management: Classification Techniques and Cost Considerations

Maitra,

Kundu

2023

2023 27th International Computer Science and Engineering Conference (ICSEC)

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Machine Learning Applications in the Supply Chain, a Literature Review

Cited by 3 publications

References 29 publications

Backorder Prediction in Inventory Management: Classification Techniques and Cost Considerations

Backorder Prediction in Inventory Management: Classification Techniques and Cost Considerations

Potable Water Identification with Machine Learning: An Exploration of Water Quality Parameters

Backorder Prediction in Inventory Management: Classification Techniques and Cost Considerations

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