Machine learning in supply chain: prediction of real-time e-order arrivals using ANFIS

Hamdan, Ihab K. A.; Wulamu, Aziguli; Zhang, Dezheng; Sumarliah, Eli

doi:10.1007/s13198-022-01851-7

Cited by 6 publications

(4 citation statements)

References 63 publications

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“…Demand forecasting is also identified as a significant field, demonstrating the value of machine learning in anticipating market needs and planning accordingly. Section 4.4 of the article delves into specific methods and their practical applications, such as the hybrid models of learning algorithms adopted by Hamdan, Aziguli, Zhang and Sumarliah (2023) to predict the arrival of electronic orders in real time, highlighting the synergy between ML techniques and fuzzy approaches.…”

Section: Studied Areas Of the Supply Chainmentioning

confidence: 99%

“…Inveritasoft uses logistic regression to predict failures with 90% accuracy, while Revunit uses neural networks to design more efficient routes, reducing transportation costs by 10% (RevUnit, n.d.) These examples reflect the concepts addressed in the selected articles. In the section on 'Demand Forecasting' (Section 4.4), where Hamdan et al (2023) discuss hybrid learning algorithms, the parallels with Amazon's approach to predicting product demand are evident. Similarly, Walmart's route optimizations are reflected in our 'Logistics and Transportation' analysis (Section 4.11), which shows the broader relevance of machine learning models to operational efficiencies.…”

Section: Studied Areas Of the Supply Chainmentioning

confidence: 99%

“…Machine learning has been used to analyze historical data and relevant variables to predict future demand. In studies like the one by Hamdan et al (2023) a hybrid learning algorithm is used that combines the benefits of Adaptive Neuro Fuzzy Inference System (ANFIS) and neural networks to predict real-time electronic order arrivals. This illustrates the complementarity between ML techniques and fuzzy approaches, demonstrating their effectiveness in logistical decision-making.…”

Section: Demand Forecastingmentioning

confidence: 99%

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Integration of machine learning in the supply chain for decision making: A systematic literature review

Polo-Triana,

Gutierrez,

Leon-Becerra

2024

JIEM

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Purpose: This study presents a systematic literature review that provides a broad and holistic view of how machine learning can be used and integrated to enhance decision-making in various areas of the supply chain, highlighting its combination with other techniques and models.Design/methodology/approach: An exhaustive literature review used three sets of keywords in the Scopus and Web of Science (WoS) databases. Through a rigorous filtering process, 70 articles were selected from an initial total of 410, focusing on those that specifically addressed the intersection of machine learning and decision-making in supply chain management.Findings: Machine learning has proven to be an essential tool in the supply chain, with applications in inventory management, logistics, and transportation, among others. Its integration with other techniques has led to significant advances in decision-making, improving efficiency in complex environments. Combining machine learning methods with traditional techniques has been particularly effective, and integration with emerging technologies has opened up new application possibilities.Originality/value: Unlike previous studies that focused on specific areas, this study offers a broad perspective on the application of machine learning in the supply chain. Additionally, combining machine learning techniques with other models is highlighted, representing added value for the scientific community and suggesting new avenues for future research.

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Section: Studied Areas Of the Supply Chainmentioning

confidence: 99%

Section: Studied Areas Of the Supply Chainmentioning

confidence: 99%

Section: Demand Forecastingmentioning

confidence: 99%

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Integration of machine learning in the supply chain for decision making: A systematic literature review

Polo-Triana,

Gutierrez,

Leon-Becerra

2024

JIEM

View full text Add to dashboard Cite

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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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Big Data—Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques

Jahin,

Shovon,

Shin

et al. 2024

Arch Computat Methods Eng

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Machine learning in supply chain: prediction of real-time e-order arrivals using ANFIS

Cited by 6 publications

References 63 publications

Integration of machine learning in the supply chain for decision making: A systematic literature review

Integration of machine learning in the supply chain for decision making: A systematic literature review

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

Big Data—Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques

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