Anomaly Detection with Machine Learning Technique to Support Smart Logistics

Kerdprasop, Nittaya; Chansilp, Kacha; Kerdprasop, Kittisak

doi:10.1007/978-3-030-24289-3_34

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…In this case, ML using recurrent neural networks (RNN) can be applied for stroke diagnosis and tracking, leveraging their ability to recognize patterns and sequences to predict likely scenarios [46]. In logistics, machine learning is similarly providing relevant contributions by testing for anomaly detection, successfully producing accurate discriminative models integrated into smart logistics management systems with high accuracy levels [47]. For instance, predictive maintenance software utilizing machine learning and deep learning algorithms, such as Presenso, are significantly benefiting logistics companies by optimizing maintenance processes and operational efficiencies [7].…”

Section: Machine Learningmentioning

confidence: 99%

A Systematic Literature Review on the Application of Automation in Logistics

Ferreira,

Reis

2023

Logistics

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Background: in recent years, automation has emerged as a hot topic, showcasing its capacity to perform tasks independently, without constant supervision. While automation has witnessed substantial growth in various sectors like engineering and medicine, the logistics industry has yet to witness an equivalent surge in research and implementation. Therefore, it becomes imperative to explore the application of automation in logistics. Methods: this article aims to provide a systematic analysis of the scientific literature concerning artificial intelligence (AI) and automation in logistics, laying the groundwork for robust and relevant advancements in the field. Results: the foundation of automation lies in cutting-edge technologies such as AI, machine learning, and deep learning, enabling self-problem resolution and autonomous task execution, reducing the reliance on human labor. Consequently, the implementation of smart logistics through automation has the potential to enhance competitiveness and minimize the margin of error. The impact of AI and robot-driven logistics on automation in logistics is profound. Through collaborative efforts in human–robot integration (HRI), there emerges an opportunity to develop social service robots that coexist harmoniously with humans. This integration can lead to a revolutionary transformation in logistics operations. By exploring the scientific literature on AI and automation in logistics, this article seeks to unravel critical insights into the practical application of automation, thus bridging the existing research gap in the logistics industry. Conclusions: the findings underscore the impact of artificial intelligence and robot-driven logistics on improving operational efficiency, reducing errors, and enhancing competitiveness. The research also provided valuable insights into the applications of various automation techniques, including machine learning and deep learning, in the logistics domain. Hence, the study’s insights can guide practitioners and decision makers in implementing effective automation strategies, thereby improving overall performance and adaptability in the dynamic logistics landscape. Understanding these foundations can pave the way for a future where automation and human expertise work hand in hand to drive logistics toward unparalleled efficiency and success.

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Section: Machine Learningmentioning

confidence: 99%

A Systematic Literature Review on the Application of Automation in Logistics

Ferreira,

Reis

2023

Logistics

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“…Forecasting and anomaly detection have separately been widely discussed in the literature and in particular through the deployment of machine learning techniques (Nam et al, 2020;Loureiro et al, 2018;Fildes et al, 2022;Kerdprasop et al, 2019;Tran et al, 2019) Anomaly detection is a subject that has often been dealt with using machine learning given its importance and Amellal, et al: Improving Lead Time Forecasting and Anomaly Detection for Automative Spare Parts with A Combined CNN-LSTM Operations and Supply Chain Management 16(2) pp. 265 -278 © 2023 267 cross-functionality for different fields involving data analysis.…”

Section: Related Workmentioning

confidence: 99%

Improving Lead Time Forecasting and Anomaly Detection for Automotive Spare Parts with A Combined CNN-LSTM Approach

Amellal,

Seghiouer

et al. 2023

OSCM: An Int. Journal

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This paper presents a solution to a challenge faced in the supply chain management of a spare parts distributor with a dispersed global supply network and local distribution network in Morocco. The problem is a lack of accurate lead time information, leading to difficulties in meeting customer demand. The proposed solution is a framework using an LSTM (Long Short Term Memory) model for lead time forecasting and anomaly detection. The framework combines CNN (Convolution neural network) -Bidirectional LSTM model for forecasting and an LSTM autoencoder with One-Class Support Vector Machine for anomaly detection. The data was obtained from a legal ERP system of a major automotive distributor in Morocco. The results show that the framework is effective in overcoming the lead time information issue, and the relevance of the methods used has been verified via precise performance indicators, such as the RMSE (Root of the mean of the square of the errors) testifying to the accuracy of the results and also through comparison with other models.

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Forecasting of Daily Demand’s Order Using Gradient Boosting Regressor

Anzar

2021

Advances in Intelligent Systems and Computing

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Anomaly Detection with Machine Learning Technique to Support Smart Logistics

Cited by 5 publications

References 28 publications

A Systematic Literature Review on the Application of Automation in Logistics

A Systematic Literature Review on the Application of Automation in Logistics

Improving Lead Time Forecasting and Anomaly Detection for Automotive Spare Parts with A Combined CNN-LSTM Approach

Forecasting of Daily Demand’s Order Using Gradient Boosting Regressor

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