Lead time prediction using advanced deep learning approaches: a case study in the textile industry

Fri, Mouhsene

doi:10.17270/J.LOG.000979

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Tytuł artykułu

Lead time prediction using advanced deep learning approaches: a case study in the textile industry

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Fri Mouhsene

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DOI

10.17270/J.LOG.000979

Języki publikacji

Abstrakty

Background: In supply chain management, consumers are increasingly sensitive to the delivery time and delivery reliability. Within the textile industry, which is characterized by elongated delivery times, the effective management of delivery timelines is imperative for the timely and trustworthy arrival of products on a global scale. Conventional methods of estimating delivery timelines, which are founded upon historical data, possess certain limitations in their ability to handle the intricacies inherent in contemporary textile manufacturing environments. The paper introduces an innovative deep learning model designed for predicting lead time delivery, with the anticipation that this predictive capability will produce a host of advantages. These benefits encompass heightened operational efficiency and elevated levels of customer satisfaction. Methods: To deal with these difficulties, this paper suggests employing deep learning approaches to predict the lead time in the textile industry. The utilization of historical production data obtained from manufacturing execution systems is leveraged for the purpose of training the models. This paper appraises the most advanced approaches in lead-time prediction, delineates a methodology for anticipating textile manufacturing outcomes, and examines the practicality of these methods through experimentation using real-world data. Results: The conducted research compares three advanced models of deep learning with a classical deep learning model. The three models under consideration are Eagle Strategy Slime Mould Algorithm - General Regression Neural Network (ESSMA-GRNN), Eagle Strategy Slime Mould Algorithm - Convolutional Neural Networks (ESSMA-CNN), and Eagle Strategy Slime Mould Algorithm - Generative Adversarial Networks (ESSMA-GAN), in the specific context of predicting lead-time in textile supply chain. The results demonstrate that the ESSMA-GRNN model exhibits high performance in terms of key evaluation measures, such as Mean Squared Error (MSE), Mean Absolute Error (MAE), R-squared, and Explained Variance, when compared to ESSMA-CNN and ESSMA-GAN. Improving the lead time prediction afforded decision-makers the opportunity to overcome delays and establish effective strategies for minimizing lead time. Conclusions: The study highlights the limitations of conventional lead-time estimation approaches and supports the implementation of sophisticated deep learning methods in the textile supply chain. This paper provides practical perspectives for production planners, underscoring the significance of utilizing advanced techniques to surmount the intricacies of the supply chain. The study concludes by proposing future research directions for lead-time prediction and optimization in the textile sector.

Słowa kluczowe

lead time prediction textile supply chain deep learning model advanced forecasting methods supply chain management

przewidywanie czasu realizacji zamówienia łańcuch dostaw tekstyliów modele głębokiego uczenia zaawansowane metody prognozowania zarządzanie łańcuchem dostaw

Wydawca

Wyższa Szkoła Logistyki

Czasopismo

LogForum

Rocznik

2024

Tom

Vol. 20, no. 2

Strony

149--159

Opis fizyczny

Bibliogr. 23 poz., rys., tab.

Twórcy

autor

Fri Mouhsene

m.fri@ueuromed.org

Euromed University of Fes, UEMF, Morocco

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).

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