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A hybrid forecasting model with logistic regression and neural networks for improving key performance indicators in supply chains
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Pietukhov, Rostyslav, Ahtamad, Mujthaba, Faraji Niri, Mona and El-said, Tarek (2023) A hybrid forecasting model with logistic regression and neural networks for improving key performance indicators in supply chains. Supply Chain Analytics, 4 . 100041. doi:10.1016/j.sca.2023.100041
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WRAP-hybrid-forecasting-model-logistic-regression-neural-networks-improving-key-performance-indicators-supply-chains-2023.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (1185Kb) | Preview |
Official URL: https://doi.org/10.1016/j.sca.2023.100041
Abstract
This study investigates the potential of predictive analytics in improving Key Performance Indicators (KPIs) forecasting by leveraging Lean implementation data in supply chain enterprises. A novel methodology is proposed, incorporating two key enhancements: using Lean maturity assessments as a new data source and developing a hybrid forecasting model combining Logistic regression and Neural Network techniques. The proposed methodology is evaluated through a comprehensive empirical study involving 30 teams in a large supply chain company, revealing notable improvements in forecasting accuracy. Compared to a baseline scenario without process improvement data, the new methodology achieves an enhanced accuracy score by 17% and an improved F1 score by 13 %. These findings highlight the benefits of integrating Lean maturity assessments and adopting a hybrid forecasting model, contributing to the advancement of supply chain analytics. By incorporating lean maturity assessments, the forecasting process is enhanced, providing a deeper comprehension of the underlying Lean framework and the impact of its elements on supply chain performance. Additionally, adopting a hybrid model aligns with current best practices in forecasting, allowing for the utilisation of various techniques to optimise KPI prediction accuracy while leveraging their respective strengths.
Item Type: | Journal Article | ||||||||
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Subjects: | H Social Sciences > HD Industries. Land use. Labor H Social Sciences > HF Commerce Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > T Technology (General) T Technology > TS Manufactures |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
Library of Congress Subject Headings (LCSH): | Business logistics , Performance technology, Six sigma (Quality control standard), Neural networks (Computer science) , Logistic regression analysis | ||||||||
Journal or Publication Title: | Supply Chain Analytics | ||||||||
Publisher: | Elsevier | ||||||||
Official Date: | December 2023 | ||||||||
Dates: |
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Volume: | 4 | ||||||||
Article Number: | 100041 | ||||||||
DOI: | 10.1016/j.sca.2023.100041 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 23 November 2023 | ||||||||
Date of first compliant Open Access: | 24 November 2023 | ||||||||
Open Access Version: |
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