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• W4387373721 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." @default.
• W4387373721 created "2023-10-06" @default.
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• W4387373721 date "2023-12-01" @default.
• W4387373721 modified "2023-10-12" @default.
• W4387373721 title "A Hybrid Forecasting Model with Logistic Regression and Neural Networks for Improving Key Performance Indicators in Supply Chains" @default.
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• W4387373721 doi "https://doi.org/10.1016/j.sca.2023.100041" @default.
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