A hybrid forecasting model with logistic regression and neural networks for improving key performance indicators in supply chains

• Published in: Supply Chain Analytics 2023-12, Vol.4, p.100041, Article 100041
• Main Authors: Pietukhov, Rostyslav, Ahtamad, Mujthaba, Faraji-Niri, Mona, El-Said, Tarek
• Format: Article
• Language: English
• Subjects: Key performance indicators; Lean six sigma; Logistic regression; Neural networks; Process improvement; Supply chain analytics
• ISSN: 2949-8635; 2949-8635
• DOI: 10.1016/j.sca.2023.100041

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.