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Dynamic Dual-Phase Forecasting Model for New Product Demand Using Machine Learning and Statistical Control

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  • Chien-Chih Wang

    (Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 24303, Taiwan)

Abstract

Forecasting demand for newly introduced products presents substantial challenges within high-mix, low-volume manufacturing contexts, primarily due to cold-start conditions and unpredictable order behavior. This research proposes the Dynamic Dual-Phase Forecasting Framework (DDPFF) that amalgamates machine learning-based classification, similarity-driven analogous forecasting, ARMA-based residual compensation, and statistical process control for adaptive model refinement. The framework underwent evaluation through five real-world case studies conducted by a Taiwanese semiconductor tray manufacturer, encompassing a variety of scenarios characterized by high volatility, seasonality, and structural drift. The results indicate that DDPFF consistently outperformed conventional ARIMA and analogous forecasting methodologies, yielding an average reduction of 35.7% in mean absolute error and a 41.8% enhancement in residual stability across all examined cases. In one representative instance, the forecast error decreased by 44.9% compared to established benchmarks. These findings underscore the framework’s resilience in cold-start situations and its capacity to adapt to evolving demand patterns, providing a viable solution for data-scarce and dynamic manufacturing environments.

Suggested Citation

  • Chien-Chih Wang, 2025. "Dynamic Dual-Phase Forecasting Model for New Product Demand Using Machine Learning and Statistical Control," Mathematics, MDPI, vol. 13(10), pages 1-23, May.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:10:p:1613-:d:1655713
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    References listed on IDEAS

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    1. Eduardo Luiz Alba & Gilson Adamczuk Oliveira & Matheus Henrique Dal Molin Ribeiro & Érick Oliveira Rodrigues, 2024. "Electricity Consumption Forecasting: An Approach Using Cooperative Ensemble Learning with SHapley Additive exPlanations," Forecasting, MDPI, vol. 6(3), pages 1-25, September.
    2. Spyros Makridakis & Evangelos Spiliotis & Vassilios Assimakopoulos, 2018. "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-26, March.
    3. Rita Gamberini & Francesco Lolli & Bianca Rimini & Fabio Sgarbossa, 2010. "Forecasting of Sporadic Demand Patterns with Seasonality and Trend Components: An Empirical Comparison between Holt-Winters and (S)ARIMA Methods," Mathematical Problems in Engineering, Hindawi, vol. 2010, pages 1-14, July.
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