Abstract
In manufacturing, many important decision-makings are based on demand forecasting of products. However, so many uncertainties lead to demand forecasting error. Especially, semiconductor manufacturing is characterized by its short product lifecycle, which means only limited historical data of a single product can be used to support error evaluation and correction of demand forecasting model, while traditional forecasting error evaluation and correction methods need large sample size to ensure the quality of results. To solve this problem, a new method titled “Nonparametric Inter-Quartile Range” (NIQR), which combines nonparametric density kernel estimation with cumulative probability distribution function, is proposed for error evaluation and model selection, and second quartile is used to correct model’s forecasting error. Numerical experiments in semiconductor manufacturing are used to show the feasibility and effectiveness of the proposed NIQR method for small sample size from short lifecycle product.
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Li, WR., Li, B. (2009). Nonparametric Inter-Quartile Range for Error Evaluation and Correction of Demand Forecasting Model under Short Product Lifecycle. In: Wang, H., Shen, Y., Huang, T., Zeng, Z. (eds) The Sixth International Symposium on Neural Networks (ISNN 2009). Advances in Intelligent and Soft Computing, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01216-7_51
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DOI: https://doi.org/10.1007/978-3-642-01216-7_51
Publisher Name: Springer, Berlin, Heidelberg
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