TY - JOUR
T1 - New Product Short-Term Demands Forecasting with Boxplot-Based Fractional Grey Prediction Model
AU - Li, Der Chiang
AU - Huang, Wen Kuei
AU - Lin, Yao San
N1 - Funding Information:
Funding: This research was funded by the Ministry of Science and Technology, Taiwan, grant number MOST-110-2221-E-006-194.
Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/5/1
Y1 - 2022/5/1
N2 - The cost of investing in new product development (NPD) is high, and it is a feasible way to use demand forecasts for customer or end-users as a decisive reference. However, this short-term time-series data has difficulties in learning because there is no past performance on which to base the estimates. In the past, it has been proven that the cumulative method of the fractional grey prediction model (FGM) is better than the traditional integer cumulative method of the grey model (GM) model. There are many studies using different optimal algorithms to determine the moderate score order. How to set the coefficient of α in FGM is also worth exploring. Therefore, this research reveals a new fractional grey prediction model which uses box-and-whisker plots to estimate the trends of data, known as the boxplot-based fractional scale prediction model (boxplot-based FGM, BP-FGM) to improve the accuracy of predictors by setting the coefficient sets of α. In the experiment, the examined dataset was collected from a well-known equipment manufacturer as the research object. For modeling, the mean absolute percentage error (MAPE) was established as the objective function of the optimization model, the results from three datasets verified the effect through the commodity attributes and public test data of its production, and the experimental results show that BP-FGM has better prediction results than FGM.
AB - The cost of investing in new product development (NPD) is high, and it is a feasible way to use demand forecasts for customer or end-users as a decisive reference. However, this short-term time-series data has difficulties in learning because there is no past performance on which to base the estimates. In the past, it has been proven that the cumulative method of the fractional grey prediction model (FGM) is better than the traditional integer cumulative method of the grey model (GM) model. There are many studies using different optimal algorithms to determine the moderate score order. How to set the coefficient of α in FGM is also worth exploring. Therefore, this research reveals a new fractional grey prediction model which uses box-and-whisker plots to estimate the trends of data, known as the boxplot-based fractional scale prediction model (boxplot-based FGM, BP-FGM) to improve the accuracy of predictors by setting the coefficient sets of α. In the experiment, the examined dataset was collected from a well-known equipment manufacturer as the research object. For modeling, the mean absolute percentage error (MAPE) was established as the objective function of the optimization model, the results from three datasets verified the effect through the commodity attributes and public test data of its production, and the experimental results show that BP-FGM has better prediction results than FGM.
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U2 - 10.3390/app12105131
DO - 10.3390/app12105131
M3 - Article
AN - SCOPUS:85130939458
SN - 2076-3417
VL - 12
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
IS - 10
M1 - 5131
ER -