Abstract
Product life cycles have become increasingly shorter because of global competition. Under fierce competition, the use of small samples to establish demand forecasting models is crucial for enterprises. However, limited samples typically cannot provide sufficient information; therefore, this presents a major challenge to managers who must determine demand development trends. To overcome this problem, this paper proposes a modified grey forecasting model, called DSI–GM(1,1). Specifically, we developed a data smoothing index to analyze the data behavior and rewrite the calculation equation of the background value in the applied grey modeling, constructing a suitable model for superior forecasting performance according to data characteristics. Employing a test on monthly demand data of thin film transistor liquid crystal display panels and the monthly average price of aluminum for cash buyers, the proposed modeling procedure resulted in high prediction outcomes; therefore, it is an appropriate tool for forecasting short-term demand with small samples.
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Acknowledgments
This study was partially sponsored by the Natural Science Foundation of Zhejiang Province (China) under Grant LY16G010002, K. C. Wong Magna Fund in Ningbo University, and the Ningbo University under Grant XKW15D201.
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Chang, CJ., Lin, JY. & Jin, P. A grey modeling procedure based on the data smoothing index for short-term manufacturing demand forecast. Comput Math Organ Theory 23, 409–422 (2017). https://doi.org/10.1007/s10588-016-9234-0
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DOI: https://doi.org/10.1007/s10588-016-9234-0