A mega-trend-diffusion grey forecasting model for short-term manufacturing demand

Accurate short-term demand forecasting is critical for developing effective production plans; however, a short forecasting period indicates that the product demands are unstable, rendering tracking of product development trends difficult. Determining the actual developing data patterns by using forecasting models generated using historical observations is difficult, and the forecasting performance of such models is unfavourable, whereas using the latest limited data for forecasting can improve management efficiency and maintain the competitive advantages of an enterprise. To solve forecasting problems related to a small data set, this study applied an adaptive grey model for forecasting short-term manufacturing demand. Experiments involving the monthly demand data for thin film transistor liquid crystal display panels and wafer-level chip-scale packaging process data showed that the proposed grey model produced favourable forecasting results, indicating its appropriateness as a short-term forecasting tool for small data sets.

[1]  Fatih Emre Boran,et al.  Forecasting Natural Gas Consumption in Turkey Using Grey Prediction , 2015 .

[2]  Kai Wang,et al.  An optimized Nash nonlinear grey Bernoulli model based on particle swarm optimization and its application in prediction for the incidence of Hepatitis B in Xinjiang, China , 2014, Comput. Biol. Medicine.

[3]  Der-Chiang Li,et al.  The Generalized-Trend-Diffusion modeling algorithm for small data sets in the early stages of manufacturing systems , 2010, Eur. J. Oper. Res..

[4]  Yaser S. Abu-Mostafa,et al.  Learning from hints in neural networks , 1990, J. Complex..

[5]  Sifeng Liu,et al.  Advances in grey systems research , 2010 .

[6]  GuoDong Li,et al.  A grey-based rough approximation model for interval data processing , 2007, Inf. Sci..

[7]  Qing-mei Tan,et al.  The forecast of cultivate land quantity based on Grey-Markov model: A case study of Jiangsu province , 2011, Proceedings of 2011 IEEE International Conference on Grey Systems and Intelligent Services.

[8]  Claudio Moraga,et al.  A diffusion-neural-network for learning from small samples , 2004, Int. J. Approx. Reason..

[9]  Naiming Xie,et al.  Forecasting energy consumption in China following instigation of an energy-saving policy , 2014, Natural Hazards.

[10]  S. Delurgio.  Forecasting Principles and Applications , 1998 .

[11]  Tung-I Tsai,et al.  Using virtual data effects to stabilize pilot run neural network modeling , 2013, Proceedings of 2013 IEEE International Conference on Grey systems and Intelligent Services (GSIS).

[12]  Der-Chiang Li,et al.  Utilizing an adaptive grey model for short-term time series forecasting: A case study of wafer-level packaging , 2013 .

[13]  Czesław Cempel,et al.  Using a set of GM(1,1) models to predict values of diagnostic symptoms , 2015 .

[14]  J. Scott Armstrong,et al.  Beyond Accuracy: Comparison of Criteria Used to Select Forecasting Methods , 1995 .

[15]  I-Hsiang Wen,et al.  Practical information diffusion techniques to accelerate new product pilot runs , 2015 .

[16]  Sifeng Liu,et al.  Discrete grey forecasting model and its optimization , 2009 .

[17]  Sifeng Liu,et al.  Grey system model with the fractional order accumulation , 2013, Commun. Nonlinear Sci. Numer. Simul..

[18]  Li-Chang Hsu,et al.  A genetic algorithm based nonlinear grey Bernoulli model for output forecasting in integrated circuit industry , 2010, Expert Syst. Appl..

[19]  Long-Sheng Chen,et al.  Using Functional Virtual Population as assistance to learn scheduling knowledge in dynamic manufacturing environments , 2003 .

[20]  Yi Lin,et al.  Grey Systems: Theory and Applications , 2010 .

[21]  Chaoqing Yuan,et al.  Forecasting China’s energy demand and self-sufficiency rate by grey forecasting model and Markov model , 2015 .

[22]  Der-Chiang Li,et al.  A latent information function to extend domain attributes to improve the accuracy of small-data-set forecasting , 2014, Neurocomputing.

[23]  Chun-I Chen,et al.  Forecasting Taiwan's major stock indices by the Nash nonlinear grey Bernoulli model , 2010, Expert Syst. Appl..

[24]  Manoj K. Malhotra,et al.  Operations Management: Processes and Supply Chains , 2012 .

[25]  Wei Cui,et al.  Non-homogenous discrete grey model with fractional-order accumulation , 2014, Neural Computing and Applications.

[26]  Der-Chiang Li,et al.  A forecasting model for small non-equigap data sets considering data weights and occurrence possibilities , 2014, Comput. Ind. Eng..

[27]  Chien-Chih Chen,et al.  A novel procedure for multimodel development using the grey silhouette coefficient for small-data-set forecasting , 2015, J. Oper. Res. Soc..

[28]  Der-Chiang Li,et al.  Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge , 2007, Comput. Oper. Res..

[29]  Sungzoon Cho,et al.  Virtual sample generation using a population of networks , 2004, Neural Processing Letters.

[30]  Zheng-Xin Wang,et al.  An optimized Nash nonlinear grey Bernoulli model for forecasting the main economic indices of high technology enterprises in China , 2013, Comput. Ind. Eng..