A Grey Approach for the Prediction of Supply Chain Demand

Amanat Ur Rahman^1, and Marzia Tuz Zahura¹

¹Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Bangladesh

Cite this paper:
Amanat Ur Rahman and Marzia Tuz Zahura. A Grey Approach for the Prediction of Supply Chain Demand. American Journal of Industrial Engineering. 2018; 5(1):25-30. doi: 10.12691/ajie-5-1-4

Abstract

With the progress of technology and globalization, competition has risen and so has the need to optimize the Supply Chain. More enterprises are now focusing on supply chain efficiency in order to increase its profit margin and customer satisfaction. A part of the solution for increasing supply chain efficiency lies in the ability to make accurate forecast of demands, since, it interacts with multiple component of the supply chain network. Use of statistical, heuristics and machine learning algorithms is very common for future time series prediction, however, the accuracy of prediction by these models are significantly affected by the uncertainty, imprecision, and the size of source dataset. Grey theory has shown its effectiveness for its quick, brief and accurate prediction for vague, incomplete and imprecise data sets. In this paper, the grey one order one variable model GM (1,1) is applied for demand prediction in a case where the source data is brief and highly uncertain. The effectiveness of GM (1,1) is tested against one of the most commonly used and established forecasting method, exponential smoothing technique, for vague, imprecise and incomplete dataset. Based on the simulated results, the prediction accuracy of the grey prediction model has been observed to be a better fit than that of exponential smoothing technique. The average relative error (ARE) from the grey prediction model satisfies the level 2 of the accuracy scale and also achieving a mean relative simulation accuracy of 95.5%. Hence, based to the observed results, it can be established the GM (1,1) can be effectively used for any future time series prediction in such cases.

Keywords:
grey theory demand forecasting GM (1 1) exponential smoothing supply chain

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	A. Samvedi and V. Jain, “A grey approach for forecasting in a supply chain during intermittentdisruptions,” Eng. Appl. Artif. Intell., vol. 26, no. 3, pp. 1044-1051, 2013.
[2]	H. Balfaqih, Z. M. Nopiah, N. Saibani, and M. T. Al-Nory, “Review of supply chain performance measurement systems: 1998-2015,” Comput. Ind., vol. 82, pp. 135-150, 2016.
[3]	S. C. Graves, D. B. Kletter, and W. B. Hetzel, “A dynamic model for requirements planning with application to supply chain optimization,” Oper. Res., vol. 46, no. 3-supplement-3, pp. S35-S49, 1998.
[4]	M. Xia and W. K. Wong, “A seasonal discrete grey forecasting model for fashion retailing,” Knowledge-Based Syst., vol. 57, pp. 119-126, 2014.
[5]	H.-S. Shih, E. S. Lee, S.-H. Chuang, and C.-C. Chen, “A forecasting decision on the sales volume of printers in Taiwan: An exploitation of the Analytic Network Process,” Comput. Math. with Appl., vol. 64, no. 6, pp. 1545-1556, 2012.
[6]	L.-C. Hsu, “Applying the Grey prediction model to the global integrated circuit industry,” Technol. Forecast. Soc. Change, vol. 70, no. 6, pp. 563-574, Jul. 2003.
[7]	R. Quintana and M. T. Leung, “Adaptive exponential smoothing versus conventional approaches for lumpy demand forecasting: case of production planning for a manufacturing line,” Int. J. Prod. Res., vol. 45, no. 21, pp. 4937-4957, 2007.
[8]	R. Tsaur, “Forecasting by fuzzy double exponential smoothing model,” Int. J. Comput. Math., vol. 80, no. 11, pp. 1351-1361, 2003.
[9]	V. Yorucu, “The analysis of forecasting performance by using time series data for two Mediterranean islands,” Rev. Soc. Econ. Bus. Stud., vol. 2, pp. 175-196, 2003.
[10]	B. Billah, M. L. King, R. D. Snyder, and A. B. Koehler, “Exponential smoothing model selection for forecasting,” Int. J. Forecast., vol. 22, no. 2, pp. 239-247, 2006.
[11]	S. Makridakis et al., “The accuracy of extrapolation (time series) methods: Results of a forecasting competition,” J. Forecast., vol. 1, no. 2, pp. 111-153, 1982.
[12]	S. Makridakis and M. Hibon, “The M3-Competition: results, conclusions and implications,” Int. J. Forecast., vol. 16, no. 4, pp. 451-476, 2000.
[13]	C. Hamzaçebi, “Improving artificial neural networks’ performance in seasonal time series forecasting,” Inf. Sci. (Ny)., vol. 178, no. 23, pp. 4550-4559, 2008.
[14]	D. H. F. Yip, E. L. Hines, and W. W. H. Yu, “Application of artificial neural networks in sales forecasting,” in Neural Networks, 1997., International Conference on, 1997, vol. 4, pp. 2121-2124.
[15]	S.-M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy sets Syst., vol. 81, no. 3, pp. 311-319, 1996.
[16]	N. N. Karnik and J. M. Mendel, “Applications of type-2 fuzzy logic systems to forecasting of time-series,” Inf. Sci. (Ny)., vol. 120, no. 1-4, pp. 89-111, 1999.
[17]	E. Hadavandi, H. Shavandi, and A. Ghanbari, “Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting,” Knowledge-Based Syst., vol. 23, no. 8, pp. 800-808, 2010.
[18]	G. P. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159-175, 2003.
[19]	J.-L. Deng, “Control problems of grey systems.,” Sys. Contr. Lett., vol. 1, no. 5, pp. 288-294, 1982.
[20]	D. Julong, “Introduction to grey system theory,” J. grey Syst., vol. 1, no. 1, pp. 1-24, 1989.
[21]	S. Liu, J. Forrest, and Y. Yang, “A brief introduction to grey systems theory,” Grey Syst. Theory Appl., vol. 2, no. 2, pp. 89-104, 2012.
[22]	S. F. Liu, Y. Yang, and J. Forrest, Grey Data Analysis. Springer, 2017.
[23]	C. C. Hsu and C. Y. Chen, “Applications of improved grey prediction model for power demand forecasting,” Energy Convers. Manag., vol. 44, no. 14, pp. 2241-2249, 2003.
[24]	C.-F. Tsai and S.-L. Lu, “Novel grey models for the trend forecast of Taiwan waste gas apparatus,” Int. J. Environ. Technol. Manag., vol. 18, no. 2, pp. 170-184, 2015.
[25]	T. Xia, X. Jin, L. Xi, Y. Zhang, and J. Ni, “Operating load based real-time rolling grey forecasting for machine health prognosis in dynamic maintenance schedule,” J. Intell. Manuf., vol. 26, no. 2, pp. 269-280, 2015.
[26]	P. Qu, “Mobile communication service income prediction method based on grey buffer operator theory,” Grey Syst. Theory Appl., vol. 4, no. 2, pp. 250-259, 2014.
[27]	R. Rajesh, “Forecasting supply chain resilience performance using grey prediction,” Electron. Commer. Res. Appl., vol. 20, pp. 42-58, 2016.
[28]	N. Xie and S. Liu, “Discrete grey forecasting model and its optimization,” Appl. Math. Model., vol. 33, no. 2, pp. 1173-1186, 2009.
[29]	L.-C. Hsu, “Using improved grey forecasting models to forecast the output of opto-electronics industry,” Expert Syst. Appl., vol. 38, no. 11, pp. 13879-13885, Oct. 2011.
[30]	C. Liu, T. Shu, S. Chen, S. Wang, K. K. Lai, and L. Gan, “An improved grey neural network model for predicting transportation disruptions,” Expert Syst. Appl., vol. 45, pp. 331-340, 2016.