The downstream manufactures will order their materials from different upstream suppliers to maintain a certain level of the demand. This paper proposes a bivariate model to portray this phenomenon of material demand. We use empirical data to estimate the parameters of model and evaluate the RMSD of model calibration. The results show that the model has better fitness.<\/p>\r\n","references":"[1]\tJ. L. Zeballosa and C. A. M\u00e9ndeza, A. P. Barbosa-Povoab, A. Q. Novais, \u2018Multi-period design and planning of closed-loop supply chains with uncertain supply and demand\u201d, Computers & Chemical Engineering, vol 66, no.4, pp. 151\u2013164, 2014.\r\n[2]\tM. Zorgdrager and R. Curran, W. J. C. Verhagen, B. H. L. Boesten, and C. N. Water, \u201cA predictive method for the estimation of material demand for aircraft non-routine maintenance\u201d, 20th ISPE International Conference on Concurrent Engineering: Proceedings, pp. 509-515, 2013.\r\n[3]\t H. Stadtler,\u201d Supply chain management and advanced planning-basics, overview and challenges\u201d, European Journal of Operational Research, vol. 163, no.3, pp. 575-588, 2005.\r\n[4]\tH. Lu, H. Wang, Y. Xie and H, Li, \u201d Construction material safety-stock determination under nonstationary stochastic demand and random supply yield\u201d, IEEE Transactions on Engineering Management, vol. 63 , no.2, pp.201-212,\r\n[5]\tA. Gupta, C. D. Maranas and C. M. McDonald, \u201cMid-term supply chain planning under demand uncertainty: customer demand satisfaction and inventory management\u201d, Computers & Chemical Engineering vol. 24, no.12, pp. 2613-2621, 2000.\r\n[6]\tT. C. Poona, K. L. Choya, F. T. S. Chana and H.C.W., Lau, \u201cA real-time production operations decision support system for solving stochastic production material demand problems\u201d, Expert Systems with Applications, vol. 38, no.5, pp. 4829-4838, 2011.\r\n[7]\tE. A. Mart\u00ednez Cese\u00f1a and P. Mancarella, \u201cPractical recursive algorithms and flexible open-source applications for planning of smart distribution networks with demand response\u201d, Sustainable Energy, Grids and Networks, In Press, 2016.\r\n[8]\tS. R. Cardoso, A. Paula, F.D. Barbosa-P\u00f3voa and S. Relvas, \u201cDesign and planning of supply chains with integration of reverse logistics activities under demand uncertainty\u201d, European Journal of Operational Research, vol. 226, no.3, pp. 436-451, 2013.\r\n[9]\t\tH. H. Huang, \u201cA materials demand model with ordering quantity of past and recency of ordering time\u201d, Key Engineering Materials, In Press, 2016.\r\n[10]\tH. H. Huang, \u201cA detection model of customer alive in information management application\u201d, Advanced Materials Research, vol.684, pp.505-508, 2013.\r\n[11]\tH. H. Huang, \u201cData mining application of marketing: a bivariate model of customer purchase monetary and interpurchase time\u201d, Information and Knowledge Management, vol.45, pp.154-157, 2012.\r\n[12]\tH. H. Huang, \u201cCombining Recency of Ordering Time and Different Sources from Upstream to Predict the Materials Demand of Downstream Manufactures\u201d, Advances in Engineering Research, In Press, 2016.\r\n[13]\t N. L., Johnson and Kotz, S., \u201con some generalized Farlie-Gumbel-Morgenster distributions\u201d, Communications in Statistics, vol.4, no.4, pp.415-27, 1975. \r\n[14]\t N. L.,\tJohnson and Kotz, S., \u201con some generalized Farlie-Gumbel-Morgenster Distributions-II: Regression, correlation and further generalizations\u201d, Communications in Statistics, vol.6, vol.6, pp. 485-96, 1977.\r\n[15]\tD. J. G., Farlie, \u201cThe Performance of Some Correlation Coefficients for a General Bivariate Distribution\u201d, Biometrika, vol.47, pp.307-23, 1960.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 124, 2017"}