Forecasting Materials Demand from Multi-Source Ordering

Authors: Hui Hsin Huang

Abstract:

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.

Keywords: Farlie-Gumbel-Morgenstern family of bivariate distributions, multi-source ordering, materials demand quantity, recency, ordering time.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1340352

References:

[1] J. L. Zeballosa and C. A. Méndeza, A. P. Barbosa-Povoab, A. Q. Novais, ‘Multi-period design and planning of closed-loop supply chains with uncertain supply and demand”, Computers & Chemical Engineering, vol 66, no.4, pp. 151–164, 2014.
[2] M. Zorgdrager and R. Curran, W. J. C. Verhagen, B. H. L. Boesten, and C. N. Water, “A predictive method for the estimation of material demand for aircraft non-routine maintenance”, 20th ISPE International Conference on Concurrent Engineering: Proceedings, pp. 509-515, 2013.
[3] H. Stadtler,” Supply chain management and advanced planning-basics, overview and challenges”, European Journal of Operational Research, vol. 163, no.3, pp. 575-588, 2005.
[4] H. Lu, H. Wang, Y. Xie and H, Li, ” Construction material safety-stock determination under nonstationary stochastic demand and random supply yield”, IEEE Transactions on Engineering Management, vol. 63 , no.2, pp.201-212,
[5] A. Gupta, C. D. Maranas and C. M. McDonald, “Mid-term supply chain planning under demand uncertainty: customer demand satisfaction and inventory management”, Computers & Chemical Engineering vol. 24, no.12, pp. 2613-2621, 2000.
[6] T. C. Poona, K. L. Choya, F. T. S. Chana and H.C.W., Lau, “A real-time production operations decision support system for solving stochastic production material demand problems”, Expert Systems with Applications, vol. 38, no.5, pp. 4829-4838, 2011.
[7] E. A. Martínez Ceseña and P. Mancarella, “Practical recursive algorithms and flexible open-source applications for planning of smart distribution networks with demand response”, Sustainable Energy, Grids and Networks, In Press, 2016.
[8] S. R. Cardoso, A. Paula, F.D. Barbosa-Póvoa and S. Relvas, “Design and planning of supply chains with integration of reverse logistics activities under demand uncertainty”, European Journal of Operational Research, vol. 226, no.3, pp. 436-451, 2013.
[9] H. H. Huang, “A materials demand model with ordering quantity of past and recency of ordering time”, Key Engineering Materials, In Press, 2016.
[10] H. H. Huang, “A detection model of customer alive in information management application”, Advanced Materials Research, vol.684, pp.505-508, 2013.
[11] H. H. Huang, “Data mining application of marketing: a bivariate model of customer purchase monetary and interpurchase time”, Information and Knowledge Management, vol.45, pp.154-157, 2012.
[12] H. H. Huang, “Combining Recency of Ordering Time and Different Sources from Upstream to Predict the Materials Demand of Downstream Manufactures”, Advances in Engineering Research, In Press, 2016.
[13] N. L., Johnson and Kotz, S., “on some generalized Farlie-Gumbel-Morgenster distributions”, Communications in Statistics, vol.4, no.4, pp.415-27, 1975.
[14] N. L., Johnson and Kotz, S., “on some generalized Farlie-Gumbel-Morgenster Distributions-II: Regression, correlation and further generalizations”, Communications in Statistics, vol.6, vol.6, pp. 485-96, 1977.
[15] D. J. G., Farlie, “The Performance of Some Correlation Coefficients for a General Bivariate Distribution”, Biometrika, vol.47, pp.307-23, 1960.