Demand Forecasting in Supply Chain: Comparing Multiple Linear Regression and Artificial Neural Networks Approaches


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Abstract


Forecasting plays an important role, in supply chain management. It allows anticipating and meeting future demands and expectations of customers. This article presents a contribution to improve the quality of forecasts by application of artificial neuron networks (ANNs). The neural network model is compared with the multiple linear regression (MLR). The results show that ANNs are more efficient and are very promising for solving forecast problems.
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Keywords


Demand Forecasting; Supply Chain; Time Series; Causal Method; Multiple Regression; Artificial Neural Networks

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References


Benkachcha. S, Benhra. J, El Hassani. H, Causal Method and Time Series Forecasting model based on Artificial Neural Network, International Journal of Computer Applications (0975 – 8887). Volume 75– No.7, 2013.

J. S. Armstrong, & K. C. Green, Demand forecasting: Evidence-based methods, Monash University Department of Econometrics and Business Statistics, Working Paper 24-05, 2005.

D. D. Illeperuma, T. Rupasinghe, Applicability of Forecasting Models and Techniques for Stationery Business:A Case Study from Sri Lanka, International Journal of Engineering Research. Volume No.2, Issue No. 7, pp : 449-451, 2013.

V.Gosasang , W.Chan., and S.KIATTISIN, A Comparison of Traditional and Neural Networks Forecasting Techniques for Container Throughput at Bangkok Port, The Asian Journal of Shipping and Logistics, Vol. 27, N° 3, pp. 463-482, 2011.

J. S. Armstrong, Illusions in Regression Analysis, International Journal of Forecasting, Vol.28, p 689 – 694, 2012.

Charles W. Chase Jr., Integrating Market Response Models in Sales Forecasting, The Journal of Business Forecasting. Spring: 2, 27, 1997.

K.Y. Chen, Combining linear and nonlinear model in forecasting tourism demand, Expert Systems with Applications, Vol.38, p 10368–10376, 2011.

C. A. Mitrea, , C. K. M. Lee, and Z. Wu, A Comparison between Neural Networks and Traditional Forecasting Methods: A Case Study, International Journal of Engineering Business Management, Vol. 1, No. 2, p 19-24, 2009.

Daniel Ortiz-Arroyo, Morten K. Skov and Quang Huynh, Accurate Electricity Load Forecasting With Artificial Neural Networks , Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCAIAWTIC’05) , 2005.

Daryl S. Paulson, Handbook of Regression and Modeling Applications for the Clinical and Pharmaceutical Industries (Publisher: Boca Raton : Taylor & Francis Group, LLC, 2007).

John O. Rawlings, Sastry G. Pantula, David A. Dickey, Applied Regression Analysis: A Research Tool (Springer texts in statistics, 2nd ed. p. cm. 1998).

S.Weisberg, Applied Linear Regression (Published by John Wiley & Sons, Inc., Hoboken, New Jersey, 3rd ed. 2005).

S.Makridakis, Accuracy measures: theoretical and practical concerns, International Journal of Forecasting 9, 527-52, 1993.

S. Makridakis, & M. Hibon, Evaluating Accuracy (or Error) Measures, Working Paper, INSEAD, Fontainebleau, France, 1995.

B. M.Wilamowski, Neural Network Architectures. Industrial Electronics Handbook (vol. 5 – Intelligent Systems, 2nd Edition, chapter 6, pp. 6-1 to 6-17, CRC Press, 2011).

G. Zhang, B. E. Patuwo, and M.Y. Hu, Forecasting with artificial neural networks : The state of the art, International Journal of Forecasting.Vol.14, , p 35–62, 1998.

B. M.Wilamowski, H.Yu, Improved Computation for Levenberg Marquardt Training, IEEE Trans. on Neural Networks, vol. 21, no. 6, pp. 930-937, 2010.

Ashour, Z.H., Farahat, M.A., A new artificial neural network approach with selected inputs for short term electric load forecasting, (2008) International Review of Electrical Engineering (IREE), 3 (1), pp. 32-36.

Farahat, M.A., Talaat, M., The using of curve fitting prediction optimized by genetic algorithms for short-term load forecasting, (2012) International Review of Electrical Engineering (IREE), 7 (6), pp. 6209-6215.

Porkar, S., Poure, P., Saadate, S., Distributed generation electricity price forecasting in a deregulated electricity market, (2012) International Review of Electrical Engineering (IREE), 7 (5), pp. 5829-5839.


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