Abstract
Artificial neural networks (ANNs) provide a way to make intelligent decisions while leveraging on today’s processing power. In this paper, an attempt has been made to use ANN in demand forecasting by modeling it mathematically. MATLAB and R software are used to create the neural networks. Data has been organized and results are compared using Python. The complete analysis has been done using demand forecasting of American multinational retail corporation, Walmart. What we have managed to achieve in the end is almost perfect accuracy in forecasting demand of Walmart by ensuring that the set of inputs are complete enough to provide an output and then further ensuring that we do obtain an output. In compliance with the same, average sales of each Walmart store in question was calculated from training data and normalized. A correction factor was used to compensate for the effect of seasonality which is an external factor. By doing this, the model is saved from the trouble of having to map an extra factor which can otherwise be easily compensated for. The method used is a multi-layered perceptron in all cases. Iterations were done to find the best parameters to build the model.
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References
Chen, C.H.: Neural networks for financial market prediction. In: IEEE World Congress Computational Intelligence (1994). Chopra, S., Meindl, P.: Supply Chain Management: Strategy, Planning and Operation. Prentice Hall, NJ, (2001)
Stalidis, G., Karapistolis, D., Vafeiadis, A.: Marketing decision support using artificial intelligence and knowledge modeling: Application to tourist destination and management. Proc. Soc. Behav. Sci. 175, 106–113 (2015)
Zhang, G., Patuwo, B., Hue, M.: Forecasting with artificial neural networks: The state of the art. Int. J. Forecast. 14, 35–62 (1998). Horton, N.J., Kleinman, K.: Using R For Data Management, Statistical Analysis, and Graphics. CRC Press, Clermont (2010)
Sultan, J.A., Jasim, R.M.: Demand forecasting using artificial neural networks optimized by artificial bee colony. Int J. Manage, Inf. Technol. Eng. 4(7), 77–88 (2016)
MathWorks, Inc: Using MATLAB, Version 6. MathWorks, Inc, MA, USA (2000)
Günther, F., Fritsch, S.: Neuralnet: Training of Neural Networks. R J. 2(1), 30–38 (2010)
Hagan, M.T., Demuth, H.B.: Neural Networks Design, 2nd edn. PWS Publication, Boston (1996)
Sengupta, S.: NPTEL lectures on Neural networks and Applications. http://nptel.ac.in/courses/117105084/
Chang, P.C., Wang, Y.W.: Fuzzy Delphi and backpropagation model for sales forecasting in PCB industry. Expert Syst. Appl. 30(4), 715–726 (2006)
Trippi, R.R., Turban, E. (eds.): Neural Networks in Finance and Investing: Using Artificial Intelligence Toimprove Real—World Performance. Probus, Chicago (1993)
Efendigil, T.: a decision support system for demand forecasting with artificial neural networks and neuro fuzzy models: A comparative analysis. Expert Syst. Appl. 36(3), 6697–6707 (2009)
Kabacoff, R.: R in Action. Manning Publications Co, Shelter Island (2011). Lander, J.P.: R for Everyone: Advanced Analytics and Graphics. Addison-Wesley Professional, Boston (2014)
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Chawla, A., Singh, A., Lamba, A., Gangwani, N., Soni, U. (2019). Demand Forecasting Using Artificial Neural Networks—A Case Study of American Retail Corporation. In: Malik, H., Srivastava, S., Sood, Y., Ahmad, A. (eds) Applications of Artificial Intelligence Techniques in Engineering . Advances in Intelligent Systems and Computing, vol 697. Springer, Singapore. https://doi.org/10.1007/978-981-13-1822-1_8
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DOI: https://doi.org/10.1007/978-981-13-1822-1_8
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