Demand Forecasting of Retail Sales Using Data Analytics and Statistical Programming

Aburto, L., & Weber, R. (2007). Improved supply chain management based on hybrid demand forecasts. Applied Soft Computing, 7(1), 136-144. https://doi.org/10.1016/j.asoc.2005.06.00110.1016/j.asoc.2005.06.001Search in Google Scholar

Aksoy, A., Ozturk, N., & Sucky, E. (2012). A decision support system for demand forecasting in the clothing industry. International Journal of Clothing Science and Technology, 24(4), 221-236. https://doi.org/10.1108/0955622121123282910.1108/09556221211232829Search in Google Scholar

Alon, I., Qi, M., & Sadowski, R. J. (2001). Forecasting aggregate retail sales: a comparison of artificial neural networks and traditional methods. Journal of retailing and consumer services, 8(3), 147-156. https://doi.org/10.1016/S0969-6989(00)00011-410.1016/S0969-6989(00)00011-4Search in Google Scholar

Aslanargun, A., Mammadov, M., Yazici, B., & Yolacan, S. (2007). Comparison of ARIMA, neural networks and hybrid models in time series: tourist arrival forecasting. Journal of Statistical Computation and Simulation, 77(1), 29-53. https://doi.org/10.1080/1062936060056487410.1080/10629360600564874Search in Google Scholar

Au, K. F., Choi, T. M., & Yu, Y. (2008). Fashion retail forecasting by evolutionary neural networks. International Journal of Production Economics, 114(2), 615-630. https://doi.org/10.1016/j.ijpe.2007.06.01310.1016/j.ijpe.2007.06.013Search in Google Scholar

Barreto, L., Amaral, A., & Pereira, T. (2017). Industry 4.0 implications in logistics: an overview. Procedia Manufacturing, 13, 1245-1252. https://doi.org/10.1016/j.promfg.2017.09.04510.1016/j.promfg.2017.09.045Search in Google Scholar

Boone, T., Ganeshan, R., Jain, A., & Sanders, N. R. (2019). Forecasting sales in the supply chain: Consumer analytics in the big data era. International Journal of Forecasting, 35(1), 170-180. https://doi.org/10.1016/j.ijforecast.2018.09.00310.1016/j.ijforecast.2018.09.003Search in Google Scholar

Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.Search in Google Scholar

Chambers J., Mullick K. & Smith D., (1971), How to Choose the Right Forecasting Technique. [online]. Available at: https://hbr.org/1971/07/how-to-choose-the-right-forecasting-technique [accessed at 27.11.2019].Search in Google Scholar

Diebold, F. X., & Mariano, R. S. (2002). Comparing predictive accuracy. Journal of Business & economic statistics, 20(1), 134-144. https://doi.org/10.1198/07350010275341044410.1198/073500102753410444Search in Google Scholar

Efthymiou, O. Κ., & Ponis, S. T. (2019). Current Status of Industry 4.0 in Material Handling Automation and In-house Logistics. International Journal of Industrial and Manufacturing Engineering, 13(10), 1370-1374. https://doi.org/10.5281/zenodo.3566333Search in Google Scholar

Fildes, R., Ma, S., & Kolassa, S. (2019). Retail forecasting: Research and practice. International Journal of Forecasting (in press). https://doi.org/10.1016/j.ijforecast.2019.06.00410.1016/j.ijforecast.2019.06.004Search in Google Scholar

Gardner Jr, E. S. (1985). Exponential smoothing: The state of the art. Journal of Forecasting, 4(1), 1-28. https://doi.org/10.1002/for.398004010310.1002/for.3980040103Search in Google Scholar

Geurts, M. D., & Kelly, J. P. (1986). Forecasting retail sales using alternative models. International Journal of Forecasting, 2(3), 261-272. https://doi.org/10.1016/0169-2070(86)90046-410.1016/0169-2070(86)90046-4Search in Google Scholar

Kohzadi, N., Boyd, M. S., Kermanshahi, B., & Kaastra, I. (1996). A comparison of artificial neural network and time series models for forecasting commodity prices. Neurocomputing, 10(2), 169-181. https://doi.org/10.1016/0925-2312(95)00020-810.1016/0925-2312(95)00020-8Search in Google Scholar

Kuo, C., & Reitsch, A. (1995). Neural networks vs. conventional methods of forecasting. The Journal of Business Forecasting, 14(4), 17.Search in Google Scholar

Vassakis, K., Petrakis, E., & Kopanakis, I. (2018). Big data analytics: Applications, prospects and challenges. In: Skourletopoulos G., Mastorakis G., Mavromoustakis C., Dobre C., Pallis E. (eds) Mobile Big Data. Lecture Notes on Data Engineering and Communications Technologies, vol 10. (pp. 3-20). Springer. https://doi.org/10.1007/978-3-319-67925-9_110.1007/978-3-319-67925-9_1Search in Google Scholar

Sartorius, L. C., & Mohn, N. C. (1976). Sales forecasting models: a diagnostic approach (No. 69). Georgia State University Press.Search in Google Scholar

Seeger, M. W., Salinas, D., & Flunkert, V. (2016). Bayesian intermittent demand forecasting for large inventories. In: Advances in Neural Information Processing Systems (pp. 4646-4654).Search in Google Scholar

Silver Ed. A., Pyke D. F. & Peterson R., (1998). Inventory Management and Production Planning and Scheduling, 3, pp. 86-98. Wiley.Search in Google Scholar

Takahashi, K., & Nakamura, N. (2004). Push, pull, or hybrid control in supply chain management. International Journal of computer integrated manufacturing, 17(2), 126-140. https://doi.org/10.1080/0951192031000159308310.1080/09511920310001593083Search in Google Scholar

Wong, W. K., & Guo, Z. X. (2010). A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm. International Journal of Production Economics, 128(2), 614-624. https://doi.org/10.1016/j.ijpe.2010.07.00810.1016/j.ijpe.2010.07.008Search in Google Scholar

Zhang, G. P., & Qi, M. (2005). Neural network forecasting for seasonal and trend time series. European Journal of Operational Research, 160(2), 501-514. https://doi.org/10.1016/j.ejor.2003.08.03710.1016/j.ejor.2003.08.037Search in Google Scholar