ARTIFICIAL NEURAL NETWORK BASED DEMAND FORECASTING INTEGRATED WITH FEDERAL FUNDS RATE

Anupa ARACHCHIGE; Ranil SUGATHADASA; Oshadhi HERATH; Amila THIBBOTUWAWA

doi:10.23743/acs-2021-27

ARTIFICIAL NEURAL NETWORK BASED DEMAND FORECASTING INTEGRATED WITH FEDERAL FUNDS RATE

Article Sidebar

Open full text

pdf

Published: Dec 30, 2021

DOI: https://doi.org/10.23743/acs-2021-27

Main Article Content

DOI

https://doi.org/10.23743/acs-2021-27

Authors

Anupa ARACHCHIGE

anupa13@hotmail.com

University of Moratuwa, Faculty of Engineering, Department of Transport and Logistics, Moratuwa

Ranil SUGATHADASA

ranils@uom.lk

University of Moratuwa, Faculty of Engineering, Department of Transport and Logistics, Moratuwa

Oshadhi HERATH

oshadhik@uom.lk

University of Moratuwa, Faculty of Engineering, Department of Transport and Logistics, Moratuwa

Amila THIBBOTUWAWA

amilat@uom.lk

University of Moratuwa, Faculty of Engineering, Department of Transport and Logistics, Moratuwa

Abstract

Adverse effects of inaccurate demand forecasts; stockouts, overstocks, customer loss have led academia and the business world towards accurate demand forecasting methods. Artificial Neural Network (ANN) is capable of highly accurate forecasts integrated with many variables. The use of Price and Promotion variables have increased the accuracy while the addition of other relevant variables would decrease the occurrences of errors. The use of the Federal Funds Rate as an additional macroeconomic variable to ANN forecasting models has been discussed in this research by the means of the accuracy measuring method: Average Relative Mean Absolute Error.

Keywords:

demand forecasting, artificial neural network, price, promotion, federal funds rate

References

Abolghasemi, M., Eshragh, A., Hurley, J., & Fahimnia, B. (2020). Demand Forecasting in the Presence of Systematic Events: Cases in Capturing Sales Promotions. International Journal of Production Economics, 230, 107892. https://doi.org/10.1016/j.ijpe.2020.107892 DOI: https://doi.org/10.1016/j.ijpe.2020.107892

Adebayo, A. (2018). Predictive Sales Model using Multi-layer Neural Network with Backpropagation Algorithm. International Journal of Engineering Technology, Management and Applied Sciences, 6(4), 30–40.

Ali, Ö. G., Sayin, S., van Woensel, T., & Fransoo, J. (2009). SKU demand forecasting in the presence of promotions. Expert Systems with Applications, 36(10), 12340–12348. https://doi.org/10.1016/j.eswa.2009.04.052 DOI: https://doi.org/10.1016/j.eswa.2009.04.052

Balachandra, K., Perera, H. N., & Thibbotuwawa, A. (2020). Human Factor in Forecasting and Behavioral Inventory Decisions: A System Dynamics Perspective. In International Conference on Dynamics in Logistics (pp. 516–526). Springer, Cham. https://doi.org/10.1007/978-3-030-44783-0_48 DOI: https://doi.org/10.1007/978-3-030-44783-0_48

Barker, J. (2020). Machine learning in M4 : What makes a good unstructured model? International Journal of Forecasting, 36(1), 150–155. https://doi.org/10.1016/j.ijforecast.2019.06.001 DOI: https://doi.org/10.1016/j.ijforecast.2019.06.001

Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research, 184(3), 1140–1154. https://doi.org/10.1016/j.ejor.2006.12.004 DOI: https://doi.org/10.1016/j.ejor.2006.12.004

Davydenko, A., & Fildes, R. (2016). Forecast Error Measures : Critical Review and Practical Recommendations. In Business Forecasting: Practical Problems and Solutions. John Wiley & Sons Inc. https://doi.org/10.13140/RG.2.1.4539.5281

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.2019.06.004 DOI: https://doi.org/10.1016/j.ijforecast.2019.06.004

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. The MIT Press.

Guidolin, M., Guseo, R., & Mortarino, C. (2019). Regular and promotional sales in new product life cycles: Competition and forecasting. Computers and Industrial Engineering, 130, 250–257. https://doi.org/10.1016/j.cie.2019.02.026 DOI: https://doi.org/10.1016/j.cie.2019.02.026

Harris, N. L., Nadler, L. M., & Bhan, A. K. (1984). Review of Nils Nilsson Principles of Artificial Intelligence. The American Journal of Pathology, 117(2), 262-272. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/6437232%5Cnhttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC1900435

Hewage, H. C., Perera, H. N., & De Baets, S. (2021). Forecast adjustments during post-promotional periods. European Journal of Operational Research, in press. https://doi.org/10.1016/j.ejor.2021.07.057 DOI: https://doi.org/10.1016/j.ejor.2021.07.057

Huang, T., Fildes, R., & Soopramanien, D. (2014). The value of competitive information in forecasting FMCG retail product sales and the variable selection problem. European Journal of Operational Research, 237(2), 738–748. https://doi.org/10.1016/j.ejor.2014.02.022 DOI: https://doi.org/10.1016/j.ejor.2014.02.022

Huang, T., Fildes, R., & Soopramanien, D. (2019). Forecasting retailer product sales in the presence of structural change. European Journal of Operational Research, 279(2), 459–470. https://doi.org/10.1016/j.ejor.2019.06.011 DOI: https://doi.org/10.1016/j.ejor.2019.06.011

Ludwig, N., Feuerriegel, S., & Neumann, D. (2015). Putting Big Data analytics to work: Feature selection for forecasting electricity prices using the LASSO and random forests. Journal of Decision Systems, 24(1), 19–36. https://doi.org/10.1080/12460125.2015.994290 DOI: https://doi.org/10.1080/12460125.2015.994290

Ma, S., Fildes, R., & Huang, T. (2016). Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information. European Journal of Operational Research, 249(1), 245–257. https://doi.org/10.1016/j.ejor.2015.08.029 DOI: https://doi.org/10.1016/j.ejor.2015.08.029

Matharage, S. T., Hewage, U., & Perera, H. N. (2020). Impact of Sharing Point of Sales Data and Inventory Information on Bullwhip Effect. In 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 857–861). IEEE. https://doi.org/10.1109/IEEM45057.2020.9309733 DOI: https://doi.org/10.1109/IEEM45057.2020.9309733

Ni, D., Xiao, Z., & Lim, M. K. (2019). A systematic review of the research trends of machine learning in supply chain management. International Journal of Machine Learning and Cybernetics, 11, 1463–1482. https://doi.org/10.1007/s13042-019-01050-0 DOI: https://doi.org/10.1007/s13042-019-01050-0

Oliva, R., & Watson, N. (2009). Managing functional biases in organizational forecasts: A case study of consensus forecasting in supply chain planning. Production and Operations Management, 18(2), 138–151. https://doi.org/10.1111/j.1937-5956.2009.01003.x DOI: https://doi.org/10.1111/j.1937-5956.2009.01003.x

Parker, S. (2014). Principles and Practice. IFLA Journal, 32(3), 179-180. https://doi.org/10.1177/0340035206070163 DOI: https://doi.org/10.1177/0340035206070163

Perera, H. N., & Sudusinghe, J. I. (2017). Longitudinal analysis of supply chain transformation project management. 2017 Moratuwa Engineering Research Conference (MERCon) (pp. 153–158). IEEE. https://doi.org/10.1109/MERCon.2017.7980473 DOI: https://doi.org/10.1109/MERCon.2017.7980473

Perera, H. N., Thibbotuwawa, A. I., Rajasooriyar, C., & Sugathadasa, P. R. S. (2016). Managing Supply Chain Transformation Projects in the Manufacturing Sector: Case-based Learning from Sri Lanka. In Conference on Research for Transportand Logistics Industry 2016 (pp. 143–145). R4TLI-D13.

Sagaert, Y. R., Aghezzaf, E. H., Kourentzes, N., & Desmet, B. (2018a). Tactical sales forecasting using a very large set of macroeconomic indicators. European Journal of Operational Research, 264(2), 558–569. https://doi.org/10.1016/j.ejor.2017.06.054 DOI: https://doi.org/10.1016/j.ejor.2017.06.054

Sagaert, Y. R., Aghezzaf, E. H., Kourentzes, N., & Desmet, B. (2018b). Temporal big data for tactical sales forecasting in the tire industry. Interfaces, 48(2), 121-129. https://doi.org/10.1287/inte.2017.0901 DOI: https://doi.org/10.1287/inte.2017.0901

Shahrabi, J., Mousavi, S. S., & Heydar, M. (2009). Supply chain demand forecasting: A comparison of machine learning techniques and traditional methods. Journal of Applied Sciences, 9(3), 521–527. https://doi.org/10.3923/jas.2009.521.527 DOI: https://doi.org/10.3923/jas.2009.521.527

Sharma, G. D., Singh, S., & Singh, G. S. (2012). Impact of Macroeconomic Variables on Economic Performance: An Empirical Study of India and Sri Lanka. SSRN Electronic Journal, 1-35. https://doi.org/10.2139/ssrn.1836542 DOI: https://doi.org/10.2139/ssrn.1836542

Spiliotis, E., Makridakis, S., Semenoglou, A. A., & Assimakopoulos, V. (2020). Comparison of statistical and machine learning methods for daily SKU demand forecasting. Operational Research. https://doi.org/10.1007/s12351-020-00605-2 DOI: https://doi.org/10.1007/s12351-020-00605-2

Srivastav, R., Sudheer, K., & Chaubey, I. (2007). A simplified approach to quantify predictive and parametric uncertainty in artificial neural network hydrologic models. Water Resour, 43(10), W10407. https://doi.org/10.1029/2006WR005352 DOI: https://doi.org/10.1029/2006WR005352

Ranil, P. T., Sugathadasa, S., Senadheera, S. W., & Thibbotuwawa, A. (2021). A Study of Supply Chain Risk Factors of the Large-Scale Apparel Manufacturing Companies–Sri Lanka. Engineer, 54(03), 49–58. http://doi.org/10.4038/engineer.v54i3.7459 DOI: https://doi.org/10.4038/engineer.v54i3.7459

Sugathadasa, R., Wakkumbura, D., Perera, H. N., & Thibbotuwawa, A. (2021). Analysis of Risk Factors for Temperature-Controlled Warehouses. Operations and Supply Chain Management: An International Journal, 14(3), 320–337. http://doi.org/10.31387/oscm0460305 DOI: https://doi.org/10.31387/oscm0460305

Suzuki, K. (2012). Artificial Neural Networks. Methodological Advances and Biomedical Applications. IntechOpen. Tangjitprom, N. (2012). The Review of Macroeconomic Factors and Stock Returns. International Business Research, 5(8), 107– 115. https://doi.org/10.5539/ibr.v5n8p107 DOI: https://doi.org/10.5539/ibr.v5n8p107

Verstraete, G., Aghezzaf, E. H., & Desmet, B. (2020). A leading macroeconomic indicators’ based framework to automatically generate tactical sales forecasts. Computers and Industrial Engineering, 139, 106169. https://doi.org/10.1016/j.cie.2019.106169 DOI: https://doi.org/10.1016/j.cie.2019.106169

Vhatkar, S., & Dias, J. (2016). Oral-Care Goods Sales Forecasting Using Artificial Neural Network Model. Procedia Computer Science, 79, 238–243. https://doi.org/10.1016/j.procs.2016.03.031 DOI: https://doi.org/10.1016/j.procs.2016.03.031

Wang, P.-H., Lin, G.-H., & Wang, Y.-C. (2019). Applied Sciences Application of Neural Networks to Explore Manufacturing Sales Prediction. Applied Sciences, 9(23), 5107. https://doi.org/10.3390/app9235107 DOI: https://doi.org/10.3390/app9235107

Yang, D., Goh, G. S. W., Xu, C., Zhang, A. N., & Akcan, O. (2015). Forecast UPC-level FMCG demand, Part I: Exploratory analysis and visualization. Proceedings – 2015 IEEE International Conference on Big Data (pp. 2106–2112). IEEE. https://doi.org/10.1109/BigData.2015.7363993 DOI: https://doi.org/10.1109/BigData.2015.7363993

Article Details

ARACHCHIGE, A., SUGATHADASA, R., HERATH, O., & THIBBOTUWAWA, A. (2021). ARTIFICIAL NEURAL NETWORK BASED DEMAND FORECASTING INTEGRATED WITH FEDERAL FUNDS RATE. Applied Computer Science, 17(4), 34–44. https://doi.org/10.23743/acs-2021-27

ACM
ACS
APA
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver

Endnote/Zotero/Mendeley (RIS)
BibTeX

Abstract views: 630

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.