Abstract
Product sales forecasting is a key aspect of purchasing management. Adoption of predictive analytics helps estimate market demand and determine inventory stock levels, which have been ongoing challenges, especially in the supermarkets and grocery stores. The total expected profit is reduced if a required level of products is not available. As a result, sales forecasting for goods has a significant impact to minimize the total costs associated with the lost opportunity. The purpose of this study is to create a forecasting model using machine learning algorithms, to get accurate forecasts for product sales. In this regard, several regression models (i.e., random forest, linear regression, decision tree) are employed and then, their results are discussed in detail using a grocery store's data set. Furthermore, the feed forward artificial neural network is utilized to optimize the results.
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References
A.S. Ahmad, M.Y. Hassan, M.P. Abdullah, H.A. Rahman, F. Hussin, H. Abdullah, R. Saidur, A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renew. Sustain. Energy Rev. 33, 102–109 (2014)
Ö. Akar, O. Güngör, Classification of multispectral images using Random Forest algorithm. J. Geodesy Geoinf. 1(2), 105–112 (2012)
Ö.G. Ali, S. Sayın, T. Van Woensel, J. Fransoo, SKU demand forecasting in the presence of promotions. Expert Syst. Applic. 36(10), 12340–12348 (2009)
S. Bhadouria, A. Jayant, Development of ANN models for demand forecasting. Am. J. Eng. Res 6, 142–147 (2017)
N.S. Chauhan, KDnuggets Online Data Science Platform (2019). https://www.kdnuggets.com/2019/10/introduction-artificial-neural-networks.html. Accessed March 28, 2021
D. Corsten, T. Gruen, Desperately seeking shelf availability: an examination of the extent, the causes, and the efforts to address retail out-of-stocks. Int. J. Retail Distrib. Manag. 31(12), 605–617 (2003)
M. Cottrell, B. Girard, Y. Girard, M. Mangeas, C. Muller, Neural modeling for time series: a statistical stepwise method for weight elimination. IEEE Trans. Neural Networks 6(6), 1355–1364 (1995)
Favorita-grocery-sales, Open dataset from Kaggle (2019). https://www.kaggle.com/c/favorita-grocery-sales-forecasting/data. Accessed March 28, 2021
R. Fildes, S. Ma, S. Kolassa, Retail forecasting: research and practice. Int. J. Forecasting (In Press)
T. Huang, R. Fildes, D. Soopramanien, Forecasting retailer product sales in the presence of structural change. Eur. J. Oper. Res. 279(2), 459–470 (2019)
K. Kandananond, Consumer product demand forecasting based on artificial neural network and support vector machine. World Acad. Sci. Eng. Technol. 63, 372–375 (2012)
G.O. Kaya, A. Turkyilmaz, Intermittent demand forecasting using data mining techniques. Appl. Comp. Sci. 14(2) (2018)
M. Khashei, M. Bijari, An artificial neural network (p, d, q) model for timeseries forecasting. Expert Syst. Appl. 37(1), 479–489 (2010)
A. Kochak, S. Sharma, Demand forecasting using neural network for supply chain management. Int. J. Mechan. Eng. Robot. Res. 4(1), 96–104 (2015)
W.I. Lee, B.Y. Shih, C.Y. Chen, Retracted: A hybrid artificial intelligence sales-forecasting system in the convenience store industry. Human Fact. Ergon. Manufact. Serv. Ind. 22(3), 188–196 (2012)
K. Leksakul, P. Holimchayachotikul, A. Sopadang, Forecast of off-season longan supply using fuzzy support vector regression and fuzzy artificial neural network. Comput. Elect. Agricul. 118, 259–269 (2015)
H. Liu, Forecasting model of supply chain management based on neural network. In 2015 International Conference on Automation, Mechanical Control and Computational Engineering. Atlantis Press (2015, April)
A.L. Loureiro, V.L. Miguéis, L.F. da Silva, Exploring the use of deep neural networks for sales forecasting in fashion retail. Decis. Support Syst. 114, 81–93 (2018)
C.J. Lu, T.S. Lee, C.M. Lian, Sales forecasting for computer wholesalers: a comparison of multivariate adaptive regression splines and artificial neural networks. Decis. Support Syst. 54(1), 584–596 (2012)
J. Patel, S. Shah, P. Thakkar, K. Kotecha, Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Syst. Appl. 42(1), 259–268 (2015)
F. Wijnhoven, O. Plant, Sentiment analysis and google trends data for predicting car sales. In ICIS (2017)
D.H. Yip, E.L. Hines, W.W Yu, Application of artificial neural networks in sales forecasting. In Proceedings of International Conference on Neural Networks (ICNN'97) (Vol. 4, pp. 2121–2124). IEEE (1997)
T. Yiu, Towards Data Science (2019). https://towardsdatascience.com/understanding-random-forest-58381e0602d2, Accessed March 28, 2021
G. Zhang, B.E. Patuwo, M.Y. Hu, Forecasting with artificial neural networks: The state of the art. Int. J. Forecast. 14(1), 35–62 (1998)
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Yeasmin, N., Amin, S.H., Tosarkani, B.M. (2022). Machine Learning Techniques for Grocery Sales Forecasting by Analyzing Historical Data. In: Fernandes, S.L., Sharma, T.K. (eds) Artificial Intelligence in Industrial Applications. Learning and Analytics in Intelligent Systems, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-030-85383-9_2
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