Accurate sales forecasting is essential in the fast-paced world of business for effective strategic planning and resource allocation. However, traditional forecasting methods often lack precision and flexibility. This study aims to address this issue by incorporating machine learning (ML) techniques to improve forecasting accuracy and responsiveness to market changes. The methodology involves gathering extensive sales data and carefully preprocessing it to ensure quality. Various ML algorithms, such as time series analysis, regression models, and neural networks, are utilized to account for the complex and non-linear nature of sales patterns. These models are trained and validated using historical sales data, taking into consideration external factors like economic indicators and consumer trends. The results show a significant enhancement in forecast accuracy compared to traditional methods. The ML models effectively capture underlying trends and seasonal variations, providing reliable predictions that closely match actual sales results. Additionally, the models demonstrate strong adaptability, quickly adjusting to unexpected market shifts.
References
[1]
Ofoegbu, K. (2021) A Comparison of Four Machine Learning Algorithms to Predict Product Sales in a Retail Store. Ph.D. Thesis, Dublin Business School.
[2]
Deo, R.C., Kisi, O. and Singh, V.P. (2017) Drought Forecasting in Eastern Australia Using Multivariate Adaptive Regression Spline, Least Square Support Vector Machine and M5tree Model. Atmospheric Research, 184, 149-175. https://doi.org/10.1016/j.atmosres.2016.10.004
[3]
Álvarez-Díaz, M., González-Gómez, M. and Otero-Giráldez, M. (2018) Forecasting International Tourism Demand Using a Non-Linear Autoregressive Neural Network and Genetic Programming. Forecasting, 1, 90-106. https://doi.org/10.3390/forecast1010007
[4]
Shumway, R.H. and Stoffer, D.S. (2017) ARIMA Models. Time Series Analysis and Its Applications. Springer.
[5]
Lu, C. and Kao, L. (2016) A Clustering-Based Sales Forecasting Scheme by Using Extreme Learning Machine and Ensembling Linkage Methods with Applications to Computer Server. Engineering Applications of Artificial Intelligence, 55, 231-238. https://doi.org/10.1016/j.engappai.2016.06.015
[6]
Hofmann, E. (2013) Supply Chain Management: Strategy, Planning and Operation. Elsevier Science.
[7]
Mentzer, J. and Moon, M. (2005). Sales Forecasting Management: A Demand Management Approach. SAGE Publications. https://doi.org/10.4135/9781452204444
[8]
Ballon, R. (2004) Business Logistics/Supply Chain Management: Planning, Organizing and Controlling the Supply Chain. 5th Edition, Pearson.
[9]
Pavlyshenko, B. (2019) Machine-learning Models for Sales Time Series Forecasting. Data, 4, Article 15. https://doi.org/10.3390/data4010015
[10]
Hussain, S., Atallah, R., Kamsin, A. and Hazarika, J. (2018) Classification, Clustering and Association Rule Mining in Educational Datasets Using Data Mining Tools: A Case Study. In: Silhavy, R., Ed., Cybernetics and Algorithms in Intelligent Systems, Springer, 196-211. https://doi.org/10.1007/978-3-319-91192-2_21
[11]
Kaur, M. and Kang, S. (2016) Market Basket Analysis: Identify the Changing Trends of Market Data Using Association Rule Mining. Procedia Computer Science, 85, 78-85. https://doi.org/10.1016/j.procs.2016.05.180
[12]
Sinaga, K.P. and Yang, M. (2020) Unsupervised K-Means Clustering Algorithm. IEEE Access, 8, 80716-80727. https://doi.org/10.1109/access.2020.2988796
[13]
Catal, C., Ece, K., Arslan, B. and Akbulut, A. (2019) Benchmarking of Regression Algorithms and Time Series Analysis Techniques for Sales Forecasting. Balkan Journal of Electrical and Computer Engineering, 7, 20-26. https://doi.org/10.17694/bajece.494920
[14]
Glynn, J., Perera, N. and Verma, R. (2007) Unit Root Tests and Structural Breaks: A Survey with Applications. https://www.researchgate.net/publication/30387415_Unit_Root_Tests_and_Structural_Breaks_A_Survey_with_Applications
[15]
Chai, T. and Draxler, R.R. (2014) Root Mean Square Error (RMSE) or Mean Absolute Error (MAE). Geoscientific Model Development Discussions, 7, 1525-1534.
