Applying Machine Learning Techniques to Forecast Demand in a South African Fast-Moving Consumer Goods Company

作者

Martin Chanza¹ , Louise De Koker² , Sasha Boucher² , Elias Munapo¹ , Gugulethu Mabuza²

1. North-West University
2. Nelson Mandela University

会议/活动

6th International Conference on Intelligent Computing & Optimization 2023, April 2023 (Hua Hin, Prachuap Khiri Khan, Thailand)

海报摘要

Inventory planning is a critical function in FMCG companies, and forecasting plays a pivotal role in demand forecasting. The primary objective of this study was to compare the forecasting ability of statistical forecasting methods and machine learning models in demand forecasting in the FMCG sector. A case study approach was followed, using sales data from a specific category in a selected FMCG company for 2014-2019. Moving Average models, Seasonal Autoregressive Integrated Moving Average models and Artificial Neural Networks were used in this study. The results of this revealed that ANN model is more accurate in predicting demand in the FMCG sector. Forecasts from the ANN model showed an increasing trend in the sales of the supplements category. There is an increasing demand in baby products in the supplements category. For further research, we recommend using the Auto-Rforrressive Integrated Moving Average (ARIMAX) model for modeling demand when multivariate data is present.

关键词

Machine Learning, SARIMA, Demand Forecasting, FMCG

研究领域

Business, Economics and Finance, Statistics

参考文献

1. A. Dikshit, B. Pradhan, and M. Santosh, ‘Artificial neural networks in drought prediction in the 21st century–A scientometric analysis’, Appl. Soft Comput., vol. 114, p. 108080, Jan. 2022, doi: 10.1016/j.asoc.2021.108080

基金

暂无数据

补充材料

暂无数据

附加信息

利益冲突

No competing interests were disclosed.

数据可用性声明

The datasets generated during and / or analyzed during the current study are available from the corresponding author on reasonable request.

知识共享许可协议

Copyright © 2023 Chanza et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.