Manuscript received June 2, 2023; revised August 12, 2023; accepted September 28, 2023; published January 16, 2024
Abstract—Food industry is one of the most important industries in Thailand. The case-study company is a condiment manufacturer that needs to efficiently manage and plan for their business. One of the most important issues is demand forecasting. The company should precisely forecast their product demands, which will be used for operation planning. This study proposes forecasting models for both short-term and long-term planning for the company’s main condiment products. The proposed models are time series, machine learning and hybrid forecasting models which will be compared with pure time series and machine learning methods. Unlike previous work, this study proposes an innovative hybrid model, i.e., Holt- Winters exponential smoothing and Seasonal Autoregressive Integrated Moving average hybrid with Artificial neural network, which has never been considered previously. The accuracy is measured by mean absolute percentage error (MAPE) where the results are also compared to the method currently used in the company. The results show hybrid forecasting model provides the lowest overall error for both short-term and long-term forecast. The most accurate model from this paper can provide MAPE of 2.07% from short-term forecast and MAPE of 2.20% for long-term forecast (6 months in advance). When comparing with the company’s existing MAPE of 20.05%, the proposed model can increase forecast accuracy effectively.
Keywords—hybrid forecasting models, artificial neural network, food demand forecasting, machine learning
Cite: Naragain Phumchusri and Woratanon Sirimak, "Time Series and Machine Learning Hybrid Models for Food Condiment Demand Forecasting: A Case Study in Thailand," International Journal of Machine Learning vol. 14, no. 1, pp. 1-6, 2024.
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).