Multi-Location Demand Forecasting in FMCG via Deep Reinforcement Learning

Sergül Ürgenç; Ata Osman Özgüz

doi:10.34110/forecasting.1681404

Multi-Location Demand Forecasting in FMCG via Deep Reinforcement Learning

Abstract

In this study, we propose a unified model for forecasting the daily demand of Fast-Moving Consumer Goods (FMCG) across multiple restaurant locations. Unlike traditional machine learning approaches that require prior segmentation of restaurants and products or separate forecasting models for each combination, our approach enables a single model to predict sales for multiple products and locations simultaneously. To achieve this, we trained and evaluated reinforcement learning (RL) models using key features such as pricing, holidays, weather conditions, and USD exchange rates. The study utilized daily sales data spanning from January 1, 2022, to October 14, 2024, covering three restaurants and two products. We experimented with several RL-based models, including Deep Q-Network (DQN), Convolutional Deep Q-Network (CDQN), Long Short-Term Memory (LSTM)-based RL, and Recurrent Neural Networks (RNN)-based RL, comparing their performance using Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE) as evaluation metrics. Experimental results indicate that the DQN model achieved the highest predictive accuracy, outperforming other approaches. The proposed forecasting model can significantly contribute to price optimization, inventory management, and strategic decision-making, offering businesses a more efficient way to anticipate demand without the need for extensive segmentation or multiple independent models.

Keywords

Ethical Statement

This study does not involve any human participants, animal subjects, or sensitive personal data. Therefore, ethical approval was not required.

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Details

Primary Language

English

Subjects

Deep Learning, Neural Networks, Machine Learning (Other)

Journal Section

Research Article

Authors

Sergül Ürgenç ^*
0000-0003-1965-4488
Türkiye

Ata Osman Özgüz
0009-0002-8303-8348
Türkiye

Publication Date

September 30, 2025

Submission Date

April 22, 2025

Acceptance Date

September 28, 2025

Published in Issue

Year 2025 Volume: 9 Number: 2

DOI

https://doi.org/10.34110/forecasting.1681404

IZ

https://izlik.org/JA65GG49PK

Cite

RIS / Bibtex

APA

Ürgenç, S., & Özgüz, A. O. (2025). Multi-Location Demand Forecasting in FMCG via Deep Reinforcement Learning. Turkish Journal of Forecasting, 9(2), 30-36. https://doi.org/10.34110/forecasting.1681404

AMA

1.Ürgenç S, Özgüz AO. Multi-Location Demand Forecasting in FMCG via Deep Reinforcement Learning. TJF. 2025;9(2):30-36. doi:10.34110/forecasting.1681404

Chicago

Ürgenç, Sergül, and Ata Osman Özgüz. 2025. “Multi-Location Demand Forecasting in FMCG via Deep Reinforcement Learning”. Turkish Journal of Forecasting 9 (2): 30-36. https://doi.org/10.34110/forecasting.1681404.

EndNote

Ürgenç S, Özgüz AO (September 1, 2025) Multi-Location Demand Forecasting in FMCG via Deep Reinforcement Learning. Turkish Journal of Forecasting 9 2 30–36.

IEEE

[1]S. Ürgenç and A. O. Özgüz, “Multi-Location Demand Forecasting in FMCG via Deep Reinforcement Learning”, TJF, vol. 9, no. 2, pp. 30–36, Sept. 2025, doi: 10.34110/forecasting.1681404.

ISNAD

Ürgenç, Sergül - Özgüz, Ata Osman. “Multi-Location Demand Forecasting in FMCG via Deep Reinforcement Learning”. Turkish Journal of Forecasting 9/2 (September 1, 2025): 30-36. https://doi.org/10.34110/forecasting.1681404.

JAMA

1.Ürgenç S, Özgüz AO. Multi-Location Demand Forecasting in FMCG via Deep Reinforcement Learning. TJF. 2025;9:30–36.

MLA

Ürgenç, Sergül, and Ata Osman Özgüz. “Multi-Location Demand Forecasting in FMCG via Deep Reinforcement Learning”. Turkish Journal of Forecasting, vol. 9, no. 2, Sept. 2025, pp. 30-36, doi:10.34110/forecasting.1681404.

Vancouver

1.Sergül Ürgenç, Ata Osman Özgüz. Multi-Location Demand Forecasting in FMCG via Deep Reinforcement Learning. TJF. 2025 Sep. 1;9(2):30-6. doi:10.34110/forecasting.1681404