A proactive food demand forecasting-inventory management approach under weather disruptions

Document Type

Article

Source of Publication

Cleaner Logistics and Supply Chain

Publication Date

6-1-2026

Abstract

Effective demand forecasting has become crucial to strengthening system resilience, reducing food waste, and achieving sustainability in food systems. Despite recent advances in leveraging machine learning for food demand forecasting, most existing models remain static and assume stable demand patterns, posing a challenge for adapting to demand changes during disruption events. This paper develops a proactive approach that leverages demand forecasting outputs and weather disruption flags to guide inventory replenishment, ensuring adaptability to varying demand conditions across three weather disruption events while reducing waste. This paper first uses a stacking model to predict next-day demand for a food retailer, leveraging real-world historical and weather datasets. Additionally, three weather-disruption flags are identified from real weather data using rule-based approaches. The proposed methodology further uses predicted demand and disruption flags to inform inventory order quantities. The proposed proactive approach is assessed using real-world data from the Australian food market, and the findings reveal that the model performs consistently across the three weather disruption events, achieving an average accuracy of 86% across all conditions. Additionally, the findings highlight the effectiveness of the integrated approach in strengthening system resilience, reducing food waste, and supporting more sustainable, adaptive food-supply operations during weather disruptions.

ISSN

2772-3909

Publisher

Elsevier BV

Volume

19

Disciplines

Computer Sciences

Keywords

Demand forecasting, Food Waste, Inventory, Machine learning, Resilience, Sustainability, Weather disruption

Scopus ID

105032702282

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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