Reverse logistics optimization in the indian dairy industry forecasting vendor returns to reduce spoilage| International Journal of Innovative Science and Research Technology

Reverse Logistics Optimization in the Indian Dairy Industry: Forecasting Vendor Returns to Reduce Spoilage

Authors : Aman Raj Singh; Manish Raj; Arikatla Sai Sumedha; Goalla Kartheek; Bharani Kumar Depuru

Volume/Issue : Volume 10 - 2025, Issue 3 - March

Google Scholar : https://tinyurl.com/mv9v43nj

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DOI : https://doi.org/10.38124/ijisrt/25mar1266

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Abstract : The dairy industry grapples with significant hurdles in reverse logistics, particularly due to high return rates from vendors, inefficiencies in transportation, and challenges faced by retailers. Perishable goods such as milk, yogurt, and buttermilk have limited shelf lives, making accurate demand prediction and inventory control crucial to minimizing waste and financial losses. This research focuses on predicting vendor-specific return rates to streamline the reverse logistics process. We employ ARIMA and XGBoost models to forecast return rates from vendors using historical sales data, seasonal trends, and regional demand variations. By accurately predicting returns, we enable proactive redistribution of stock, ensuring products close to expiration are redirected to high-demand regions before spoilage occurs. Additionally, we propose inventory optimization strategies, including FIFO-based stock rotation and dynamic demand adjustments, to reduce waste. To enhance logistical efficiency, we implement route optimization, IoT-enabled real-time monitoring, and temperature-controlled transportation, reducing delays and maintaining product quality. Furthermore, we address retailer challenges through targeted sales training, cold chain infrastructure support, and incentive programs to improve demand planning and reduce overstocking. By integrating machine learning-based forecasting, operational enhancements, and supply chain optimization, our approach improves efficiency, reduces spoilage, lowers costs, and promotes sustainability in dairy reverse logistics. The proposed framework is adaptable to other industries dealing with perishable goods and similar return-related challenges.

Keywords : Reverse Logistics, Dairy Industry, Vendor Return Forecasting, ARIMA, LSTM, XGBoost, Supply Chain Optimization, Machine Learning, IoT.

References :

Manisha Jayprakash singh. 2024. A Study on Problem and Prospect of dairy Industry in India. IJIRT166092_PAPER.pdf
Gyanesh Kumar Sinha, Sumit Mishra. 2023. Sustainable Supply Chain Management Practices in the Dairy Industry: A Comparative Study of Leading Dairy Firms and Future Research Directives. https://arccjournals.com/journal/asian-journal-of-dairy-and-food-research/DR-2120
Sima Siami Namini, Neda Tavakoli Akbar Siami Namin. 2018. A Comparison of ARIMA and LSTM in Forecasting Time Series. https://www.researchgate.net/publication/330477082
Abhishek Kashyap, Om Ji Shukla, Bal Krishna Jha, Bharti Ramtiyal, Gunjan Soni. 2023. Enhancing Sustainable Dairy Industry Growth through Cold-Supply-Chain-Integrated Production Forecasting. https://www.mdpi.com/2071-1050/15/22/16102
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The dairy industry grapples with significant hurdles in reverse logistics, particularly due to high return rates from vendors, inefficiencies in transportation, and challenges faced by retailers. Perishable goods such as milk, yogurt, and buttermilk have limited shelf lives, making accurate demand prediction and inventory control crucial to minimizing waste and financial losses. This research focuses on predicting vendor-specific return rates to streamline the reverse logistics process. We employ ARIMA and XGBoost models to forecast return rates from vendors using historical sales data, seasonal trends, and regional demand variations. By accurately predicting returns, we enable proactive redistribution of stock, ensuring products close to expiration are redirected to high-demand regions before spoilage occurs. Additionally, we propose inventory optimization strategies, including FIFO-based stock rotation and dynamic demand adjustments, to reduce waste. To enhance logistical efficiency, we implement route optimization, IoT-enabled real-time monitoring, and temperature-controlled transportation, reducing delays and maintaining product quality. Furthermore, we address retailer challenges through targeted sales training, cold chain infrastructure support, and incentive programs to improve demand planning and reduce overstocking. By integrating machine learning-based forecasting, operational enhancements, and supply chain optimization, our approach improves efficiency, reduces spoilage, lowers costs, and promotes sustainability in dairy reverse logistics. The proposed framework is adaptable to other industries dealing with perishable goods and similar return-related challenges.

Keywords : Reverse Logistics, Dairy Industry, Vendor Return Forecasting, ARIMA, LSTM, XGBoost, Supply Chain Optimization, Machine Learning, IoT.