Revolutionize cold chain: an AI/ML driven approach to overcome capacity shortages
Name
Revolutionize cold chain an AI ML driven approach to overcome capacity shortages.pdf
Description
Published version
Size
4.22 MB
Format
Adobe PDF
Checksum (MD5)
c9680fc95cb36b0ef0fc7a141c3b3273
Author(s)
Jackson, Ilya
Namdar, Jafar
Saénz, Maria Jesús
Elmquist III, Richard Augustus
Dávila Novoa, Luis Rodrigo
Date Issued
March 19, 2025
Journal
International Journal of Production Research
Publisher
Taylor & Francis
Citation
Jackson, I., Namdar, J., Saénz, M. J., Elmquist III, R. A., & Dávila Novoa, L. R. (2025). Revolutionize cold chain: an AI/ML driven approach to overcome capacity shortages. International Journal of Production Research, 63(6), 2190–2212.
Version
Final published version
Abstract
This research investigates how Artificial Intelligence (AI) and Machine Learning (ML) forecasting methodologies can be leveraged for cold chain capacity planning, specifically utilising Prophet and Seasonal Autoregressive Integrated Moving Average parametrised through grid search. In collaboration with Americold, the world's second-largest refrigerated logistic service provider, the study explores the challenges and opportunities in applying AI/ML techniques to complex operations covering 385 customers and a capacity of 73,296 pallet positions. We train and test several AI/ML and traditional statistical models using extensive data for every customer over 3.5 years. Based on the results, MAPE of 5.28% was achieved on the whole site level, and SARIMA outperformed ML models in most cases. Next, we show that developing and applying a Customer Segmentation Matrix has enabled more accurate forecasting and planning across various customer segments, addressing the issue of forecasting inaccuracies. This approach effectively improves forecasting inaccuracies, underscoring the significance of tailoring AI/ML models for demand forecasting within the cold-chain industry. Ultimately, this research presents an AI-driven approach that transcends mere forecasting, offering a practical pathway to manage capacity in light of the constraints.
MIT Department
Massachusetts Institute of Technology. Center for Transportation & Logistics
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1080/00207543.2024.2398583
