Revolutionize cold chain: an AI/ML driven approach to overcome capacity shortages
研究了利用Prophet和SARIMA等AI/ML方法进行冷链产能规划，与Americold合作，基于385个客户和3.5年数据实现全站点MAPE 5.28%，并通过客户细分矩阵提高预测准确性，帮助管理者应对产能短缺。
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.