When refrigerated or frozen goods arrive at a Lineage Logistics warehouse, advanced technology kicks into gear. Computer vision scans pallets and records essential details about customers, products, and item descriptions. AI-driven algorithms merge this shipment data with historical patterns to predict when trucks will depart the warehouse, assigning optimal storage locations and guiding forklift operators.
While such technology enhances efficiency across various supply chains, it’s especially crucial in cold warehouses that store perishable goods like frozen foods, produce, and pharmaceuticals. Even a slight temperature fluctuation can compromise quality, making accuracy and productivity vital.
Though refrigeration and temperature-sensing technology have been foundational for years, the industry is now embracing more sophisticated solutions. Cold-chain providers are moving away from manual methods to AI algorithms and exploring innovations such as digital twins and AI agents to automate operations further.
“Technology is pervasive in the cold chain, whether it’s long-standing systems or cutting-edge AI,” says Sudarsan Thattai, CIO of Lineage Logistics.
At Lineage, AI is employed through decision algorithms that optimize the placement of products upon arrival. For example, when a shipment of poultry from Tyson Foods arrives, the algorithms instruct forklift operators to store items in a way that minimizes travel distances within the warehouse.
Cold-chain provider Americold is also witnessing significant technological interest, especially in sectors like pharmaceuticals and fresh produce due to stringent regulations. While customers may not explicitly request AI, they do expect outcomes that AI can help facilitate, such as reducing stockouts and enabling real-time responses to changes. Americold invests in predictive analytics to better anticipate customer demands and optimize warehousing capacity.
Unilever utilizes AI to predict ice cream demand across its cold chain, analyzing weather patterns to forecast sales fluctuations. This approach has led to improved forecasting accuracy and higher sales.
Despite these advancements, challenges remain regarding data sharing and visibility within the cold chain. Many businesses still operate with outdated manual processes, impeding the effectiveness of AI predictions. As Thattai notes, achieving the full potential of AI in this sector is still a work in progress.