Automated Warehouse Case Study | Cold Chain Solutions

Cold chain solutions major, uscs, use
data science to improve carrier turn
times by 15%

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Challenge

High turn time in carrier pick-ups/drop-offs due to manual appointment scheduling

Approach

ML-based appointment scheduler for accurate appointment scheduling

Outcome

USCS saw a 15% improvement in carrier turn-time with 650 appointments/day

About United States Cold Storage

United States Cold Storage, Inc. (USCS), is a premier provider of public refrigerated warehousing (PRW) and related logistics services in the USA.

With roots dating back to 1899, USCS has long served a diverse customer base with requirements ranging from primary storage to fully integrated third-party logistics. They offer more than 330 million cubic feet of temperature controlled warehouse and distribution space in 43 facilities located in 13 states (California, Delaware, Florida, Georgia, Illinois, Indiana, Nebraska, North Carolina, Pennsylvania, Tennessee, Texas, Utah and Virginia). USCS is the third largest PRW Logistics provider in North America. It is a subsidiary of the U.K.’s John Swire & Sons Ltd.

Partnership - USCS & Gramener join hands

Gramener partnered with US Cold Storage to design and develop a more intelligent scheduling system. As a design-led data science organization, Gramener rapidly builds custom data and AI solutions that improve business processes using their low-code platform Gramex.

Gramener partnered with USCS in creating a machine learning-based Intelligent Appointment Scheduler, also known as IAS, that enhanced the efficacy of the dock scheduling system.

Built on Gramex low-code app builder

All the applications were built on Gramex, Gramener’s proprietary low-code app builder. Gramex is a PaaS data platform that can rapidly build customized data and AI applications by using over 200 pre-built components and microservices. The platform is easy-to-use making it ideal for citizen developers. It has a zero-learning curve because of its easy drag-and-drop visual features. All of this enables both professional developers and data enthusiasts to create enterprise-grade data applications quickly and efficiently.

THE PROBLEM

The above scenario made it necessary to accurately predict the arrival time and the time taken to service a carrier while scheduling an appointment. But challenges such as mechanical issues, fluctuating weather, previous pick-up delays, and traffic can make accurate scheduling and staff planning a complex guessing game, causing long turn times and increased costs for the facility due to over-staffing and fines. There was a need for an evolved data-driven system that could schedule carrier appointments with high precision.

THE APPROACH

USCS approached Gramener to come up with a smart and predictive solution to their challenge. Gramener developed the Intelligent Appointment Scheduler that uses historical data and automates the carrier appointments with sufficient staffing and precision in scheduling. The Intelligent Appointment Scheduler (IAS) is an intuitive data-driven solution that has revolutionized the carrier appointment scheduling for US Cold Storage.

This solution uses predictive data to recommend and automatically schedule appointments. It evaluates various parameters such as order complexity, warehouse load, pallets and case-picks, and the expected delay at the carrier’s end. With such intuitive information and machine learning capabilities, IAS analyzes heaps of data to make smart and predictive recommendations.