GlobalTranz and MIT Center for Transportation and Logistics Develop Load Optimization Technology

Scenarios across different regions of the U.S. and demonstrate an average potential costs savings of 15.6% and an average reduction in truck usage of 11.1%

GlobalTranz Enterprises, LLC., a leading technology-enabled third-party logistics solutions provider offering award-winning technology, people-powered customer service, and extensive multimodal carrier relationships, launched a collaboration with the MIT Center for Transportation & Logistics (MIT CTL) to develop a scalable, technology-driven solution for load consolidation challenges. The capstone project results will debut at the MIT Supply Chain Management Research Fest in May 2021.

The project, initiated in the third quarter of 2020, addresses shipping challenges felt across the logistics industry. For shippers, the challenge of accessing reliable capacity at acceptable rates became an acute challenge in 2020 due to increased market volatility. For carriers, historically high shipment volumes represented a challenge for efficient asset utilization, especially when shippers required lengthy linehauls between hubs rather than local deliveries near hubs -the preferred model for many carriers. The GlobalTranz project with MIT CTL incorporated this “voice of shipper” and “voice of carrier” feedback to develop a scalable solution for optimizing modes and routes to limit transportation costs and transit times, reduce the risk of damaged freight, and impact carbon emissions, while maximizing truck utilization, overall efficiency and end-customer satisfaction.

To achieve this goal, the GlobalTranz Data Science team engaged MIT CTL Master’s degree candidates in Supply Chain Management, Olivia Schaufenbuel, SCM 2021, and Daniel Piechnik, SCM 2021, to design a scalable and automated solution used to describe how shipments can be optimally paired and consolidated in order to reduce total network transportation costs and create efficiencies.

“Our GlobalTranz Data Science team collaborated with experts at MIT CTL to model how algorithmic approaches and constraints affect order consolidation opportunities,” says Aditya Athavale, Senior Manager of Analytics at GlobalTranz. “We’re excited to have the opportunity to innovate and leverage cutting-edge techniques to develop and implement this solution.”

To date, the teams have developed a working model with impressive and promising results. The initial model results as of February 2021 are based on several different scenarios across different regions of the U.S. and demonstrate an average potential costs savings of 15.6% and an average reduction in truck usage of 11.1%. Ultimately, the model will be applied across multiple modes of transportation to drive efficiencies.

“We’re thrilled with the results of the model to date and look forward to creating further efficiencies,” said Matthias Winkenbach, project advisor at MIT CTL. “Our team values the opportunity to work with innovative industry players such as GlobalTranz to apply our solutions to real-world challenges.”

In addition to collaborating closely on the model, the GlobalTranz team has had the opportunity to join experts at MIT CTL and other members of the MIT CTL Supply Chain Exchange to share ideas and industry solutions at various virtual roundtables and forums.

“We view this ongoing collaboration as ideal because both organizations are vocal and active participants in innovating for the logistics industry,” says GlobalTranz Chief Technology Officer Russ Felker. “Given the rapid changes in the industry this past year, our work with MIT CTL allows us to pioneer new models which will transform the industry overall.”

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