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Johnson & Johnson Uses Advanced Analytics to Optimize Gaylord Building and Truck Loading for Outbound Container Shipments

Author

Listed:
  • Nitish Umang

    (Johnson & Johnson Supply Chain Digital & Data Science, New Brunswick, New Jersey 08933)

  • Thomas Balcavage

    (Johnson & Johnson MedTech Deliver Analytics & Innovation, New Brunswick, New Jersey 08933)

  • Jefferson Jee

    (Johnson & Johnson MedTech Deliver Analytics & Innovation, New Brunswick, New Jersey 08933)

  • Riddhesh Nitin Kumtakar

    (Johnson & Johnson Supply Chain Digital & Data Science, New Brunswick, New Jersey 08933)

  • Prem Raj Dahal

    (Johnson & Johnson Healthcare Systems, Memphis Logistics Center, Memphis, Tennessee 38118)

  • Angela Simko

    (Johnson & Johnson Supply Chain Digital & Data Science, New Brunswick, New Jersey 08933)

  • James Oduntan Bode

    (Johnson & Johnson Supply Chain Digital & Data Science, New Brunswick, New Jersey 08933)

Abstract

Johnson & Johnson ships millions of product units through third-party vendors to meet the healthcare needs of the global population. Efficient loading and packing of container shipments is critical to reducing operating costs and improving supply chain resilience. The Johnson & Johnson Supply Chain Digital and Data Science team, in collaboration with the Johnson & Johnson MedTech Deliver Analytics & Innovation team, developed a web-based decision-aid tool called LoadMax that uses advanced optimization solutions to generate three-dimensional plans for loading picked containers into gaylords and stacking gaylords onto trucks for outbound container shipments. The tool is currently deployed at a major distribution site in Johnson & Johnson to optimize the loading of container shipments on the eight largest shipping lanes by volume, resulting in significant savings in operating cost and streamlining the shipment process. In the future, there are plans to expand the deployment across other major international distribution sites and outbound lanes with total estimated annual net savings of millions of dollars.

Suggested Citation

  • Nitish Umang & Thomas Balcavage & Jefferson Jee & Riddhesh Nitin Kumtakar & Prem Raj Dahal & Angela Simko & James Oduntan Bode, 2025. "Johnson & Johnson Uses Advanced Analytics to Optimize Gaylord Building and Truck Loading for Outbound Container Shipments," Interfaces, INFORMS, vol. 55(3), pages 254-262, May.
    • Handle: RePEc:inm:orinte:v:55:y:2025:i:3:p:254-262
    DOI: 10.1287/inte.2023.0085
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