JOURNAL ARTICLE

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

  • Published In: INFORMS Journal on Applied Analytics, 2025, v. 55, n. 3. P. 254 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Umang, Nitish; Balcavage, Thomas; Jee, Jefferson; Kumtakar, Riddhesh Nitin; Dahal, Prem Raj; Simko, Angela; Bode, James Oduntan 3 of 3

Abstract

This article focuses on the development, implementation, and deployment of LoadMax, a web-based decision-aid tool created by Johnson & Johnson's Supply Chain Digital and Data Science and MedTech Deliver Analytics & Innovation teams to optimize the three-dimensional loading and packing of outbound shipment containers at a major distribution site. LoadMax uses advanced mathematical optimization models, including integer programming and matheuristics, to generate efficient loading plans for packing products into gaylords (cardboard containers on pallets) and stacking these onto trucks, balancing computational complexity with solution accuracy for daily operational use. Deployed initially at the Memphis Logistics Center for key international shipping lanes, the tool has led to significant freight cost savings, improved container utilization, and reduced CO2 emissions, while supporting Johnson & Johnson's commitment to timely delivery of medical products. The article also details the tool's algorithmic approach, data integration, staged deployment process, and plans for future expansion and enhancement using emerging computational methods.

Additional Information

  • Source:INFORMS Journal on Applied Analytics. 2025/05, Vol. 55, Issue 3, p254
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2025
  • ISSN:2644-0865
  • DOI:10.1287/inte.2023.0085
  • Accession Number:187706319
  • Copyright Statement:Copyright of INFORMS Journal on Applied Analytics is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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