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

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Title: Johnson & Johnson Uses Advanced Analytics to Optimize Gaylord Building and Truck Loading for Outbound Container Shipments.
Authors: Umang, Nitish (AUTHOR), Balcavage, Thomas (AUTHOR), Jee, Jefferson (AUTHOR), Kumtakar, Riddhesh Nitin (AUTHOR), Dahal, Prem Raj (AUTHOR), Simko, Angela (AUTHOR), Bode, James Oduntan (AUTHOR)
Source: INFORMS Journal on Applied Analytics. May/Jun2025, Vol. 55 Issue 3, p254-262. 9p.
Subjects: Shipping containers, Johnson & Johnson (Company), Business enterprises, Resource allocation, Cost control, Freight & freightage, Data analytics, Transportation problems (Programming)
Abstract: This paper describes the implementation and deployment of LoadMax, an advanced web-based decision aid tool designed to optimize the loading and packing of outbound shipment containers at a major Johnson & Johnson distribution site. The LoadMax tool leverages advanced algorithms to balance computational complexity with solution accuracy, enabling the generation of efficient 3D loading plans for daily shipment operations. 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. [ABSTRACT FROM AUTHOR]
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.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Johnson & Johnson Uses Advanced Analytics to Optimize Gaylord Building and Truck Loading for Outbound Container Shipments.
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  Data: <searchLink fieldCode="AR" term="%22Umang%2C+Nitish%22">Umang, Nitish</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Balcavage%2C+Thomas%22">Balcavage, Thomas</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jee%2C+Jefferson%22">Jee, Jefferson</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kumtakar%2C+Riddhesh+Nitin%22">Kumtakar, Riddhesh Nitin</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dahal%2C+Prem+Raj%22">Dahal, Prem Raj</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Simko%2C+Angela%22">Simko, Angela</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bode%2C+James+Oduntan%22">Bode, James Oduntan</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22INFORMS+Journal+on+Applied+Analytics%22">INFORMS Journal on Applied Analytics</searchLink>. May/Jun2025, Vol. 55 Issue 3, p254-262. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Shipping+containers%22">Shipping containers</searchLink><br /><searchLink fieldCode="DE" term="%22Johnson+%26+Johnson+%28Company%29%22">Johnson & Johnson (Company)</searchLink><br /><searchLink fieldCode="DE" term="%22Business+enterprises%22">Business enterprises</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Cost+control%22">Cost control</searchLink><br /><searchLink fieldCode="DE" term="%22Freight+%26+freightage%22">Freight & freightage</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analytics%22">Data analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Transportation+problems+%28Programming%29%22">Transportation problems (Programming)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper describes the implementation and deployment of LoadMax, an advanced web-based decision aid tool designed to optimize the loading and packing of outbound shipment containers at a major Johnson & Johnson distribution site. The LoadMax tool leverages advanced algorithms to balance computational complexity with solution accuracy, enabling the generation of efficient 3D loading plans for daily shipment operations. 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>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.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=187706319
RecordInfo BibRecord:
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        Value: 10.1287/inte.2023.0085
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      – Code: eng
        Text: English
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        PageCount: 9
        StartPage: 254
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      – SubjectFull: Shipping containers
        Type: general
      – SubjectFull: Johnson & Johnson (Company)
        Type: general
      – SubjectFull: Business enterprises
        Type: general
      – SubjectFull: Resource allocation
        Type: general
      – SubjectFull: Cost control
        Type: general
      – SubjectFull: Freight & freightage
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      – SubjectFull: Data analytics
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      – SubjectFull: Transportation problems (Programming)
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      – TitleFull: Johnson & Johnson Uses Advanced Analytics to Optimize Gaylord Building and Truck Loading for Outbound Container Shipments.
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            – D: 01
              M: 05
              Text: May/Jun2025
              Type: published
              Y: 2025
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