Automation of Manual Transport Processes

Author
.
2024

Overview

Challenge

Efficient planning of energy transport logistics across the supply chain is a significant challenge for the sector. Several key issues impact this operation:  

  • Time-intensive planning process: Manual planning of trailer loads requires considerable time and effort for a team of >3 planners.
  • Dynamic logistics plan: frequent updates to the plan are needed to accommodate constantly changing incoming cargo requests, often at short notice.
  • Dependency on individual knowledge: The planning process heavily relies on the expertise of individual team members, creating bottle necks and dependencies.

Solution

  • Blend improved the client’s cloud infrastructure and enhanced their data through the deployment of Databricks.
  • In close collaboration with the controllers in the Transport team, Blend built an optimiser– through advanced data science techniques— that automates the planning of trailer loads from a pool of cargo requests.
  • Blend visualised the client data in a Power BI Dashboard, providing real-time visibility of metrics including acceptance rates, rejection reasons, overall fleet efficiency, and breakdowns of cargo transported by client.

Impact

  • Optimiser runs within <5minutes, returning results of ~85% accuracy. This runtime has saved 2-3 hours daily for the team – freeing up their time for high-value tasks.
  • Optimiser has increased the utilisation of trailers by 20%.
  • Deployment of the optimiser has created more sustainable business operations as there is reduced dependency on the logistics planners.

Key Data Points

85%
accuracy is attained with Optimiser
20%
increase in trailer utilisation
2-3
hours saved daily for team