Network Optimization and Simulation Solution for a Leading Logistics Provider

Case Study

Network Optimization and Simulation Solution for a Leading Logistics Provider

Business Objective

Our client is the logistic planning team of a major logistics solutions provider with one of the largest managed logistics and transportation networks in the world. The client wanted to get the answers to the following questions:

  • Which are top origins and destination locations?
  • What is the impact of increasing/decreasing DCs (Distribution Centers) on cost and service metrics?
  • What is my current baseline cost?
  • Do I have the right number of DCs?

Hence, they wanted to:

  • Develop a network optimization model reflecting imminent operational changes
  • Build a network simulator to simulate various network strategies including production/distribution constraints
  • Understand usage of DCs and produce key reports outlining current network statistics

Challenges

  • Data from the client’s logistics management system and their customer’s system was not in harmony
  • Building a robust network simulation for a 10K+ mile network required significant computation

Solution Methodology 

  • Utilized shipment history data at order level comprising of origin/destination points and their types, estimated and actual delivery time, weight, product, carrier, vehicle, mode, freight and warehousing costs, and production/distribution constraints as input data for model development
  • Performed data cleaning and network mapping to understand the top origin and destination cities, identify peak periods of shipments, average weight handled by each of the DCs vs capacity, and understand key products vis-à-vis destinations
  • Built models using Google OR tools (open source) to optimize the network leveraging Constraint Programming, Glop Linear Optimizer, and routing
  • Created multiple scenarios with various combinations of DCs, transfer points, and constraints to understand the impact on delivery times, costs, and shipment handling capacity
  • Presented optimized network solutions considering certain imminent changes and provided the following recommendations:
    • Relocating one of the DCs
    • Reallocating capacity amongst existing DCs
    • Adding a new transfer point, etc.

Business Impact

  • Saved USD 300K annually in total operational costs
  • Potential to handle additional 200K MT shipments annually
  • Delivered a highly visual dashboard allowing users to analyze network statistics and simulate various scenarios
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