Case Study

Optimizing Distribution Network Helps Purolator Meet Commitments and Potentially Save Millions

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Objective: Optimize the network to save costs while still meeting delivery commitments

Solutions: LLamasoft Supply Chain Guru, Data Guru, Network Optimization and Transportation Optimization

Results: By adjusting delivery options, service commitments and rationalizing its footprint, Purolator identified significant potential savings opportunities while still meeting customer commitments.

Optimizing Distribution Network Helps Purolator Meet Commitments and Potentially Save Millions

Challenge

Meeting promises is vital to business success. No organization understands this better than Purolator, Canada’s leading integrated freight, package, and logistics solutions provider. They’ve been delivering on their customer promises for over 50 years.

When a company cultivates a reputation for reliability and quality, success often follows. Nevertheless, there are still challenges as these organizations grow and markets change. The challenges for Purolator were primarily found in its organically grown distribution network of more than 154 terminals and premium services provided. The network had never been fully optimized (nationally) considering these three main variables: service, cost and flexibility.

In addition, margins can be tight in the logistics business as customers naturally look to save money. Purolator management wanted to see if they could optimize their network to lower costs and still deliver the same high level of value and service for which the company was known. To answer this challenge, the team called in the supply chain modeling experts from LLamasoft.

Solution

Together with the Purolator supply chain team, LLamasoft used Supply Chain Guru to gain perspectives on the network from several different angles:

Network optimization– Examined the network as a whole to determine opportunities for improvement and efficiencies, by relaxing and adjusting network operating constraints.

Facility location optimization– Did they have the right number of nodes and were they in the right locations? Where new nodes could be added to improve cost structure?

Transportation optimization– What effect would changing certain pick-up and delivery parameters have on the cost model?

The team developed a baseline model of the existing network, then developed the model across several time horizons: three, five, and 10 years. Through this, they ran multiple scenarios that examined existing nodes, with an eye toward right-sizing footprint and optimizing location, plus potential new locations using greenfield analysis. They also looked at the potential impact of changing the mode of transportation between nodes, e.g., adding rail lanes between locations where it was available.

To further optimize the transportation, they did a detailed analysis of several scenario changes at one node including:

  • Changing the delivery time requirements for some of their offerings
  • Delivering in two waves, sending the early morning deliveries out first
  • Analyzing overtime costs
  • Designing high utilization multi-stop routes
  • Analyzing utilization and cost impact of delivery bands

Due to the amount of data this involved, the team used LLamasoft® Data Guru® to help manage and run data sets and then modeled the data through the cloud for faster results. This analysis gave them insight they could then extrapolate to other nodes. The data from this analysis was used in both the network optimization analysis as well as the transportation analysis.

Results

By optimizing transportation alone, Purolator was able to identify savings opportunities by having an optimal distribution network aligned to its strategic direction; and build a strategic network road map for the next three to five years.

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