Case Study

Backhaul Optimization Keeps Cost-Reduction Rolling for Outdoor Goods Retailer

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Objective: To reduce supply chain costs without impacting product availability. The focus of this phase was on backhaul optimization for a dedicated fleet of trucks.

Solutions: Supply Chain Guru network optimization and transportation optimization

Results: 

  • Saved $55K in freight costs and $110K in carrying costs from a single pilot vendor project
  • Total cost reduction of roughly 25% of projected costs based on growth models
  • Can guarantee deliveries within a 15-minute window with 99.97% accuracy
  • Improved vendor relations

The Challenge

One of the world’s leading specialty omni-channel retailers of hunting, fishing, camping, shooting sports and related outdoor merchandise had grown by leaps and bounds, opening more than 50 stores over a period of five years. Knowing what the customer wants and having it available in the store is key to sales success. To ensure their stores are kept supplied with the right products at the right time, the retailer maintains four US retail distribution centers, one upstream distribution center in the US, and one in Canada.

For a retail business of this size, logistics is understandably one of the largest overhead expenses. Before this latest growth phase, the majority of the logistics requirements were handled through third-party logistics providers (3PLs). While that made logistics easier to manage, it wasn’t necessarily the most economical choice as the organization grew its retail business.

“In 2011, the Supply Chain group sat down and considered what would happen if we did nothing, and the cost as a percentage of sales continued at the same rate,” said the company’s Retail Supply Chain Director. “What would this look like as we grew the retail business? How much money would we spend? The number was very, very scary.”

The Journey

In 2013, the company laid the foundation for future improvements by implementing a transportation management system that gave them visibility into their logistics network including their 3PLs. Then, in 2014, they took the logistics function back from the 3PLs. That required a considerable investment in the supply chain team, but the organization was confident those costs would be recovered as they implemented the next phase of their logistics optimization project.

In 2015, the team decided the only way to fully optimize the system was to have a dedicated trucking fleet. This meant they had to pay for every mile these trucks traveled, both to the retail stores and back to the distribution centers. However, a dedicated fleet gave them more flexibility in how they delivered to the stores, which for a retailer of seasonal outdoor gear can be a huge advantage. The key to making this work and reducing expenses was to ensure the trucks were filled both ways.

In 2016, the number of backhaul miles from trucks returning to the distribution centers amounted to over 4 million miles from 9,392 loads. “These were no longer the responsibility of the carrier, the 3PL, or anyone else to line up, and if we didn’t put something on that truck, we essentially just paid double for that lane,” said the company’s Retail Supply Chain Director.

These backhaul opportunities could be broken down into two types. Static opportunities were the most predictable and the easiest to plan. In this scenario, the retailer had a vendor along an existing route that shipped to the distribution center on a regular basis. By aligning the schedules, the truck that delivered to that store could swing by the vendor on its return to the distribution center to pick up a load.

Dynamic opportunities were more complex. In this case, the company had a regular supplier along the return route, but the demand for the product was less predictable. The company’s merchandising team might issue a purchase order that week, or they might not.

Working with the team at LLamasoft, the retailer’s supply chain team used LLamasoft Supply Chain Guru transportation optimization to create a model and a process. First, the team exported data from its transportation management system (TMS) and the retail delivery schedule. Next, they cleansed this data into a useable format for the model. Currently, this data is housed in Microsoft Access, but the team is looking at using LLamasoft Data Guru in future phases. They are also considering greater automation of the process by integrating the TMS with the model.

After loading the data into the model, the retailer’s transportation coordinators run two scenarios. First, they use the TMS’ earliest/latest pickup date restrictions to show a more conservative picture of backhaul opportunities. Then they run a second scenario extending the latest pickup date.

As a case study to help them prove the concept internally, the team identified a vendor ideally suited for improvements to product flow. This vendor was located within a minimal out-of-route distance from the backhaul routes for several of the retail stores. However, the retailer’s order cadence with the retailer was irregular. There could be as many as fifteen to twenty pickups required in a week, or there might be none at all. Because of the irregular cadence, much of the volume from this location was transported via one-way trucks.

The Results

The supply chain team partnered with the planning & inventory team to smooth out the product orders so that the return load each week was comparable to spare capacity in the backhaul trucks. This saved $55,000 in freight costs from this vendor alone. As an added benefit, inventory turns increased from 2.43 to 4.37, and they lowered inventory levels from $3.6 million to $2 million for a carrying-cost savings of $110,000 without impacting availability and sales. Finally, because orders were more predictable, the vendor was also able to lower inbound lead time from 14 days to 7 days.

By implementing improvements like these with other vendors and routes, the retailer has seen a steady improvement in savings, from roughly 10 percent of total domestic, inbound costs at the end of 2015 to more than 25 percent by the end of the first quarter of 2017.

Savings and other benefits have been seen in other areas as well. The team can now guarantee deliveries to within a 15-minute window with 99.7 percent accuracy. This allows the retail stores to plan for their unloading/loading labor more effectively with fewer disruptions due to delayed or unexpected deliveries. As a result, the company has been able to redeploy $2 million in retail labor from backroom tasks to customer-facing sales floor activities.

Despite these tremendous gains, the retailer’s supply chain team has even bigger plans in store. According to the Retail Supply Chain Director, “When we launched this phase, we had 90 percent unused space on our backhaul trucks. Now we’re trending around 60 percent. We would like to get this to around 50 percent.”

The supply chain team also believes the tool will strengthen their negotiating position with vendors since these vendors can leverage the predictability of orders to improve operations and potentially lower their supply chain costs as well. With a little vendor flexibility, the company is better able to ensure the right inventory is in stores for the end of the week, typically the retailer’s biggest sales days.

Of course, backhaul optimization and other supply chain projects require intensive collaboration with other internal teams, such as planning & inventory. These teams have their own cadence and make decisions based on their objectives. Showing them how changes will make their lives easier and help them reach their targets is essential to gaining buy-in and convincing them to loosen constraints.

In the future, the team is also looking at combining optimization of transportation with inventory turns. The Retail Supply Chain Director estimates a potential reduction of $160 million in average inventory on hand for a savings of $11.4 million in carrying costs. Showing these dollar figures is essential. “This is a new capability as a company. We’re a two-year-old project going against fifty years of history. We can now show other groups within the organization how we can save them a lot of money and that it’s not just the supply chain function that wins.”

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