Case Study

Global Sporting Goods Retailer Uses Simulation for Supply Chain Quick Wins

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Objective: Lower inventory levels and carrying costs while minimizing impact on service levels.

Solutions: Supply Chain Guru, Data Guru

Results:

  • Lowered inventory levels within two pilot product lines by 7%
  • Decreased operating expense by 4.5% due to lower carrying and handling costs
  • Estimated 1st year ROI for bikes of 284%
  • Estimated 1st year ROI for kayaks of 116%
  • Developed a repeatable simulation model that could be used with other product lines and numerous scenarios such as disaster recovery

Global Sporting Goods Retailer Uses Simulation for Supply Chain Quick Wins

Supply chain optimization can be a game changer for many organizations. However, no matter how great the benefits, change rarely comes easy. A large retail organization used simulation to identify several opportunities for improvement that offered a substantial ROI but would be easy to execute. Starting with these quick wins made it easier to gain commitment from stakeholders across the company.

The Challenge

Operating more than 600 retail stores, the company puts a high value on service and customer experience. In retail, that often means having what the customer wants when they want it. To achieve an over 90 percent service level, merchandising would often order and receive shipments of seasonal products months in advance. Since many of these products are bulky, the associated costs for storage and handling are high. In addition, these investments tie up valuable working capital.

The Journey

Executive buy-in is vital to the success of any supply chain initiative. To avoid starting a project that had limited support, the company’s supply chain managers and the LLamasoft team sat down with company executives to understand their perspectives. Like most executives, they were eager to cut costs, but not at the expense of service levels. The team mapped out a number of potential areas for consideration, comparing the potential impact against the ease of implementation. Two product lines, bikes and kayaks, emerged as the clear winners.

Bikes and kayaks were ordered and arriving at the distribution centers three to six months before the prime selling season for these products. They were bulky products that took up a great deal of storage space. And, while the costs varied, some inventory items were quite expensive and tied up a considerable amount of working capital. Best of all, the retailer’s executive team agreed that, if the simulation showed a cost savings with a minimal impact on service levels for these two products, they would implement the findings.

The team created a simulation model that looked at a number of potential “what if?” scenarios: What would happen to service levels if orders were placed closer to peak demand? What if we broke shipments out into smaller shipments that came in closer to demand? What if we established minimum order quantity policies? And, what if our suppliers implemented store ready packaging so we only had to cross-docked at the distribution centers to lower handling costs?

These scenarios were chosen in keeping with the idea of quick wins. None of these scenarios would be difficult to implement, and they could lower carrying costs and free up substantial capital. The question was whether any trade off in service level would be worth the savings.

Results

Simulation allowed executives to see the impact of their decisions on service levels and costs. For example, as the following chart shows, the closer they ordered product to demand, the more their costs savings went up and their service levels went down. This simulation gave them the insight they needed to create ordering policies with the right balance of costs savings to service levels.

Ultimately, the retailer chose a policy that would allow them to reduce inventory levels by approximately 21 to 25 percent, freeing up capital they could reinvest in building additional stores. In addition, they estimated an approximately 4.5 percent improvement in operating expense from lowered inventory handling and carrying costs.

The team also looked at the ROI of the investment, factoring in costs such as the cost of the software tools, LLamasoft consulting time, client time and the cost to implement the changes. These costs could be recouped by the bike product line within four months for a return on investment of 284% and in kayaks within ten months for a return of 116%. The payback could be even higher in subsequent years since some costs, such as software and consulting, would be either lower or not applicable. In addition, because the simulation was designed to be repeatable, the scenarios could now be applied to every other product categories, particularly other bulky categories such as basketball hoops and tennis tables.

The model also proved useful for looking at the impact of adding additional stores, introducing new products, and changing supplier options. The model will even help the retailer be ready for whatever the future brings by allowing them to analyze the impact of a disaster, such as a flood that wipes out a distribution center, and develop strategies that minimize the impact and speed recovery.

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