White Paper. Maximizing the value of clearance inventory. Seasonal inventory is hard to manage

Transcription

1 White Paper Maximizing the value of clearance inventory Seasonal inventory is hard to manage

2 Contents A menswear buyer s dilemma... 1 Understanding the trade-off through analytics... 2 In search of an ideal approach... 2 SAS Markdown Optimization: High-impact financial performance... 2 Knowing before doing... 3 Size of the prize: The proof is in the numbers... 3 Conclusion... 3

3 1 If you are a retailer, at some point you will have excess inventory to move in order to make room for new stock. Even the best retailers understand that certain buying decisions made sense when they decided to stock those items, but by the time they arrived, consumers were no longer interested. The challenge when building your scenarios is determining if it is better to focus on sell-through or revenue. This paper describes the inherent trade-off that exists between maximizing inventory sell-through versus maximizing revenue and how advanced analytics supports a more modern solution that balances both objectives. Now you are left with products that are taking up valuable space. Even more troubling, these items are tying up muchneeded cash for buying more (and hopefully more popular) products to fill your stores for the upcoming season. The traditional approach is to put excess inventory on sale at percent off, see what sells in a week or so, then increase the discount to 50 percent, wait a little longer, and finally clear it out at 75 percent off or more. But there have been some advancements with the advent of science-based solutions that help retailers optimize the value of their clearance inventory. These solutions help take the emotion out of markdown decisions and let local shopper demand and inventory levels take the center stage. Traditionally, the primary purpose these solutions have served has been to either maximize the sell-through rate of the inventory or maximize the revenue generation. In the past, the question a retailer asked was: Am I making the most of my investment with either of these strategies? The answer was no. Then you had to analyze the trade-off between the inventory sell-through and achieving your revenue goals to determine the best-balanced solution using rudimentary analytics. This meant manually conducting multiple what-if scenarios based on adjusting rules, sell-through targets, etc. If retailers had to apply this resource-intensive approach across numerous categories and locations, what started out as a science-based optimization process quickly lost its sheen when weighed against the resources required of a manually intensive process. A menswear buyer s dilemma Consider this scenario. Stella is a men s sweaters buyer. She is concerned by the excess sweater inventory for her 500-store chain as the season s end approaches. Her analysts remind her that she needs to have a plan for clearing the sweater inventory before the spring collection arrives. Stella wants to mark down and move enough inventory so that the spring collection can be stocked, but she also wants to get decent margins from the lot. The traditional markdown tool at her disposal will allow her to create markdown recommendations, but will either swing them in the favor of maximizing margin or inventory sell-through. I really need optimized recommendations that allow me to balance the inventory sell-through with the margin opportunities, Stella tells the analysts. She realizes that if she asks her analyst team to begin creating manual what-if scenarios in their existing tool to figure out the right balance, then it will be time-consuming, and she won t be confident in the results. Stella thinks that there has to be a better way when she considers the numerous categories and channels the scenarios will need to span. What Stella and other retailers that deal with seasonal inventory need is a markdown optimization solution that uses a hybrid optimization approach to understand the trade-off between maximizing inventory sell-through and maximizing revenue and margin, enabling her to pick the best options for her specific scenario.

4 2 Understanding the trade-off through analytics As mentioned earlier, maximizing inventory sell-through rate and maximizing revenue in many cases are mutually exclusive objectives, and it is critical to understand the trade-off. In search of an ideal approach The best approach is combining the two objectives (i.e., maximizing inventory sell-through and maximizing revenue), which uncovers insights from the two extreme scenarios depicted in Figure 1 and from optimization points between them (see Figure 2). Maximize Inventory Sell-Through Maximize Revenue Alternative 1 Alternative 2 Figure 2: Possible points of optimization. Figure 1: Sell-through vs. revenue. Figure 1 illustrates the extremes these two options can represent and the need for finding scenarios between these extremes. While these data points are useful and based on solid science, this analysis invariably leads to more questions than answers. SAS Markdown Optimization: High-impact financial performance SAS Markdown Optimization supports this ideal approach through multiobjective optimization. It allows analysts to specify a weighting factor that governs where the emphasis is (inventory sell-through or revenue) and generates results for an array of scenarios in a single run. These scenarios include the extremes, the chosen scenario based on the weighting factor applied by the analyst, and several other relevant options (see Figure 3) from SAS Markdown Optimization. This provides the analyst a more comprehensive view of the possibilities and supports more informed decision making. A retailer whose objective is inventory sell-through might want to know what revenue improvements are possible if the optimization scenario does not prioritize selling the most units it could. Similarly, if the retailer focuses on increasing revenue, it might want to understand if it can improve its inventory position by scaling back on the maximum revenue that can be achieved. Figure 3: Revenue and sell-through by weight.

5 3 Figure 4: Scenario analysis. Knowing before doing Once the analyst and buyer have access to the various scenarios, they can quickly identify the preferred approach. If you are currently optimizing for maximum inventory sellthrough (the base scenario in Figure 4) then significant revenue improvements could be possible by slightly decreasing the inventory sell-through target, as illustrated in Figure 4. Size of the prize: The proof is in the numbers To illustrate the power of the hybrid approach, we tested this concept against a large retailer data set characterized by: 6 million style and color combinations in stores across 15 product divisions. Sell-through target at 97 percent. Plan duration of three to 26 weeks. When the analysts compared the hybrid approach to the maximizing inventory sell-through approach, the revenue increase was $41,026,136 (or 18.5 percent). And although inventory sell-through decreased by 2.8 percent, the value of this additional inventory was $16,715,985, still leading to an impressive net increase in revenue of $24,310,151. Conclusion The ability to visualize the trade-off between inventory and revenue empowers analysts and buyers to understand their options at each end of the spectrum and the possibilities for optimization in between, leading to a more informed decision without manually intensive what-if analyses. The results above show that use of multiobjective optimization, by adding a slight preference for the chosen objective (inventory or revenue), can provide significant overall improvements. SAS Markdown Optimization provides that multiobjective optimization in addition to the more conventional objectives (maximizing revenue and maximize inventory sell-through) to help retailers achieve the best value from their clearance inventory. To find out more about our markdown optimization solution, download the product brief at: sas.com/content/dam/sas/en_us/doc/productbrief/sas-markdown-optimization pdf

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