Getting the right sizes, colors, styles and quantities to the right location:
Local demand changes at every store on a daily basis. Clustering stores together by store size and geography might simplify the process, but is inefficient and does not take into consideration individual store patterns for size, color, style and quantity of local demand and product preference. Retailers need to monitor the changing demand at every store to align their assortment in the way that is most profitable and aligned to their strategic objectives.
It sounds like a no-brainer, but when supply chains become complex, retailers cannot keep up with store level demand and will send the same amounts of every product to similar store types. However, localization of store level assortments and order plans is proven to increase availability, full price sales and customer satisfaction. It is also proven to reduce overall inventory, wastage and markdowns which all erode margin.
Optimizing sizes and rationalizing SKUs:
In order to optimize sizes and rationalize SKUs at a local store level you need an acute awareness of product behavior. There are dozens of product behaviors unique to every store. In order to analyze these behaviors, retailers should optimize by style, color, brand, promotion, price, and seasonality at each store.
The concept of localization works on two levels:
- Retailers can look at the unique behaviors of every product – to determine each stores’ selling patterns for size, color, style, quantity, brand, season, etc. With this understanding, a retailer can plan orders on a store-by-store basis to deliver the right amount of the products that customers are buying at each location, allowing the retailer to achieve the highest turn rates, reduce inventory to the appropriate levels, reduce over stocking and stock outs and ultimately increase margin.
- The second concept of localization comes from localizing distribution and utilizing vendors that produce products in a short vicinity of each store. This type of localization is most easily applied to fresh foods and markets – where customers prefer to support their local farmers and local brands.
Size Optimization Overview:
Size Optimization refers to finding the optimal ratio of sizes to carry for given product in a given store. After segmenting products by Size Run (e.g. XS – XL vs. 2-16) and attributes of interest (e.g. Shape, Color, Fabric), the optimal ratio is found by looking at historic demand, which incorporates actual and lost sales. Size profiles for each group of products are computed at the store level, where enough data exists. A number of Quality Assurance steps are applied to the final output to capture and correct for any exceptions. The client can use the size profiles to both impact the size buy pre-season and the store-level allocation in-season.
Pack Optimization Overview:
Pre-Pack Configuration Optimization refers to finding the optimal configurations and sizes for a combination of packs. Optimality is defined in terms of maximizing an objective function that includes handling costs, lost sales, and markdowns (or wastage). Pack Optimization involves choosing packs such that the increased profit from sales increase and waste reduction more than offsets any increase to handling costs.
Implications of changing pack size:
As the pack size decreases:
- Handling Costs Increase: we are ordering roughly the same quantity as before, but doing so with more packs. Assuming a given cost per pack (typically 30p), we can compute the increased cost.
- Sales Increase: greater sales are achieved by allocating more units to a store where the pack size restriction was previously a barrier.
- Waste (for Grocery customers) decreases: smaller packs will mean fewer over-allocations and less wastage.
Pack optimization for Grocery:
For Grocery, packs typically contain a single sku. This means that the pack configurations are pre-determined (i.e. 100% of 1 sku) and we can focus simply on finding the optimal quantity of each pack. While this greatly reduces the pack optimization problem, Grocery does require us to also consider the impact to waste when changing pack sizes – a consideration missing for fashion customers.
Q – the quickest and most profitable solution for size optimization, SKU rationalization and localization.
The Q system continuously monitors business strategies, profitability, service levels, stock levels and 35 different behavior models for every product in every store. Q is so intelligent that it learns from data like stock outs, lost sales, slow movers, lumpy sellers, packs, sizes, colors and styles. It takes the most recent data for each item and automatically recommends inventory need so that you never miss a sale. Plus, it optimizes the way you phase in a new product and phase out another – ensuring that you are always reaching your optimal performance, sales and service levels, giving you the highest return on the inventory you are buying.
When retailers realize that they cannot optimize sizes and packs unless they have an awareness of store demand, stock outs, and customer behavior at the local level, they will quickly become more profitable and able to compete in today’s retail market.
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