This is the part 1 of a 4 part series on Fashion Innovation and Optimization.
KEY TOPICS IN THIS SERIES:
- Size & Pack Optimization
- Assortment & Range Planning
- In-season Replenishment
- Allocation Optimization
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In fashion retail, Size and Pack Optimization are key
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.
Understanding how a product will sell through its entire life on a location by location basis – is essential for:
- Meeting sku/store demand: i.e. avoiding missed sales opportunities
- Reducing sku/store over-allocations: which would otherwise be dealt with through markdowns
- Minimizing handling costs: as the inventory makes its way from vendor to warehouse (where applicable) to store
- Reducing Markdowns: by having the appropriate level of inventory and the best assortment possible
The initial assumption of the product assortment is an important part of the process. Retailers need to know what is selling where and why, they need a strategy and goal for why that product is in their assortment and they need to make sure they can continuously re-evaluate how they expect the product to sell – in real time. This enables retailers to understand which stores will offer the greatest potential for full price sales – and appropriately decide what inventory is best and where.
When they can pinpoint the demand at their stores – they will cut distribution costs and decrease lost sales. With the ability to assign specific pack sizes will also help retailers get the exact amount of inventory to every store and reduce markdowns.
Get the right product in the right place and fulfill based on product performance //
The objective is clear: get the right product in the right place to start with – then fulfill based on how products are really performing at each store – giving the product the best chance to sell at full price and identify when and where markdowns are truly necessary.
Size, pack and prepack innovation for progressive retailers
Size Optimization uses historical sales and inventory data at the size/store level to infer historical demand, and then aggregates demand across groups of items and/or locations. Items are grouped according to the size run, attributes of interest, or merchandise classification that they share. This aggregated demand, when normalized across the sizes that compose a size-run, yields a Size Profile. These size profiles can be used pre-season to impact the size buy for the chain, or in-season to impact store-specific size allocations.
Prepack Optimization refers to the pre-determination of prepacks that contain fixed quantities of each size in the size run. Like size profiles, prepacks can be defined for groups of products where the grouping is defined by size run, specific attributes, or a common merchandise classification. Unlike size profiles, prepacks are not store-specific – a given pre-pack can be allocated to several stores, if not the entire chain.
In the most trivial cases, Prepack Optimization can be considered a by-product of Size Optimization. Suppose that we want an n-pack solution, have designated that each store should only receive one type of pack, and have pre-determined the approximate number of units in a pack. Then, we can cluster store-level size profiles into n clusters, and use each cluster size profile to determine the optimal cluster pack by multiplying the size profile by the pre-determined number of units and rounding the resulting size units to the nearest whole number.
However, pack optimization becomes more interesting when each pack in a solution can go to all stores, or when the pack quantity range is broad, thereby requiring optimization of the units in the pack. In these cases, you need more sophisticated approaches to obtaining the optimized packs – approaches that utilize historical store/size demand, allocation quantities, and pack handling costs.
Localizing sizes and packs 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, optimizing routes, re-locating product in the most optimal way, or utilizing vendors that are in a short vicinity of each store.
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.
Ultimately, you can arrive at combinations of packs that work well together to meet store/size demand and minimize handling costs without excessive over-allocation of sizes.
Get back in the game
Are you ready this year to know exactly what your customers are asking for at every location and to have the ability to react as their wants change? If you are looking for a solution that can drive momentum for your business this year, check out the solutions offered by Quantum Retail. Our customers see valuable results in 8 to 12 weeks, and our implementation approach gives your team access to the system from the beginning, so you can manage changes to your processes with ease. Quantum Retail continues to help all of its clients drive positive business value more rapidly than anything seen in retail.
For more information, visit: http://quantumretail.com/services/size-pack-optimization
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The conventional allocation process allows you to select a base of historical data to either use as a base of allocating, or to create a plan to allocate into. The assumption is that the data will reflect the way products behaved in those stores and that the pattern will repeat itself.
1. Optimizing inventory:
