Archive for August, 2011

Hardlines Optimization—Part 2: SKU Rationalization

by Quantum Retail • August 24th, 2011 • Write a comment »

Right now is an extremely important time for retailers to optimize their assortments. This process not only can dramatically increase margin and sales, but can also help localize store-level assortments and increase the efficiency of your customer’s shopping experience. When retailers offer too many choices, it can cause headaches for shoppers and supply chains alike, force unnecessary markdowns, and ultimately will take a toll on margin.

However, going through this process can be a bit daunting and takes careful consideration on your part. Determining the proper breadth and depth of your assortment is not rocket science, but it is not something to take lightly. If you cut the wrong products, you could potentially lose some of your loyal customers if you are not careful.

But all retailers can benefit from going through the process to evaluate the performance of their products and stores. SKU reduction will help you create assortments that are easier to manage, more efficient, and more profitable – this means less stock-outs of the products that are kept in the assortment (depth instead of breadth), tighter focus on product performance, and more flexibility in vendor-level considerations like tray size or pack size choices. Additionally, it can offer a better shopping experience for the customer who may otherwise be distracted by the breadth of fringe products, and make it easier for them to come to a decision.

How is this process typically done?

Most retailers have some concept of store grade by merchandise category based on store sales performance or similar criteria. If grades are ranked from highest to lowest (e.g. A through G where A is the highest volume stores), then a product will be ranged to all grades between A and x. The choice of grade x is based on whether the product is core or is just meant to fill out the assortment – in which case it may only go to the top grades. When assessing overall product performance, a product should be removed from the assortment of grades where it is not meeting business expectations. Absent of a store grade concept, the same principal can apply to individual stores where the rate-of-sale of the product in the store can be used to determine whether it should still be assorted to that store.

What are the dangers of SKU Rationalization?

Cutting certain types of items can sometimes change the customer’s perception of your brand. For example, the danger of removing an image item, like a high-end flat-screen TV, could make your shoppers see you as having inferior technology than your competitor. Though the customer most likely will not buy the most high-end item, they want to compare the biggest and best options and feel as though they made their purchase at a place that has a respectable assortment of that item.

Another danger is that if the decision to remove a product is made solely on that product’s performance, you may be losing a product that helps drive the sales of associated products. Worse, you risk losing a key customer to competition and never regaining their business. It is important to know who is buying the products being removed, and what else they buy.

How do I avoid cutting items that top shoppers really want?

Looking at transactional data (what items sold in the same transaction) or loyalty card information (which customers are associated with the sales of those items and what those customers have spent over the last year) are two means of addressing that question.

Retailers may also make choices about which products they plan to cut from their assortments by briefly discontinuing the product’s replenishment. A good assessment of the choice can be made when a planner looks at how quickly the product stocked out and if any associated product sales slumped in the process. After this analysis, it should be fairly obvious whether or not the item should stay or be removed. Similar tests can be performed in a grouping of stores. Item performance can be analyzed in those stores and similar decisions can be made for like stores, especially those with similar item level performance and demographics.

When is a good time to rationalize SKUs?

For retailer’s that have a concept of season and have items brought in for each season, SKU rationalization should be done as part of pre-season planning. For long-living items, assortment decisions would be made at the start of the item’s life that would then be tweaked after the item starts selling (but the bulk of the decision would have been made upfront).

Which inventory should retailers focus on reducing?

SKU rationalization in many cases is more effective with longer-living merchandise because you can track an item’s progress and make reasonable adjustments. With trendy items, for example, it is more difficult (but not impossible) to base next season’s SKU rationalization on the previous season when the previous season may have been impacted by the performance of particular trends, patterns, or colors (i.e. cases for computers or cell phones).

When determining whether to add or remove SKUs to an assortment, retailers should look at three major factors:

  1. The relative value of each SKU in the assortment
  2. The GMROI of the store itself (or cluster)
  3. The local demand of each store – what shoppers are buying

The reductions or additions should be made in periodic intervals, perhaps weekly. This decision will look at these three factors and assess whether a planner should add one item to this cluster, remove two from another. It’s not a once-a-year, twice-a-year process – it’s constant. This is a big deal. Going through this process on a continuous basis will give visibility to product performance and the success of a reduced assortment.

Where to begin

Your main question: What to send to which store for what reason?

The Top 3 things to consider when beginning the rationalization process:

  1. The direct impact the SKU will have on the store’s performance through its sales contribution
  2. The indirect impact the SKU will have (through halo/cannibalization, i.e. cross-item effects)
  3. The hard-to-measure “image impact” – beyond actual dollars generated by the item or associated items, does the existence of the item in the store impact your customer’s perception of your store

What you should consider when looking for new capabilities

It is important to look for tools that will help you assess the profitability and success of each item at all of your stores. When retailers have a tool that can constantly and automatically monitor the success of their products and make recommendations on the breadth and depth of the assortment at each location, they will make the most of their time and quickly increase margin.

There are new technologies available today that can simplify this process and make it ongoing by creating a strategy for these attributes and applying it to all categories and stores.

In the complex task of SKU rationalization, planners and buyers need the assistance of smart technology that can give visibility to the performance of every product at every store. This kind of technology can quickly pay for itself as it optimizes your offering, reduces inventory, and increases sales.

What to look for in assortment rationalization technology:

  1. A system that continuously monitors business strategies, customer strategies, profitability, service levels, and stock levels
  2. Technology that utilizes the data it takes in to recommend the most profitable assortment for each store across time while constantly taking customer demand into account
  3. The ability to optimize SKU rationalization by recommending like-product attributes for new products
  4. The ability to take in real-time data and automatically recommend inventory need based on local consumer behavior and store performance
  5. The ability to take in real-time sales data to make store cluster (grades) changes

Most software products focused on assortment give retailers the tools to assess item performance and to make removing or adding decisions. Quantum is going a step further by suggesting, by category/store, where ranges should be increased or decreased. The software will then quantify the specific assortment change recommended by suggesting how many items should be dropped or added to determine the final cluster assignment. The planner can then see the impact (a what-if) to sales/profitability/etc when the SKU rationalization is changed. This gives retailers the tools to make intelligent decisions regarding the rationalization – while still leaving the choice in the retailer’s hands.

When retailers optimize their product range based on local store demand, stock outs, and customer behavior, they will quickly become more profitable and better able to compete in today’s retail market.

Look out for next week’s blog on understanding long term forecasting and seasonal profiles.

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Echelon Magazine: Vicki Raport Profile

by Quantum Retail • August 17th, 2011 • Write a comment »

“One of the great benefits of being an entrepreneur is the ability to establish a culture for your organization that represents your values. In Quantum Retail, this translates to a diverse and inclusive culture where people of different ethnic, geographic, religious, and lifestyle preferences work together in a collaborative way for the benefit of the company, our clients, and each other.”

To read more, see the digital edition HERE»

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Hardlines Optimization—Part 1: Clustering is Not Localization

by Quantum Retail • August 17th, 2011 • Write a comment »

There was a time when it was enough to have individual store managers make the decisions of which products to carry and how much to order. As hardlines retailers have grown, their offerings have become more elaborate and competition has become more sophisticated. Customers and their behavior have become more complex as well. It’s no longer possible for people to keep track of all the variations and trends making each location unique.

For the last two or more decades, conventional wisdom has been that clustering stores is the way to address this problem. While, conceptually, grouping stores with similar characteristics makes sense – the reality is that it’s very difficult if not impossible to do this in a consistent and effective manner. From volume clusters to attribute clusters and even more recently devised mechanisms for deriving “intelligent” clusters using BI tools, all suffer from flaws that keep them from achieving the elusive goal of localization. Let’s look at some of these to understand why that is.

Inherent problems with volume based clusters:

The most widely used of these decades-old methods to cluster stores is volume clustering. Typically using historical sales for a group of merchandise presumed to be similar then finding apparently logical breaks as that volume is ranked high to low. The breaks represent boundaries which define which cluster stores fall into. Most commonly the result is a set of 10 or fewer cluster groups (although we’ve witnessed organizations that do many more). The three most common flaws in this process are:

  1. The clusters are virtually always based on historical sales
  2. The clusters are not updated frequently enough
  3. The clusters don’t incorporate any attributes of the merchandise or the stores

Typically a retailer looks at a store’s performance over the last year or compares like for like seasons to group stores that performed similarly together. The problem is that usually its only historical sales that are used to group the stores together. This creates a bit of a self-fulfilling prophecy. If a store underperformed in a department because of an inordinate amount of out-of-stocks, could the store have performed better?

The better option is to use either historical demand, or better yet, a demand forecast. When we refer to demand we look for what would have happened had the store been in stock. This allows us to understand any missed opportunity that may have resulted from out of stock situations. Of course using a true, forward looking demand forecast which includes understanding those missed historical opportunities to cluster would be the best of both worlds. Not only are you incorporating lost sales, but you would be incorporating future trends of store behavior as well.

Another problem is timing of when clusters are used and how often they are evaluated. Store assignment to clusters should be evaluated as often as possible. Customers change their shopping behavior constantly and this changes the behavior of individual stores constantly. As a result, store clusters need to be re-evaluated as frequently as possible using the most current understanding of behavior. At a minimum cluster should be updated prior to each merchandising activity that is consuming them. For example, updates should occur pre-season when an assortment plan is created (which products in which locations), again when a buy plan is made (soft commitments for how much), again when the actual order is placed (time has passed, things have changed), again when commitments to DC shipping locations (DC splits) are confirmed, again if the buy is pre-distributed to commit stock to stores, another when the goods hit the DC, and so on.

Furthermore, clusters should never be used to drive replenishment decisions. Once the goods have hit the stores, any re-allocation/replenishment activities should be based on actual, individual item/store behavior. There is simply no good reason to use clusters after the first allocation – and even then a sound argument can be made as to whether they should be used at all. This is especially true for hardlines and commodity products that don’t have the volatility or size complexity seen in hardlines. Today’s technology is capable of identifying and leveraging the uniqueness of individual products in individual stores, so each store can be treated as it needs to be. This is where true localization can truly begin to provide huge benefits.

Clustering beyond volume

Retailers use more than just volume clusters but as of yet no standards have been adopted. Climate, an attribute of physical location, is one of the most commonly seen attribute clustering criteria. While useful, categorizing a location as one of three or four climate groupings is an inexact science at best. And there are surely many other attributes of location and product that can refine the quality of the result. In traditional execution, however, more attributes leads to more groups needing to be managed. These groups are often nested into artificial hierarchies and quickly become difficult to navigate and virtually useless when complexity reaches the point that execution is impossible.

More recently many have tried to utilize Business Intelligence solutions and statistical analysis to find more refined groupings of stores that behave similarly. One of the most common flaws of this approach is that the resulting clusters have no definition of what makes them similar. While it may be accurate that they have similar behavior, that conclusion alone is useless unless a merchant understands what makes a group unique. Without that there is nothing available to guide a cluster specific decision.

Where does that leave us?

Ultimately, newer technologies that have been focused on solving these problems in retail have refined the utilization of clusters significantly. Better solutions constantly review and update clusters based on current behavior and the processes that consume clusters are able to accept and modify their conclusions accordingly and without unnecessary user intervention. They analyze and update activity across a variety of attributes proven to impact the products and locations within the grouping and offer flexibility to navigate across those without being tied to unmanageable hierarchical relationships. They also derive learning about individual products to the point that the need for clusters is either significantly reduced or eliminated in many processes throughout the management of inventory (such as replenishment). If your processes are not supporting that level of locating management and practical clustering, it’s simply not possible to achieve what is expected when discussing localization in retail.

Consider the following as you think about the quality of your clustering practices:

  1. Do you cluster only on volume or on other attributes or KPIs too?
  2. Do you cluster only on historical sales or do you incorporate missed opportunities / lost sales?
  3. Do you cluster based on a forecast of demand for the periods you’re using the clusters to represent?
  4. Do you re-evaluate your cluster assignments as often as possible?
  5. Do you re-cluster in-season?
  6. Do you cluster the stores as low on the merchandise hierarchy as is reasonable for your business?
  7. If you change cluster definitions can the systems that use them accept that change cleanly?
  8. Do you drive re-allocations or replenishment by clusters?

If the answer to any of the above questions is “no”, there is room for some self-evaluation and improvement to your localization strategy.

Look out for next week’s blog on SKU Rationalization.

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Fashion Forward—Part 7: Advanced Multi-Level Distribution for Retail

by Quantum Retail • August 4th, 2011 • Write a comment »

How to capture the most profit for your inventory investment

In fast fashion, speed is key not only in the changeover of your assortment, but in the logistics that manage your supply chain. When you are dealing with 4-8 weeks of supply in a fast fashion assortment it may seem that there is little time to do anything but allocate your stores and markdown the excess, but it is likely that you are missing sales in the process.

What it takes to turn those losses into profit is a smart holdback strategy, real-time demand awareness, and a multi-level distribution method.

For many fashion retailers, it is common practice to use distribution centers simply as a flow-through point as they allocate their stores. But by pushing all of your inventory out to stores in a one-shot allocation, you lose any chances of utilizing real-time trends from your stores to place the rest of your stock where it has the most potential.

Even if you think you have the best forecasts and order plans, nothing compares to true sales insight. If you can instead, allocate 60-80% of initial supply to cover the minimum presentation quantity, you can learn which products are selling best in which stores so you can easily re-assess your order quantities for the rest of the assortment’s life and then push the rest of the merchandise to the stores where it will make the most profit.

Retailers that can turn their distribution centers (DC’s) into a holding location for real-time replenishment will increase their profit, inventory efficiency and service levels. To take it a step further, retailers can even break down packs at the DC to smaller packs or eaches in order to ship the most optimal quantity based on profitability at every location.

An alternate approach at holding back stock is to break the order into two allocations and receive them a few weeks apart. This can be an effective alternative to retailers who have constraints about holding merchandise in their DC’s.

To make the most of your hold back process, it pays to have multi-tiered distribution centers, ranging from national (NDC) to regional (RDC) and to allow replenishment from multiple levels. By allowing replenishment from multiple levels and utilizing routing logistics to analyze the cost comparison between each level and the store, you can assess if it is more cost effective to replenish from your RDC, your NDC, direct from the vendor, direct from a franchisee or direct from another store.

Challenges for retailers

Speed: The speed of turning demand forecasting into execution is the biggest challenge for retailers. Distribution centers, buyers, and business analysts must have the right tools to act and execute on real-time demand in order to keep up in today’s cutthroat retail environment.

Data: In order to make the replenishment process faster, retailers need better data. Data needs to be real-time and a retailer’s processes need to be granular enough that they can understand the need for every item and every store, not just at the cluster level. The quality of the data is also crucial. Retailers need to be sure that they are receiving data that is trustworthy of true demand. Understanding the need at the lowest level and working that demand insight from the bottom up through the supply chain are key to optimizing supply chain performance.

Distribution center constraints: Some retailer’s distribution centers do not have the capacity to hold inventory, or the logistic power to manage real-time replenishment. But enhancing these management processes and increasing the capacity for short-life goods as well as longer life stock will ultimately pay off through full-price sales and customer service increases.

Allocation and replenishment processes: Many retailers do not find it feasible to utilize a process of holding back stock for shorter life products and end up cutting corners by doing a one-shot allocation. It will be worthwhile to do a feasibility assessment to see where you could be saving money in your distribution, allocation and replenishment processes.

Advanced Multi-Level Distribution (MLD) and Order Planning/Warehouse Replenishment Technology

These ideas seem great in theory, but without the right tools and capabilities in your supply chain, these theories may be far-fetched. One of the only tools on the market that has the intelligence to replenish in real-time through the process of multi-level distribution is Quantum’s solution, Q.

Q multi-level distribution works in conjunction with the order planning/warehouse replenishment and ordering. Q order planning and ordering utilize demand side information to request inventory at different levels of the supply chain. Q distribution takes the inventory actually available in the supply chain and moves it through the supply chain based on demand and profitability.

Allocation and Replenishment

Typically, the distribution of inventory is grouped into allocation or replenishment. The former is a top-down mechanism of looking at like item historical data to send inventory to stores for new items, or those with a very short life and limited inventory. Allocation uses rules to balance inventory across the chain when given limited goods. Replenishment is a demand based requesting of inventory, assuming an unlimited supply of goods.

The actual world sits in the middle of these two. Rarely does the supply match the demand, but demand is often available to make better decisions about where to send inventory.

  • Short-lived items can still use customer demand to request inventory
  • Long-lived items have inventory shortages
  • Items are often bought in inner packs, cases, or multi-packs, so the ideal amount cannot be requested.

MLD Functionality

Q distribution utilizes the demand side information with allocation principles of balancing inventory to distribute each incremental unit of inventory based on where it is needed the most to support inventory strategies.

Q distribution principles can be used to:

  • Request inventory where item packaging or vendor constraints do not allow for the requesting of single items.
  • Allocate inventory that is cross-docked. Even when inventory may have been requested at store level and then aggregated, at the point it reaches the DC the optimal inventory need has changed, and can be re-allocated.
  • Request inventory from a pool taking into account the desired lifespan of the product.
  • Place inventory to best meet the strategies of the individual product/locations.
  • Protect Inventory for a group of locations, or ensure that all location groups are given equal treatment.
  • Restrict allocation to individual locations to best manage supply shortages.
  • Push out inventory to locations to maximize sales from surplus.

Make more profit with smarter strategies

When you take the leap to optimize your supply chain, ordering, planning, allocation, replenishment and distribution processes, it will incrementally increase your efficiency and result in lasting profit gains for your company. Utilize today’s technology and become an industry leader.

That concludes our Fashion Forward series. Look out for the next series on Optimizing Hard Lines.

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Read more on Strategies for Hold Back Stock HERE»

Read more on Strategies for Linking Your Data Across Channels HERE»

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