Specific changes in this new version include:

• Multi supply chain support gives flexibility in order planning and distribution for retailers with complex supply networks and methods, such as Vendor to National Distribution Center (DC), Vendor to Regional DC, National DC to Regional DC, etc. to move stock as quickly and efficiently as possible, reducing the risk of missing a sale due to unplanned circumstances. Q now supports direct to store orders and allows users to view order quantities by location in order to get the right quantity to every local store as soon as it is needed.

• eCommerce integration enables retailers to easily manage and integrate eCommerce inventory, warehouse or vendor availability and distribution alongside physical store locations. This permits retailers to maintain availability, so that high demand products do not go out of stock either in-store or online.

• Enhanced order planning and forecasting allow planners to forecast and manage short life products at the day level while users can also change to a week view and manage forecasts and order plans for 18 months out for longer life products. Planners can also test “what-if” scenarios, with the ability to change quantities as late as time of receipt based on the most up to date demand data. This means retailers are able to easily and accurately manage the real-time demand for their inventory all the way down to the local, individual store level with the Q system.

“We took extensive feedback from customers into account when implementing the latest changes to Q,” stated Morgan Day, CTO of Quantum Retail. “This latest release incorporates some important improvements to an already highly robust software offering and we will continue to improve Q to ensure our customers have the benefit of utilizing the most advanced merchandise optimization system available.”

About Quantum Retail Technology, Inc.

Quantum Retail answers the new questions facing retailers with a merchandise optimization suite designed for the increasing pace and complexity of the consumer revolution and today’s competitive landscape. Quantum Retail’s award winning solution, Q, solves the most difficult and costly problems retailers face – quickly and permanently.

The Q solution is the new answer for: Forecasting and Order Planning — Replenishment and Allocation — Assortment and Range Planning.

Read more about Quantum Retail»

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WEEK 3

I was recently working with a major retailer who expressed that they had so many forecasts available to them that it was hard to know which one to use. There is a forecast for marketing, for the catalog, for the website, one for the replenishment of goods at a low level, one for financial merchandise planning at a high level of merchandise, one for the distribution center, and the list continued. Which one do we use for planning? It was almost enough for them to throw their hands up and just base their plan on last year. I laughed and said that if they did that they would be on par with almost every other retailer out there.

Sadly, my experience has shown that to be true. While forecasting has, to a certain extent permeated the realm of higher level merchandise financial planning it has yet to make a real beachhead in assortment planning. I would argue that there is a lot of opportunity to be gained if the forecast is incorporated into the assortment planning process for determining store assortment breadth, depth, and whether or not items will be carried at all.

Assortment Review

Finding the balance between the benefit of utilizing a forecast in assortment planning or not partly depends on what you are forecasting. When the assortment plan is synonymous with an assortment review process or category review the benefits definitely align with utilizing a forecast. An Assortment review process is most typically used in long life items. Whether they be hardlines merchandise or long life softlines merchandise, such as jeans, the forecast can predict performance of an item with a high degree of accuracy. Traditional forecasting systems require a great deal of history to provide a forecast that has a confidence level that is high enough to be worthwhile to incorporate into the process. Items that have long life, often referred to as replenished items, typically have a confidence level that is high enough. So, the results of a demand forecast, which is a forecast that incorporates lost sales and available inventory, can be utilized by the planner to determine which items should be kept, which items should be deleted, and which items should be added or removed from a specific cluster. Typically this process is completed using only historical performance. However, trends that may not be perceptible when looking at historical performance can be seen in a forecast.

Determining the breadth of the assortment to a specific store, or cluster of stores can also be enhanced by forecasting. By using a forecast to best match a product with clusters that are most likely to sell the item profitably, it is possible to reduce overstocks and prevent markdowns.

Forecasting for fashion

Nobody would tell you that it’s easy to forecast for fashion or any short shelf life product such as cell phones or DVDs. Why is it so difficult to forecast fashion? There are a number of reasons, but the primary issue is short life. Traditional forecasting systems need long periods of historical activity to identify selling trends and begin producing results they have confidence in. Add the complexity of sized merchandise and the data is much too granular to draw SKU / store level conclusions from. Many have come up with complex algorithms, constraints and rules that attempt to address this issue. So retailers have adopted an alternative approach: consolidation. By consolidating the histories of many products that have similarities to the current product, we feel confident that the current product will behave as its predecessors have. For example, when allocating a new product to stores, it’s common to use a base data set of the product’s class, or alternatively, choose a “like item”. This of course is simply a surrogate to address the limitations of forecasting and store replenishment. Since the products don’t live long, we supplement our need for more historical selling time by applying our knowledge of similar products or product groups to give us more data. This allows us to begin seeing selling patterns. We then apply calculations that interpret the relationships in this base of data to derive a calculated recommendation.

These calculations are simpler than forecasting routines, but together with the additional merchandise that makes up the base of data, they are much less volatile and therefore return reasonably stable results. We review this result and change it based on other dimensions of data we analyze, assumptions and intuition. Having said that, there are forecasting systems that have been able to aggregate similarities in products, such as attributes, price points, or fashionability to give a semblance of accuracy to a forecast.

Tracking Life Cycles

Recently, a few companies have had success applying forecasting to fashion allocation. They have done this by combining advancements in technology with innovation in retail science to understand the relationships of behavior across many different products, store types, and levels. Two of these relationships that have shown some promise are lifecycle and strategies. Tracking the lifecycle of an item at a store level to see how that store behaves with a new product that has a short life has shown to be an excellent indicator of future item behavior. A typical product introduction has a curve to it over time that shows how quickly a new product takes off and how long it produces positive results. Mapping that behavior by store to new items gives a solid indication of how a similar new item will perform in the same location.

Product Strategies

With the knowledge of life cycles, product strategies and price points will give the forecast lots of historical data points. Another helpful tactic is to create product strategies. An item’s strategy is defined by how the product is expected to behave or by assessing why the item is in the assortment. Traffic drivers, loss leaders, fringe items and core items are all terms that are typically used to describe an item’s strategies.   The combination of strategies and lifecycles starts to give us a preview of an item’s behavior by store once it is introduced. These can be used to help a planner determine where certain items will perform well in order to determine which clusters are best to receive the item.

Technology to simplify the complexity

With automated inventory management systems, the complex execution can be simplified. Since these systems also understand what you as an allocator are trying to achieve, they can execute to that automatically. Only when they cannot do what you’ve asked of them does the allocator need to intervene. Even then, issues are addressed using business logic rather than trying to manage complicated calculations, statistics or controls.

The same process can be applied to any new item, whether short life or long. By using a culmination of information similar to that product, a new product can be forecasted with enough accuracy that a planner can have a good recommendation as to where that product should be carried. For example, by knowing how fashion-forward an item is, the item’s color, price point, and attributes, such as sleeve length, the forecast can use a consolidation of similar items to forecast how that item will perform in a given store based on that store’s historical performance metrics. If we spend more time finding the data that most closely reflects the trending, lifecycle, seasonality and historical demand of the item we’re allocating, results ultimately improve. Once these metrics are known, a planner can determine if the item will positively impact sales or profit enough to carry it in the store.

Forecasting for localization

The benefits to localization are rarely disputed. All retailers to a matter of degree are attempting to place the optimal assortment in each store based on that store’s propensity to sell. By looking at history alone for a given store the localization process is simply not going to be optimized. In an earlier installment to this topic I wrote about the need for clusters to continually adjust to the behavior of the stores. Stores should not be locked into a particular cluster for an entire season/year but should shift as plans become actuals. Additionally, SKU rationalization or optimization, depending on your definition, needs to be a part of the localization process. As stores behaviors change, items need to be added or removed from the assortment in order to optimize the stores performance.

Forecasting should also be part of the localization process, although not as blatantly as dynamic clustering or SKU Rationalization. Rationalizing of the SKUs should be based, in part, on the forecast of the SKU / store rather than solely based on history. A stores assignment to a cluster should also utilize a forecast to cluster the stores given their expected behavior in the near term. As a caveat, this only works if you are re-clustering the stores on a weekly or monthly basis. Any further out than that and I would not trust the forecast’s accuracy.

Forecasting for depth

The hard part in using forecasting is attempting to determine whether or not to add an item to the assortment and deciding what stores the item will be ranged to. The much easier portion of the assorting process is in determining how many of the items to hold in the store in order to capture expected demand.  A forecast can help determine the depth of the assortment and arguably have a greater impact to the performance of that assortment than helping to determine the breadth. By clustering stores together based on a forecast, the stores that are likely to perform similarly are going to be grouped. Presentation quantity is, of course, a consideration of the depth of the assortment. Typically the planner has the ability to determine how much product goes into the store and does so by the store volume cluster. Using the reliable wedge, the planner will typically put more in the larger volume stores than the smaller ones.  However, if the forecast becomes more reliable, the amount of product that initially goes to the store can be refined to a more granular level so as to avoid over or understocks early in the product’s lifecycle. A good allocation or replenishment should be able to take care of it from there.

In Summary

It’s easy to argue that the forecasts at the SKU/Store level are too inaccurate to be of any use to the assortment planning process, but with some new thinking of how to forecast, significant value can be gained.

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“The leaders of the companies on this year’s Inc. 5000 have figured out how to grow their businesses during the longest recession since the Great Depression,” said Inc. president Bob LaPointe. “The 2010 Inc. 5000 showcases a particularly hardy group of entrepreneurs.”

Quantum Retail attributes its success to a revolutionary new approach to retail merchandising challenges. The company has created a dynamic solution, called Q, that optimizes and automates retail processes related to forecasting and advanced order planning, replenishment and allocation, and assortment and range planning. Through a deep understanding of item behavior and merchandise roles, goals, and strategies, Q is driving unprecedented value for retailers of all types.

Quantum Retail’s Q software enables retailers to make strategic decisions for every product at every store, quickly delivering exceptional return on investment for their customers.

View the award on inc.com here » http://www.inc.com/inc5000/profile/quantum-retail

About Quantum Retail Technology, Inc.

Quantum Retail answers the new questions facing retailers with a merchandise optimization suite designed for the increasing pace and complexity of the consumer revolution and today’s competitive landscape. Quantum Retail’s award winning solutions solve the most difficult and costly problems retailers face — quickly and permanently.

The Q solution is the new answer for: Forecasting and Order Planning — Replenishment and Allocation — Assortment and Range Planning.

Read more about Quantum Retail»

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WEEK 2

Years ago the store owner knew his customers by name. He could pull their goods in advance of them coming in to the store. If the customer wanted something that the store owner didn’t carry, the customer could request a specific item to be added to his assortment and he could choose whether it would be worth it or not. As store chains developed, non-centralized planning and merchandising allowed the store manager to keep his finger on the pulse of his customers. What were they asking for? What did they like? What did they not like?

Today is a much different picture. These same chains have expanded their store counts by hundreds, if not thousands, and now rely on buyers and planners that sit in headquarters trying to determine how to localize the assortments to maximize the potential revenue and margin that each individual store has the ability to provide. How can today’s merchant personalize and localize an assortment the way the store owner or store manager would have done when they were responsible for just one store? The obvious answer would be to assort each store independently, but that just isn’t realistic. There are not enough people to do that. The answer lies in clustering.

The Beginnings of Clustering

Clustering started decades ago as chains began reaching the high double digits in store count and merchandising became more centralized. Back then, everybody did it the same way. Stores were ranked in terms of sales and grouped, usually by percent of average. The “A” stores may be those stores that perform at 200% of the “average store.” Of course, there was no “average store,” but it was the total sales divided by the number of stores that represented the average. “B” stores could be 160% to 199% the average store, and so on. The number of clusters were somewhat a semblance of how many stores were being managed, but also the number of clusters a buyer or planner could manage was a factor as well.  The more clusters there were, the more precise the assortment could be, but the more difficult it was to merchandise. Trade-offs were common. This was the beginning of clustering.

Merchandise Hierarchies

Next the merchants started to group the stores by merchandise hierarchies. Categories, departments, and classes now were getting their own clusters of stores, a logical transition. An “A” store could be a fantastic store in women’s career apparel, but terrible in men’s accessories. This allowed merchants to be increasingly specific in building assortments that would perform better in certain stores.

This is about where your typical retailer is today. A majority of retailers dissect their stores into volume (sales) based clusters in this manner at a merchandise hierarchy level. That merchandise hierarchy varies, but it’s typically at the level that planners are building an assortment plan, most likely to be class. While a majority of retailers are at this point, a few have successfully moved beyond this stage and made a variety of improvements.

Nested Clusters

Some clusters are nested, building clusters not just on sales volume, but also on a variety of store attributes. Climate is probably the most common and most logical. This has a big impact on a variety of categories. Outerwear will sell better (and earlier) in Minneapolis than in Miami. Store size is another somewhat common attribute that merchants use to cluster as is demographic information such as race, religion (for some classes heavily influenced by holidays), or income. All of these make sense, but they are far from being universally adopted.

Statistical Clustering

A mathematician would tell you that what I have previously referred to in this article as clustering is actually “grouping of stores.” Pre-determining both the break points as well as the number of groups doesn’t allow stores to truly “cluster” together, but instead to simply “group.” By applying statistical methods to clustering, stores that are truly more alike will end up in the same cluster. The number of clusters becomes statistically relevant as well, and not something as simple as 26 clusters because that’s how many letters there are in the alphabet. You laugh, but I’ve seen it more than once in my career.

An Evolving Process

So, the evolution has begun, clusters are now really clusters, as opposed to groups. Stores are being clustered together based on more options than sales volume alone and being clustered with statistical accuracy. Consideration for demographics or store attributes such as climate are now commonplace.  However, there is a big piece missing that I haven’t hit on yet.

Three major problems of clustering

While there might be exceptions out there the vast majority of clustering has three major problems associated with the process. These three issues are seriously inhibiting the retailer from truly localizing their stores.

1. Clusters of stores are almost always based on historical performance.
2. Clusters are typically locked in for a season or similar time period. If the recent economic climate has taught us anything, it is that store behavior changes and it changes rapidly, especially at the merchandise levels.
3. Clusters are hindered by store attributes. Significant value can be gained if stores were clustered based on merchandise attributes.

Plan for Future Demand

Clustering stores based off history is a mistake that almost every retailer makes. I understand why, the typical merchandiser does not have much of a choice. History is the only thing that they have at their fingertips on which to cluster. But, this means of clustering misses the quite obvious fact that stores performance last year will not equate to store performance this year. That’s why history is not the best base for clustering stores together.  A consideration of expected future behavior must be made. Clustering on a trend or, better yet, a forecast at the store/merchandise level is a better way to cluster the stores.

Stores are Dynamic

The second issue mentioned is that store clusters are typically locked in for a season or more.  An individual store that performed as an A cluster last year during the Spring season in Womens Tops will be clustered again as an A store for the entire Spring season this year. However, as often as not, that store will not repeat the same performance year over year especially in every department. Stores need to be able to move within a cluster to more closely align their actual performance with merchandise levels.  If stores don’t move with their performance, they aren’t being localized. Stores will underperform and be left with merchandise to markdown or overperform and stock out. If, however, stores actual current performance dictates the cluster and thereby their merchandise levels, these things are less likely to happen and the store is being localized more effectively. By doing that, we are introducing continuous small amounts of change into the way that products are being assorted into stores, which in itself is more manageable and timely in reacting to the way that customers are really acting in the stores. That’s an incredibly powerful piece of the puzzle.

The best way for stores to be localized given that it is impractical to expect an assortment per store is by having dynamic clusters. The assortment planning process should include a periodic, typically weekly, review of each store’s performance versus its cluster and make a recommendation to move that store to a different cluster based on a variety of criteria. This allows merchants to fine tune the assortment that will perform best in a store given the store’s behavior this season, not last year.

Don’t Forget the Merchandise!

The last issue that I have called out is clustering solely on store attributes. There is clearly value in merchandising based on some store attributes. Climate is the best and most obvious example as this not only affects the breadth and depth of the assortment, but also the flow of the merchandise. I remind you of my earlier Miami and Minneapolis example in outerwear. You’re not only going to have more choices in jackets in Minnesota, but you’ll have more inventory as well as an earlier flow of merchandise. However, clustering solely on store attributes missed a significant opportunity for store localization based on how merchandise attributes collectively perform at an individual store.

An example of this can be found in price point. If you cluster a class of merchandise based on the price tier (“good, better, best” is common representation of this), the stores that perform better with higher priced merchandise will be grouped together. I would argue that this is even more accurate than grouping the stores based on demographics such as income level. Just because a store is in a nicer neighborhood does not mean that higher priced merchandise will sell better in that store. Honestly, if the retailer creates clusters with stores that actually perform better in the type of merchandise, the demographic information hardly matters!

In Summary

Today, nobody expects every store to receive its own assortment plan. Every store, however, can receive its own localized, unique assortment even when clusters are being utilized.

Recap:

1. Cluster on more than just volume and history, by incorporating attributes of not only stores but of the merchandise.
2. Constantly update the store cluster assignments based on actual store behavior.
3. Create localized clusters based on how merchandise attributes collectively perform at an individual store.

By following these guidelines, a merchant can have a positive impact on their chain’s performance and will be able to create localized plans for the individual stores.

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Assortment planning is one of the first areas retailers should assess in order to increase profit and margin. I will be taking you through the top four strategies to optimize assortment planning including: SKU rationalization, clustering, forecasting and financial plans.

- Matt Garvis
Director of Company Strategy, Quantum Retail

WEEK 1

SKU Rationalization: Determining the proper depth & breadth of an assortment //

Right now is an extremely important time for retailers to optimize their assortments. This process can not only 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 fringe products and the additional breadth allotted in the same space and make her more likely to find her preferred color in her size.

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?

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 fast fashion, 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 styles.

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 planning and SKU 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

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 able to compete in today’s retail market.

Next post in series >>

Learn more

To learn more about assortment planning, be sure to check back weekly, or sign up below to receive email notices when the blog is published.

Subscribe to receive weekly updates of this series HERE»

For resources on assortment planning, visit: http://quantumretail.com/solutions/assortment-range-planning/resources/

We’ve covered 10 different strategies to consider in the process of executing allocations. Most existing environments will find some of these to be easy to adopt while others will be more challenging. But what can you expect as benefit for making the investment? Is allocation really an area worth investing this additional time in?

To answer these questions it’s a good practice to get a high level view of what is impacted. In the introduction to this series, I wrote about the fact that there are many more decisions in the process of allocation than there are in the other merchandising related activities. To put this in perspective, let’s compare the three major components of merchandising. We’ll use a retailer with a range of fashion and basic merchandise offerings having 3 distribution centers (DCs) and 500 stores as an example. Different environments see the following activities in different ways, but for the purpose of this example I’ve broken them into assorting, ordering and allocating as defined below.

The numbers game

Assortment planning (10 decisions) – Defined for purposes of this discussion as determining what products to buy, we generally have one major objective. That is to determine what products to buy or not buy. If we include decisions around ranging (what stores get the products we select) then we also make this choice for stores. In virtually all fashion environments, stores are combined into clusters / volumes or some similar groupings. If we assume 10 of these groups then we’re making 10 ‘include or exclude’ decisions per product.

Ordering (12 decisions) – Defined as determining how many of the items selected in assortment planning should be shipped to a warehouse or DC. Here we’re making the same number of decisions as we have DCs. This is multiplied by the number of receipts we plan. In an environment with 1/3 of product being one shot, 1/3 being 2 shots and 1/3 being ongoing basics we may have an average of say 4 receipts per product. If we have 3 DCs that’s a dozen decisions per product (3 * 4 = 12).

Allocation (2,000 decisions) – Defined as determining how much available inventory goes to each store. Here we also have decisions to make for each receipt. If we use the average of 4 receipts from above we need to make a store specific choice for each store for each of those receipts. In a chain with 500 stores we’re now talking about 2,000 decisions (500 * 4 = 2,000). In the case of direct to store ordering, generally allocation is combining the ordering and allocation steps.

Using the above logic, there are clearly many more decisions in the process of allocation than in ordering and assorting. Obviously there are multiple dimensions of things to consider for each activity, but ultimately allocation has more instances for good decisions to be helpful, or perhaps more importantly, for bad decisions to be detrimental.

Which comes first

So if you’re in an environment where you need help in all three of these areas, what then? Which should you focus on first? Well each situation is unique and these choices are dependent on your current capability and proficiency. Generally there are two reasons why it makes sense in most situations to focus on allocation first.

The first reason is explained in the numbers above. More chances to improve the quality of the decision generally have more bottom line impact. Sure, if you do a better job of choosing the “perfect product” it will result in better performance. It’s rare that those choices with dramatic influence are missed by merchants in the process of assortment planning. It’s much more common that over assortment is an issue.

This leads us to the second reason to consider allocation first. If you make the perfect assortment choices, and even create the ideal orders to DCs, a poor allocation can still irreparably damage the results you get. If, however, you make fairly good decisions on assortment and ordering (which is common since there are fewer choices being made and therefore more thought going into each) an improved allocation can make the best of what you ultimately end up with. These improvements, if done well, can almost always have more impact than changes to ordering and assorting. This frequently generates enough return to fund investment in the other two areas as time permits and as your business can absorb the change.

The retail world is changing

To add to this, complexity is the reality of today’s retail landscape. Customer behavior is changing at paces never before seen in retail. Between economic influences, brand loyalties, fashion preferences and other factors, today’s customer is more unpredictable than ever. This change is happening differently at each individual store so it’s important to have visibility to those changes and have the ability to respond to them immediately. Allocation is the last chance to identify and react to these and therefore is the closest you get to meeting the demand that your customers represent.

The last chance to get it right is logically the first place to invest in doing a better job.

Thank you for following this series. If you have any questions or comments, please feel free to contact me at greg.wilson@quantumretail.com.

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Get back in the game //

Are you ready 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 early on, 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.

Download free white papers on how to adapt to today’s retail market HERE»

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For more resources on allocation, visit: http://quantumretail.com/solutions/allocation-replenishment/resources

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“Matalan recognizes the urgency and importance of aligning their inventory investment with their customers’ continuously changing demands,” commented Chris Allan, Quantum Retail’s chief strategy officer. “Q will assist Matalan in better meeting those demands by understanding localized inventory behavior.”

With Q: Allocation and Replenishment, Matalan can now monitor and react to the unique customer behavior in real-time at each store to easily determine inventory need throughout its supply chain.

“We expect significant results from a leading edge technology like Q,” stated Andrew Scott, Matalan’s head of merchandise planning. “The system is a necessary investment that will enable us to understand exactly what our customers want and need at every location so we can provide them unparalleled service.”

Quantum Retail, winner of Supply Chain Solution of the Year and Supply Chain Excellence awards, offers Q to retailers seeking a hyper responsive, consumer driven, merchandise optimization platform to localize inventory placement and increase sales, profits, and inventory efficiency. Solutions include Allocation and Replenishment, Forecasting and Order Planning, and Assortment and Range Planning.

Matalan joins Quantum Retail’s growing list of successful clients, including Marks & Spencer, New Look, Kohl’s, and Guitar Center.

About Matalan

Matalan is a leading UK ‘value’ retailer, with annual sales of GBP 1bn through 200 stores. Matalan recently reported an increase of 30% in annual profit. Womenswear accounts for 35-40% of sales, followed by menswear at 25-30%, and childrenswear 10-15%. The remainder is made up by homewares, accessories, footwear, luggage, books, videos, etc. Matalan’s prime target market is 25–55 year old women with families.

About Quantum Retail Technology, Inc.

Quantum Retail answers the new questions facing retailers with a merchandise optimization suite designed for the increasing pace and complexity of the consumer revolution and today’s competitive landscape. Quantum Retail’s award winning solutions solve the most difficult and costly problems retailers face — quickly and permanently.

The Q solution is the new answer for: Forecasting and Order Planning — Replenishment and Allocation — Assortment and Range Planning.

Read more about Quantum Retail»

Strategy #10: Use constraints that minimize work and maximize results

Every retailer has limitations to what they can or want do in the process of allocating. Generally we refer to these limitations as constraints. For purposes of our allocation discussion we’ll discuss constraints in two categories:

Physical constraints

These are things that exist as physical limitations which may need to be considered in the process of allocating. Examples include capacity constraints such as shelf or rack capacity, eligibility (whether or not a store is eligible to receive an item at all) and packs and pre-packs. Physical constraints are facts that must be understood and considered to make the best choices in any allocation situation.

Operational constraints

These are things that we as allocators impose to ensure that the volatile nature of allocated merchandise does not cause our system’s recommendations to go too far in a given direction. Examples include mins, maxes, caps and target time supplies. Operational constraints are generally required to compensate for areas that allocation systems are unable to consider or understand otherwise.

Generally all constraints can be thought of as challenges which make the allocation process more complex. They are typically cumbersome to manage and often get in the way of allowing your system to make optimal decisions. So how can we best use constraints to minimize work and maximize results?

What you can do now

Ease up on the constraints. If you’re using better criteria, thus enabling your system to drive results more representative of what your stores need, the requirement for constraints is reduced. Here are some examples:

Physical constraints

Eligibility – tends to be binary (on or off) so there is typically not a lot of opportunity here. If, however, you are using eligibility to reduce stores in an allocation due to limited supply of stock, consider not doing that and rather letting demand determine who should be included.

Capacity – is often used as a max constraint. While this makes sense logically, be sure you’re considering the selling of inventory between the time you’re allocating and the time the new stock will hit stores. Your current inventory will be reduced during this time opening more capacity by the time the allocated inventory arrives. You should also monitor how often capacity is imposed. If it’s frequent, it may be time to consider giving the product more space.

Packs – are typically handled with rounding rules. If you have the option, consider using different rounding rules for different types of product. High ticket items and large or space consuming items are good candidates to round down more aggressively (reduce potential markdown or carrying costs) while high volume and inexpensive items are good candidates for rounding up more aggressively (less financial exposure)

Pre-packs – also generally rounded. If you have the option to configure your system to consider each item individually then do rounding based on total over or under, that is more effective than executing at the aggregate of everything in the pack. See also the note on size at the bottom of this post.

Pack Optimization – You may also have, or be considering, pack optimization options. Ideally this process should be evaluating the financial impact of pack decisions. In the case of pre-pack optimization it’s important that size profiles always be fresh. The assumption that size activity does not change within a season is false and should be challenged aggressively. Update profiles as often as time permits.

Operational Constraints

Mins & Maxes – Widen these wherever feasible. Lower mins avoid overstocking the lower performing stores. If you’re setting mins to ensure presentation, make sure you’re considering presentation for the lowest volume / space combination for the level being set (i.e. cluster). Similarly, max’s should be capping only the most extreme cases at the top of the volume for the level (i.e. cluster) that they’re set for.  Some systems can actually take chain level min/max’s and automatically modify them across volumes enabling you to set them at an average while the system grades them across individual volumes. This can achieve the same result with less effort and more intuitive parameter setting.

Caps – If you’re using a calculated trend that must be capped, these caps should be set for groups of stores (i.e. volume clusters). They should be set letting lower volume stores chase trends more aggressively since the impact is likely to be as little as one case. Higher volume groups should constrain the trend more aggressively to ensure they don’t overreact to a trend that may result in damaging overstocks. If you must set caps at chain, err on the side of caution by setting them as you would for high volume stores. There’s too much volatility, therefore exposure across your store base.

Time Supplies – If you must allocate to a time supply of inventory, do the pre-analysis to determine what an effective target is. If you have the inventory to achieve six weeks of supply (WOS) but tell the system to allocate twelve WOS, you’re forcing it to make unnecessary balancing decisions that negatively impact the result. Determine what WOS can result with the existing and available inventories first, then set the target.

What you should consider when looking for new capabilities

Today’s technology has evolved to the point that many of these constraints can be reduced or eliminated. In some cases that’s due to considering and automatically optimizing them as components of the allocation. Awareness of physical constraints are a fact that can often be interfaced in to allocation from other sources (Warehouse, Order, Assortment or Space systems etc.). Operational constraints are often reduced to just those requiring intuitive input. Presentation requirement defined as a min being a primary example. Once that minimum quantity floor is established, executing to a targeted objective such as achieving profit, revenue or service goals accommodates many if not all other constraints in the process.

Note: Size is sometimes considered in a category similar to constraints. It is a subject that deserves to be covered in and of it self. We have posted some thoughts on the topic HERE.

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Follow this series to learn all 10 strategies for improving allocation. We will be deconstructing the allocation process and exploring opportunities to improve within your current allocation processes and technology limitations. We will also review key areas to think about if you are considering investing in improved allocation capabilities.

Subscribe to receive weekly updates of this series HERE»

Download this blog as a PDF»

For resources on allocation, visit: http://quantumretail.com/solutions/allocation-replenishment/resources

Strategy #9: Go beyond sales and inventory units to factor profitability and other metrics into your allocations

When allocating in retail we’re ideally executing to a specific objective as described in our last post. That objective can be a variety of things including maximizing sales, maximizing service, maximizing profit or something similar. In reality all of the various possible objectives are all intertwined.

Focusing on just one metric may allow you to maximize it, but without visibility to and consideration of the others it is likely that you will sacrifice something more than would be ideal.

Conventional allocation solutions are generally unit based. As such they are able to attempt inventory or sales focused objectives but they have little or no visibility to financial metrics of sales, inventory and especially profit. This imposes a severe impediment to achieving the most basic of objectives in retail. Maximizing profit.

In addition to this, conventional allocation calculation capabilities tend to focus on one unit based objective at best. The most common is to level the time supply (i.e. weeks of supply) of inventory across all stores based on historical selling. The problem here is that say, six weeks of supply in one store may be profitable, the same in another store may not. This is especially true when scarcity and abundance of inventory or packs are a part of the allocation equation. So how can we get to more business oriented goals of getting the most profit or revenue from our allocations?

What you can do now

Assuming you’ve selected a quality base of data using methods discussed in prior topics posted to this series, the next goal is to get as much visibility to the competing metrics as possible. If you are able to define metrics such as dollar volumes or ideally some measure of profit or even simple margin – get them into your view. Even if they aren’t a part of the initial recommendations, they can be used as checks and balances to the result you do get. If possible, utilizing a calculation that impacts profit for overstocking beyond your target can make this even more useful.

Depending on the flexibility of your system you may also be able to consider these metrics in your recommendation calculations. While it may be too complex to create sophisticated logic around the financial results themselves, you may be able to set caps and / or alerts for situations that would create negative financial results. An example may be to cap a shipment where sending another pack would go over target WOS enough to have a negative impact to margin of more than “X%”.

If you don’t have this capability you may be looking at a situation that warrants some external analysis. Screen grabs and spreadsheets may seem remedial, but the time invested can often pay significant dividends for a well thought out process.

What you should consider when looking for new capabilities

The capabilities of technology and mathematical sciences continue to expand. Innovative systems are able to measure both historical and future impacts to financial metrics such as revenue and profitability. When applied correctly they can measure impact across a variety of competing objectives and find the right point to maximize the primary objective (say profit) without sacrificing the others (revenue, service level, availability, presentation etc.). In doing so the answer for two locations which might look exactly the same in historical sales units often result in very different answers that return better results.

<< Previous post in series | Next post in series >>

Learn more

Follow this series to learn all 10 strategies for improving allocation. We will be deconstructing the allocation process and exploring opportunities to improve within your current allocation processes and technology limitations. We will also review key areas to think about if you are considering investing in improved allocation capabilities.

Subscribe to receive weekly updates of this series HERE»

Download this blog as a PDF»

For resources on allocation, visit: http://quantumretail.com/solutions/allocation-replenishment/resources»

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As retailers seek out new business tactics to lure back the customers they lost during the recession, they will find that one of the most profitable strategies is creating a customer-centric supply chain.

However, one reality retailers must face is that the recession has created a new consumer paradigm. According to a new report by PricewaterhouseCoopers and Retail Forward, entitled “The New Consumer Behavior Paradigm: Permanent or Fleeting,” customers will not bounce back to their old shopping habits, as “seventy-two percent of all shoppers recently indicated that their shopping behavior has changed significantly or somewhat as a result of the economic environment”. The report suggests that shoppers will be more deliberate and purposeful in their spending, giving way to a more practical consumerism. However, it also predicts that these shopper behaviors may change as the recession eases.

Since we know no one can foretell the future to know exactly how and when behaviors will change, our take on this study is that consumer behaviors have changed and will continue to change, and that retailers need to actively seek new ways to engage them (especially the younger generations), and be ready to continually adjust their product mix accordingly.

With this new paradigm in mind, retailers must take a step back from their businesses to understand how to engage today’s new consumer.

Some questions that retailers should ask themselves:

What are your customers looking for when they walk into the store? Why are they buying that item? Does their buying strategy map to the one you have for the product? What are they not buying? How much are they buying? How often are they buying? Are they buying it at the same store?

To fulfill changing customer demand in your supply chain, you have to start at the store, and it comes down to the two basics: breadth and depth.

What and Where:

There are the tried and true ideas behind why customers select a certain product (the customer product strategy) when supporting customer demand: Price, impulse buys, destination items, etc. But customers today have a huge wealth of information at hand when deciding what to buy, and therefore they can include many new inputs (as well as the tried and true). These customer demand choices indicate their product preferences, and will be inputs into the customer buying strategy, and hence need to be included in your product strategy.

Preferential signals (Inputs to the customer strategy): Price, convenience, fashion-forward, technological, locally made, organic/sustainable, ethical, necessity, value, quality, size, color, style, brand, culture.

Ideally, you have a host of customer data that lets you not only map customers to purchases, but also link the changing customer buying strategy with your product strategy, and this may be different by location. If you do not have customer transaction and purchase information, you can use product/store level demand as a proxy, perhaps supported with market data. It will be important in this changing environment that these product/location strategies are continually monitored and updated.

Once you can assign the product strategy at a location level, you can tackle the breadth issue, i.e. the assortment. Most retailers cannot operationally manage unique store level assortments, and need to assign clusters that are often constrained. Care, supported by process, timely information, and optimal systems are needed to manage the conflicts between desired ranges, and operational constraints: space, fixtures, and assortment planning groups.

How Much and When:

When you assign a strategy to a product/location to drive assortment decisions, this same strategy should be used to drive depth. For example, a key destination item may need a very high service level so that your customer will not be disappointed.

In order to best meet these strategies and keep inventory performance high, the time phased aspects of the local customer demand need to be taken into account:

Circumstantial signals: The time of day or week activity occurs, holidays, local events and promotions, sports schedules, weather, seasonality and regional demand.

Putting it all Together

Retailers need to have the ability to assess and continually change with the patterns at each store based on the local signals and behaviors of their customers. In order to increase margin, achieve proper stock levels and align assortments with customer demand, top down simplifications in the inventory planning process must be removed.

Stores that can quickly process customer behavior and turn it into inventory execution will have an immense advantage in today’s marketplace. This means creating a dynamic inventory plan that is highly reactive to local demand fluctuations, allowing the retailer to be flexible and respond to how their customers are behaving now. This enables the customer to have product available when and where they want it, in the right size, the right color, and the right style at every store and in every channel.

Linda Whitaker, Chief Scientist, Quantum Retail, is one of the leading practitioners of retail science in the country. She provides the research, innovation and advanced science for Quantum Retail’s solutions. Prior to co-founding Quantum Retail, Linda spent the past 17 years developing optimization and scientific solutions for complex retail problems in replenishment, logistics, pricing, promotion and consumer behavior at Retek Inc. and HNC.