Posts Tagged ‘assortment and range planning’

This Retail Life – Part 3: Battling the big vendors simply takes a new approach

by Quantum Retail • April 27th, 2011 • Write a comment »

“Some vendors have been working on and gathering more and more solutions that cater to retailers in an end-to-end manner. Examples are solutions coming from folks like Oracle, SAP, SAS, JDA, IBM, Epicor, RedPrairie, Island Pacific, Jesta, and others. But these solutions are mainly just minor variations to the same traditional processes that have been in retail for decades now. They have yet to really achieve the ability to learn from what they “see” and promptly adapt to that with recommendations that will meet the evolving needs of today without the need for user intervention all along the way.”

LISTEN TO PART 3:

speaking with Greg Wilson, Director of Field Strategy at Quantum Retail • April 2011

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Audio Transcript

Thanks to our audience for joining us again this week. I am Dan Brown from Mulberry Marketing, in San Francisco, and I am joined with Greg Wilson, Director of Field Strategies at Quantum Retail.

Thanks for having me Dan.

It sounds like Quantum is continuing its pattern of rapid growth, but I understand there is still some push-back in the market. Why are retailers stubborn when it comes to change?

Well there are really a few reasons that retailers are a bit resistant to change.

One of them is a fear of a something new – a concern of the learning curve of a different approach. They’ve gotten comfortable with processes they’ve created and nurtured over many years, and there’s a level of re-education that’s a part of taking on any new approach. Unfortunately, many of the pitfalls of traditional approaches come directly from dated technology, its constraints, and the old thinking that surrounds it.

Another reason is that some retailers are a bit scared of the term “automated” – they see it as taking the decision making process out of their hands. There really needs to be a certain level of trust established before they’ll consider any form of automation.

And finally, a lot of retailers have made significant investments in technology over recent years and that’s what’s driving their current process. Sometimes they are hesitant to put more money into something new, or something that they could build themselves. We do actually occasionally run into retailers that try to pry information out of us and get details about how we’re solving these problems so they can build it themselves. But ultimately it’s a waste of their time though. To build something with enough capability and sophistication to solve the more challenging problems, it’s incredibly complex. It takes experts in various disciplines of business technology and the science of econometrics and other statistics related to solving the problem. So by the time they just get started in building something, assuming they could even do it, they could already been achieving value from Q.

But the old retail foundations that most retailers are still using make it impossible to change their way of thinking, right?

Well to a large degree that’s true. Traditional systems are generally like a large spreadsheet of data – and the system enables you to enter parameters or rules and calculations on data that you select – they require a significant amount of user input and manipulation, and the only way to enable that, is to set and manage this stuff at higher levels of aggregate. The problem is that this pushes you further and further from the ultimate goal which is localization, or understanding and reacting to what’s happening at each location with each product.

Some vendors have been working on and gathering more and more solutions that cater to retailers in an end-to-end manner. Examples are solutions coming from folks like Oracle, SAP, SAS, JDA, IBM, Epicor, RedPrairie, Island Pacific, Jesta, and others. But these solutions are mainly just minor variations to the same traditional processes that have been in retail for decades now. They have yet to really achieve the ability to learn from what they “see” and promptly adapt to that with recommendations that will meet the evolving needs of today without the need for user intervention all along the way.

The traditional approach is also pretty limited into the scope of what it can address to deal with the really difficult products like big ticket slow-movers, sized merchandise, highly volatile merchandise, seasonal products, short-life products, perishables, pack-constrained products, and heavily promoted items, things that are scarce or vendor-allocated, or have long lead-times.

All of these things require new models and new ways of thinking to address them really well.

So how does Quantum’s “new school” way of thinking differ from that of traditional solutions?

Well, the Q Platform that we’ve built actually enables us to solve the problems that these other vendors mainly talk about solving. It delivers on the business case every time, with proven, measurable results. Quantum has developed the concept of managing by merchandising strategy–determining the role of the product within the customer offering, such as being an image item, a traffic driver, a profit generator or a fringe assortment item, or things like that.

Users aren’t asked to select and ultimately guess at an overwhelming number of forecasting or replenishment algorithms, and to set a slew of difficult parameters around each item. Q takes the strategy and understands the objectives of the product from both a financial and a merchandising perspective and it ensures that every inventory decision that it makes is aligned with achieving those objectives.

The way that customers buy product changes over time and Q adjusts automatically to react to those changes by constantly working to ensure that alignment is maintained throughout the products’ lifecycle. This is much different from having to actively maintain ordering, allocation, and replenishment configurations for every item in every store and manually ensure that the system is set up correctly to do this like other solutions do.

In the process of understanding items, Q considers over 30 dimensions of product behavior. Things like: maximum sales, true historical demand, forecast demand, days between sales, lost sales, days between out of stocks, current inventory position, last stock-out, weeks of supply, percent in stock, etc.

But beyond these typical sales and inventory metrics, Q also understands things like:

  • When an out of stock happens, an out-of-stock on Monday has different gravity than out-of-stock that happens on Saturday, when you’re looking at things in a weekly level.
  • Variations in contributing factors like: lead times, lifecycle, and service levels, which have a significant influence on making the right decision.
  • Variability in sales, such as volatility, lumpiness, lost sales, are very important to understand. And finally, and probably most importantly, are profitability metrics such as gross margin return on inventory investment.

So these capabilities have led to retailers being able to have a high degree of automation with Q using exception management to highlight only those areas where users should be spending their time efficiently and effectively in the system.

Can you talk more in detail about product strategies? Do they change from retailer to retailer?

Yes, absolutely strategies change from retailer to retailer. Often we find people using ineffective strategies such as simple min/max replenishments, or things like that.

What they really need is a true strategy for their products.

So, rather than just setting a target service level which is a guess, usually at some group of stores of what amount of inventory hits a service level that will meet an objective, they really need to define that objective.

For example, “What am I doing in the realm of profitability for a profit generator?” I need to understand how much service allows me to capture sales, but I need to make that subject to some constraints around waste, or potential losses from carrying merchandise. I also need to be considering the volume of sales that I am generating. So profitability might not capture every sale, but it will allow me to avoid waste as a result of not capturing every single sale. So balancing all of these things has been traditionally very difficult because you have to pick a handful of parameters and manually set them at some level that is manageable. But really this evaluation should be happening all the time.

So by enabling Q to operate to this strategy, you know what the goal is, and Q can dynamically set all of the parameters as it understands about the behavior of merchandise to achieve that objective consistently.

We also see a lot of retailers falling into the trap of thinking that it’s all about forecast accuracy. But for a majority of items the value of the forecast is really diminished, if not irrelevant. You’ve got values that are significantly less than one unit, at that point the decision isn’t really about, “is the forecast .04 or .05,” it’s about what is the right inventory decision to achieve that goal given that forecast and the volatility around it.

And other vendors talk about localizing, but they’re all just talk – aren’t they?

Well yeah, they really are. And the reason for that is–because of the complexity of the problem. They are aggregating some of the criteria that is managing their processes up to higher levels to make it easier for the user to manage. But that gets you further and further away from the unique characteristics of the individual products in individual locations, which is really what’s necessary for localization.

What Q does is it enables you to set a high level parameter, but it’s a dynamic parameter that says what your objective is, and by doing that, Q can understand the behavior of each individual store and each individual product, and put that together with your objective so it is constantly achieving that objective at that SKU/store level. And only when you do that are you really achieving localization.

You mentioned that Quantum always performs on the business case, every single time. Can you tell us how that process works. How do you sit down and decide on a proper business case with new clients?

Well first of all, when you meet with Quantum, we don’t shove a big list of clients down your throat. We ask you what your problems and opportunities are – and we help you build and define a strategy for your merchandise. Every item needs to have a role and each role needs to have a goal. And that’s what makes Q so unique, it doesn’t just keep up with demand – it keeps up with each item’s strategy and the objectives of that strategy.

Let’s talk competition. You are often among the final two or three vendors of choice. How do the big vendors compare to you? Are they intimidated by you guys?

I don’t know that the big vendors are intimidated by us, as such, but they are certainly aware of us. And they are aware that there is a new approach available. And more and more retailers are coming around to understand the value of that new approach.

In addition to our system’s adaptive nature, simple and intuitive navigation, ease-of-setup, and the fact that it’s exception-driven, we are able to get in place very quickly.

Implementations happen in fewer than 20 weeks, and the system’s light footprint means a smaller investment. We can actually employ on top of some existing structures and optimize them in some cases whether that be a short term objective or part of a longer term objective.

Our customers have reported fewer than 12 months to reaching a 100 percent ROI in every single case. And this is measured through test and control groups, where we take two groups of stores that are behaving similarly. One of them becomes the test group which we manage with Q, and one is the control group, which continues being managed with existing systems. Taking this approach ensures that the returns we talk about are only possibly a result of utilizing Q.

Being a small company, how does your relationship with the client change the solution’s effectiveness?

Well from the beginning of an engagement with a retailer, we establish a relationship, we become a key component of reviewing and improving their business strategies as they affect the whole of their company. We’re looking at the larger picture – and create a business case that is specific to them – and how much profit they can expect to achieve.

Being a smaller company also allows us to build out our product to the needs of our customers. We have three core products, assortment and range planning, allocation and replenishment, and order planning. And we are always in the process of making these products better, which our current clients get to enjoy – and when they have specific needs of their own, these updates sometimes become a part of the core product.

But we also have the fun of building a strong partnership with our clients, becoming team players – their success ultimately is our success – and we are always looking out for how we can make their business better.

And can you give us a last few words of comfort for retailers looking to make the leap away from their old tech into a “new school” solution?

Yeah, I think probably the most important thing for people to understand is that new technology, even though the process and the underlying mechanics are more complex, new technology is actually easier to use, it pays for itself in months, and it improves over time. You have nothing to lose!

Thanks so much for joining us Greg!

It was good to be here.

Tune in next week when we will be speaking to Vicki Raport, CEO of Quantum Retail.

Download a PDF of This Retail Life: Changing the Game in Retail»

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#1 Supply Chain Tech sought at NRF 2011: Assortment Planning Solutions

by Quantum Retail • January 11th, 2011 • 1 Comment »

We met with over 100 retailers and analysts at NRF this year and what was the most common technology solution they were seeking? A tool for assortment planning.

As we’ve all seen, 2010 was not a stellar year. Christmas results were at the low end of the growth spectrum. Consumers remained cautious and practical with their spending. Value entered into everyone’s psyches. So with all of the work that retailers did with internal restructuring, we’re finally seeing that retailers’ wallets are prepared to open up in 2011.

In fact, in the NRF/KPMG Retail Horizons report, respondents stated that the main area they plan to begin spending in 2011 is particularly in mission-critical areas, like IT.

You can check out more highlights of the NRF/KPMG Retail Horizons report HERE »

For more information on Quantum Retail’s Assortment & Range Planning solutions, visit» http://quantumretail.com/solutions/assortment-range-planning/introduction

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

To download our guide: 4 Strategies to Optimize Assortment Planning, visit» http://info.quantumretail.com/4-strategies-to-optimize-assortment-planning

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The Profit Lab: Assortments and financial plans and how they work together

by Quantum Retail • September 10th, 2010 • 1 Comment »

Financial planning was made prevalent first by early spreadsheets and then by merchandise financial planning applications that have been around since the 1980s. Assortment planning began around the same time, but is both less mature and has more scattered, inconsistent processes throughout the retail world. It seems that no two retailers do assortment planning the same; however, on the financial side there is more commonality in areas such as: the plan, open to buy (OTB), and weekly stock, sales and inventory (WSSI) reports.

So how can assortment planning and financial plans work together in order for retailers to get the most out of each? The main enabler to having a better financial plan is to have an assortment plan that can truly identify opportunities to improve the financial goals of the organization that are outlined in the financial plan.

The financial planning process

The financial planning process typically starts off by planning sales. This is often done at the chain level, sometimes incorporating comp and non-comp or store changes such as openings and closings. Usually, this chain plan is then broken down by merchandise hierarchy, so that each department’s planning process begins with building a sales plan. This sales plans, if you’ve followed my previous posts on similar subjects, can be enhanced by a sales forecast. This forecast can pick up more recent trends in a category, class, or department level that a simple comparison over the last year cannot.

Once a sales plan has been developed, the next piece of the planning process is to build an inventory plan. The question to ask is this: “How much inventory do I need to meet the sales plan that I just laid out, which has been confirmed by a forecast as the most likely sales activity?” Once the inventory plan is established the inventory receipt plan is established. The planned inventory receipts are the planned ending inventory, plus the planned sales, markdowns and inventory adjustments, less the end of month inventory from the prior month. The receipt plan exists in order to provide an inventory purchasing plan for future periods.

The main issue is that you have no idea what to buy nor do you know where to put the inventory in order to maximize the sales potential to your business. That’s where the assortment plan becomes such a powerful vehicle to performing well. By coordinating a constantly updated forecast enabled financial plan with a sophisticated assortment planning tool, you can ensure that you’re buying goods and putting them in the stores that will perform the best.

Open to Buy

This also works when you have an Open to Buy. OTB is essentially the difference between how much inventory you now have and how much you actually need in order to meet your sales plan. It includes in-stock inventory in stores, in the warehouse, in transit to the stores, and on order. OTB dollars (or units) are often kept positive in order to take advantage of special buys or to react to items that are performing well above expectations.

While Open to Buy is a great way to manage the balance between sales and inventory, the OTB that is typically utilized is simply not at a low enough level to give the buyer or planner any insight into what merchandise will actually help you meet the sales goals that have been laid out in the financial plan. For example, OTB is most often completed down to the level that the buyer is responsible for. That could be a category or a department. But, the buyer, in coordination with the planner, has to make selections at the item level and determine which stores to put more stock in and which stores already have enough. OTB, therefore, is simply not granular enough to accomplish the goal of maximizing the sales plan.

Getting the most out of financial planning and assortment planning

In order to get the most use out of financial planning, the process must consist of a robust assortment planning tool that can tell a buyer and planner at a very granular level which products are performing well at each store or cluster of stores. To meet financial objectives, there are three important capabilities that assortment planning tools should utilize.

First, assortment planning tools should be able to quickly and constantly suggest changes to the plan that can help meet financial goals. Examples of this are suggestions of stores or clusters that are under assorted, where adding items to those stores can increase sales.

Secondly, assortment planning tools should have robust ‘what if’ capabilities. One of the better ways to accomplish this is to incorporate an interactive item/location forecast into the assortment plans. This way, if a user wants to see the effect of adding or removing an item from a cluster, she can immediately see the impact of that change in the assortment. The ability to see into the future with a forecast that is performed at different levels (e.g. department in the OTB plan and item/store in the assortment plan) can significantly enhance that marriage of the plans.

Lastly, a good assortment planning tool needs to be able to compare the financial plan and the potential ‘what if’ assortment plan to identify what is ideal for each store. If all three of these are put together in an assortment planning tool, you have the ability to fully realize the potential of the OTB or in-season plan.

New Products

Today, the typical retailer constantly introduces new products into the assortment for a variety of reasons. The one reason that permeates every type of business is to keep the assortment fresh and give the customer something new to see each time they walk into the store. New products obviously have the ability to add value to the assortment and help meet the goals of the financial plan, but knowing which new products will perform well in which stores involves some sophistication.

When looking at a new product and attempting to determine its demand, one can choose a like product or a combination of like products given attributes, price points, merchandise type, and so on. But it is critical that an assortment plan has a good way of determining how an item will perform in a given store. The total demand can be aggregated to the cluster to determine how much of that item to purchase and which stores should get the item. This quantity should then be balanced with the financial plan to manage the remaining OTB dollars.

In Summary

A financial plan is not complete without an assortment plan. The assortment plan needs to be a key tool that can help a retailer meet the goals that are determined during the financial planning process. A good assortment plan takes the guidance of the merchandise financial plan and carefully analyzes the merchandise on a detailed level recommending changes to the range breadth at a store or cluster level, and has the ability to perform ‘what if’ scenarios to find the best mix of merchandise, including new items in order to fully maximize the potential of the merchandise.

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The Profit Lab: Using Forecasting within an Assortment Plan

by Quantum Retail • August 31st, 2010 • 2 Comments »

THE PROFIT LAB // 4 Strategies to Optimize Assortment Planning

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

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The Profit Lab: Clustering with localization in mind

by Quantum Retail • August 24th, 2010 • 2 Comments »

THE PROFIT LAB // 4 Strategies to Optimize Assortment Planning

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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The Profit Lab: New series – Assortment and Range Planning

by Quantum Retail • August 17th, 2010 • 2 Comments »

THE PROFIT LAB // 4 Strategies to Optimize Assortment Planning

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.

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Fashion Innovation Series: Part 2 – Assortment Planning and Range Planning Localization

by Chris Allan • April 25th, 2010 • Write a comment »

This is the part 2 of a 4 part series on Fashion Innovation and Optimization.

KEY TOPICS IN THIS SERIES:

  1. Size & Pack Optimization
  2. Assortment & Range Planning
  3. In-season Replenishment
  4. Allocation Optimization

Look out for part 3 next week, to read part 1 – CLICK HERE. Please engage, start a discussion, or leave a comment if you like this post.

Assortment and Range Planning Localization

Customer behavior has changed…unfortunately retailer processes and systems have not kept up with the pace of that change. The way that stores are assorted needs to change – to reflect how and where the customer now wants to shop and what they want to spend their money on.

Retailers need to focus on changing the way that they plan their assortment and identify opportunities to align their offering with their customers, to drive profitability of every product in every store. To achieve this, retailers need to identify not only the financial objectives for each product in their assortment, but also the merchandise objectives – as these are key to creating a credible offering in the eye of the consumer.

The most important, but over-looked questions for assortment optimization today:

Why is this product in my assortment?

What strategies do I have in place to decide what products to include?

How am I measuring the performance of my assortment on a continuous basis?

How will this product perform in the future?

How am I aligning my assortment with local demand?

When retailers align both the breadth and depth of their merchandise offering with the localized demand of their customers, it increases full price sales and product availability, and ultimately lowers markdown spend.

There are two main areas in planning that retailers should focus right now:

Sku rationalization //

How well is the breadth of the offering aligned to the customer? It is important to identify where you have too many or too few choices for the customer and have the flexibility to execute on those decisions. If you are not doing this, you are creating both markdowns and lost sales. Retailers need to keep this flexibility and continuously monitor the profitability and contribution of each product. This will allow each store to reveal its own patterns and tell the retailer how to best align their SKUs with local demand.

Localizing inventory //

The customer is the most important element of today’s retail strategies. In order to compete in today’s market – retailers of all verticals need to focus on availability and local consumer behaviors. This kind of granular detail cannot be obtained with traditional, data aggregating systems. Retailers need to remove the simplification from their inventory planning process and focus on real-time local demand. 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 allows 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.

5 Ways to Innovate Assortment Planning

1. Optimizing inventory:

Retailers need to focus on optimizing their assortments and shaping their offering based on both the merchandise and financial objectives of those products. Many retailers are focused on shrinking their offering, but fewer wrong products will not create more sales, it will only frustrate customers. Investing in the right brands and the right products will ultimately bring new energy to the retail market. Understanding exactly where the offering should be contracted or expanded is they key to achieving those goals.

2. Better placing inventory:

Some of the best retailers have not scaled back on their inventory investments, but focused instead on where to place their inventory. Over the last year, ‘flat’ was the new ‘good’, but by putting inventory where there is demand, retailers can increase their sales and profit, while better serving their customers at the same time. Retailers can also hold back inventory to see where it is performing best – and use precision placement of their remaining inventory to increase profit and create fewer markdowns.

3. Increasing availability:

Focusing on which products need to be made available at what locations and when is a difficult task. But when the right products are available, in the right sizes and colors and in the right amounts, stores increase sales and increase customer service levels.

4. Focusing on the intelligent consumer:

The market has shifted with the intelligence of consumers. The economy has further focused the customer on seeking out the highest value for the lowest cost. The environment has also brought to light new values and new criteria that the customer has begun to judge products on. Retailers need to recognize the needs of their customers and give them products that meet these new expectations – and remember, these expectations will continue to change.

5. Focusing on newness:

If a retailer can continually have something new for the customer to see, it will increase the frequency of customer visits and increase turn rates. This is especially important in fashion and consumer electronics, where customers have become increasingly knowledgeable and demanding. If a retailer can keep up with the pace of fashion, they’ll be able to keep their inventory fresh and unique.

LISTEN >>

Learn how to implement better planning practices to manage the breadth of your assortment

Chris Allan, Chief Strategy Officer, Quantum Retail

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You can also follow our 4-part 2010 Retail Outlook series here.

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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.

For more information, visit: http://quantumretail.com/services/size-pack-optimization

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Aberdeen Group Reviews Q Assortment and Range Planning

by Quantum Retail • September 29th, 2009 • Write a comment »

Quantum Retail Launches Assortment and Range Planning: Potential for Tailored Assortments in Retail

SAHIR ANAND, On September 22, 2009 - Quantum Retail launched an assortment and range planning merchandising module to expand its merchandising suite’s (termed “Q”) footprint in retail. This module is geared towards fulfilling a localized merchandising need that exists in the retail industry today due to the ensuing tough retail economy. Quantum’s latest module comes at a time when there is a willingness among leading retailers to tailor or customize their assortments to more closely match the needs and wants of their customers. This Market Alert will outline the potential opportunities and pitfalls of Quantum’s strategy at a time when product localization and store-centric assortments are gaining traction in the retail industry, yet the purse strings are not exactly loose when it comes to retail IT spending. This Market Alert also assesses this development from an end-user’s perspective.

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Quantum Retail Releases Q: Assortment and Range Planning

by Quantum Retail • September 22nd, 2009 • 2 Comments »

Managing the inventory mix for every store in the chain is the art of retail. Q: Assortment and Range Planning merges that art with science, automation and simplicity, allowing retailers to strategically merchandise and assort their stores to drive category performance.

MINNEAPOLIS, September 22, 2009 – Quantum Retail announces the general availability of Q: Assortment and Range Planning, a new module running on its Q platform, which will enable retailers to optimize the way they approach the art of assortment and range planning, financial reconciliation and product exit management.

The software has the smarts to perform all functions of product forecasting, assortment rationalization, intelligent store clustering and behavior modeling. The module also offers advanced size, pack and markdown optimization as a service, focused on giving the retailer the highest return on the inventory they are buying. It does this by analyzing the demand of each store individually and rationalizing the product mix in each department to make recommendations to buyers as to which stores are over or under assorted, and identifies which products are holding back the category from reaching its goals. In turn, it has the know-how to calculate the best strategy to manage a product exit and phase in a new set of inventory.

“Q: Assortment and Range Planning redefines how buyers and merchants leverage planning applications. It starts with the one critical quality missing in conventional approaches: it’s actually usable,” stated Chris Allan, Quantum’s chief strategy officer. “The pace and unpredictability of retail today is challenging. Local demand changes at every store on a daily basis. Q understands that – and it helps retailers monitor the changing demand at every store, so they can align their assortment in the way that is most profitable and most strategic.”

Like all Q modules, Quantum decided to approach the assortment planning process from a different angle. Instead of managing hundreds of spreadsheets, the module constantly updates data on product performance and customer behavior, which it then uses to automate the way retailers manage and assort their stores. This allows retailers to easily determine what products should go into which locations and when, ultimately optimizing to merchandise objectives – like profitability, sales and service levels.

Continuous store insight helps Q: Assortment and Range Planning align the merchandise offering to ever-changing localized customer needs. Retailers can set strategic plans to phase out under-performing merchandise and phase in a new mix, while identifying high performing products for continuity lines. The Q module can either bolt-on to a retailer’s current system or completely replace it with the powerful Q engine which will then make sure that those categories remain stocked at the appropriate levels according to individual store demand.

Automated store clustering and assortment rationalization functions let retailers optimize their assortments immediately. Retailers do not need to wait months or years to see results; the answers from this technology are immediate and ongoing. Q helps users see the impact on their profitability and service levels with any assortment changes before they make them, as well as create several drafts to ensure that they can quickly respond when the market does not behave as expected.

The system is configured at implementation to a company’s specific merchandising goals and metrics, enabling the retailer to make strategic decisions on its assortments. Similar to other Q modules, Q: Assortment and Range Planning has a value-driven implementation approach and an expected six-month return on investment. The benefits from this product are incredible for retailers seeking rapid results.

When retailers align the breadth and depth of their merchandise offering with the localized demand of their customer need, it increases full price sales and product availability, and ultimately lowers markdown spend. Q: Assortment and Range Planning ensures that a retailer can create a product range that will meet both its merchandise and financial objectives, simplifying the complex art of merchandising.

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About Quantum Retail Technology, Inc.

Quantum Retail has created a revolutionary merchandise optimization system, called Q, that aligns the art and science of modern retailing to the challenges of the 21st Century. Every Quantum Retail customer has achieved 100% return on investment in less than 6 months.

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