Posts Tagged ‘allocation’

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The Profit Lab: Determining need… what’s your strategy?

by Greg Wilson • July 13th, 2010 • Write a comment »

THE PROFIT LAB // Top 10 Ways to Pull Profit from Allocation

Strategy #8: Create product strategies to understand each store’s true need

OK, you’ve spent the time and effort to select the perfect historical activity criteria. You now have the best possible representation of future activity you can get, now what? How will you support that with inventory?

Let’s start by taking a look at traditional approaches. Once you have an idea of how an item will sell, what do you do next? The common assumption is that if all stores have the same time supply (i.e. weeks of supply) of inventory all will be well. Alternatively, many systems use the premise that a store’s inventory need is equal to its contribution percent of the forecast or historical selling. Unfortunately these assumptions fall short in a few ways.

First, we never have the perfect forecast or criteria for all stores. As such even if we give the same time supply of merchandise they won’t sell through equally. In addition to understanding the accuracy of the projection or forecast, it’s also valuable to understand the inaccuracy. A store with a need of six units because it sells one every day is different than a store that needs six because every once in a while it sells four or five. Understanding this may cause you to make a different decision regarding how (and when) to support the need with inventory.

Second, most of us are constrained to some extent based on packs. So if a store needs 9 units and we have a pack of 6 we send either 6 or 12. We’re now under or over stocked. Which is the right decision? What if I have most of my stores on the cusp of this rounding point? I can’t treat them all the same because I don’t’ have enough inventory. Now what?

Third, we haven’t considered the true economic impact of the decision. If I send three percent of my inventory to a store that generated three percent of historical sales what is the likelihood and cost of some of those units going to markdown? How does that compare to the likelihood of missing a sale? What’s the cost of that? The answer will be different for each location.

Fourth, what is the relationship of the time supply to the presentation? What if presentation represents six weeks of supply in half your stores, but you only have four weeks of supply at the DC? If we constrain to presentation some stores will get less than three or even two weeks of supply.

Finally, we haven’t considered the role of the item in the assortment. Chances are you’re treating all items the same. An item that is in the assortment to drive traffic has different inventory requirements than an item whose role is to round out an assortment. These are different from the profit generators, which are different from your core assortment and key items etc. These roles vary by product but can also vary by location for a given product. Considering this “role” of the merchandise will lead to different inventory needs.

What you can do now

Starting with the assumption that you’ve chosen a good base of data, most conventional allocation systems are then limited to the calculations and constraints to determine the inventory need by store.  We need to manage these based on what we’re trying to achieve with the merchandise. Here are some things to consider:

  • If it’s a slow mover, ratchet down the presentation requirements and let your allocation system drive who gets the inventory.
  • If it’s a traffic driver, make sure you don’t short-change small stores with too conservative a minimum. If you do the larger locations will take everything.
  • If it’s a high margin, profit item, don’t be as concerned about chasing opportunities that may look like over stocks. Select more aggressive pack rounding options (round up) if you have the choice. The larger profit margin can quickly cover the impact of markdowns if you sell a few more units.
  • If it’s a low margin item, DO be conservative about chasing opportunity because sending markdowns may be devastating to profit. Select more conservative pack rounding options (round down) given the choice.

Ideally you’re already looking at opportunities to improve your presentation requirements and pack sizes. I’ve always felt that presentation should never be more than 1/3 the demand for any location over the lifecycle of short life merchandise. Pack sizes should be reflective of the smallest multiple you’ll need to ship. This is especially true if you’re constrained to 1 pack configuration. Consider setting a minimum of zero on fringe sizes outside of very core assortment apparel. Let demand drive that activity. If you include the core in your historical base of data you’ll capture changes in demand for fringe sizes.

One more note: If you’re spending a lot of time manipulating the recommendations your allocation system is providing you probably need to spend more time on fixing that upstream. Multiple examples have shown that effort spent in good criteria and constraints then left alone produce better results than intuition and manual overrides. In fact, based on personal experiences I’ve taken to referring to such manual intervention as “de-optimizing”. Challenge yourself and your team to see how much final intervention they can avoid by spending more time in the criteria up front.

What you should consider when looking for new capabilities

Advancements in technology and in science have enabled the most modern of systems to consider all of these things simultaneously when recommending allocations. The best systems generate regularly updated forecasts which can be used for new and existing items. The forecast shares not only the end unit need, but also the learning that went into deriving that need so all of that understanding can be used in solving the inventory side of the problem as well.

This understanding together with defining the role of the product can give these sophisticated systems the information they need to focus on how much inventory is required to meet your financial and strategic objectives with the product. The role reflects most of the complicated data metrics and parameters.  Traditional systems used to require merchants to understand, interpret, define and manage these settings manually.

This process actually simplifies merchant interaction with the system despite advancing sophistication and management of the more complex problem solving necessary to get incremental improvement in results.

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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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The Profit Lab: Is there more than one shot at profit?

by Greg Wilson • June 29th, 2010 • Write a comment »

THE PROFIT LAB // Top 10 Ways to Pull Profit from Allocation

Strategy #7: Test the opportunity in a second allocation shot in short life merchandise to significantly increase margin

When fast fashion merchandise sells, it’s quickly replaced with the next great style. Single allocations are the nature of short life fashion. However, conventional wisdom follows that because this merchandise sells through so quickly, there’s no opportunity to react. Retailer must instead rely on their experience and the “art” of retail to guide them to the single, best allocation answer with the ever present “One Shot Deal”. One receipt shipped completely to fulfill all demand.

While this may seem to make sense for very short life items on the surface, it invariably leads to missed opportunities. Some of the assumptions that have lead to this becoming commonplace in fashion retail were based on technology and/or process limitations. Any other reasons deserve a friendly challenge.

Can using one or two weeks of actual selling to drive a small second shot really have a significant improvement vs. the one shot deal on an item that lives for less than six weeks? In a word… YES! There is enough insight in that little bit of data – and enough error in your initial allocation assumptions – that doing this well invariably provides improved returns.

Consider this: If you avoid a 20-30% markdown in 5-10% of your stores by sending an item that would’ve been marked down to a store selling it at full price rather than being a lost sale, how much does that add up to in margin? Now extend that for all products that ship with one shot. It often adds up to hundreds of thousands if not millions of dollars in found profit annually.

I’m not suggesting that there’s no cost to this. I frequently get challenged with reasons why “we can’t do that” – Suppliers won’t… DC’s can’t… labor costs are too… etc. While these can be real concerns, they’re not issues beyond being addressed. Does having a second allocation opportunity provide enough return to justify the effort? Until you ask the questions and do the math you can’t be sure. Here’s a hint though… it almost always does.

What you can do now

If you can do a second shot but you’re not doing it, start! If you have limitations keeping you from doing it, challenge them. Have you asked the vendor if they’ll ship in two shots 2-3 weeks apart? What if they say it’ll cost them too much. A nickel per unit in cost hike on a $20 item is probably easily offset by the benefits. Do the math & ask! Same with DC costs. Is there a corner of the DC we can use? Can we put one person on it part time for a test within a category to prove it?

Try these things now and you could be poised to make significant impact to this holiday season!

When it comes to actually allocating, use the recent week or weeks as your base. If it’s too little data or too volatile, combine that with forward weeks for a similar item from last year to get more data while still influencing it with the recent selling. There’s a lot of opportunity to be found in second shots!

What you should consider when looking for new capabilities

Modern systems take advantage of advancements in technology and data processing to analyze what the last week’s or even the last few days mean to the behavior of a product. They can relate this to other items and locations now – and in history – to derive how this item is acting within its lifecycle and to derive a much more confident representation of what’s likely to happen as it moves toward the later stages in its life within each store. This enhanced understanding of product and store behavior commonly leads to profit increases well beyond 4% and into double digit increases in some cases.

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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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The Profit Lab: Is allocating to a plan a good plan?

by Greg Wilson • June 22nd, 2010 • Write a comment »

THE PROFIT LAB // Top 10 Ways to Pull Profit from Allocation

Strategy #6: Update plans with changes in customer demand as frequently as possible

There’s no disputing that having a good plan is important in retail. In the context of allocation within retail – especially fashion retail – it’s common to see adetailed plan as the driver of allocation execution. The philosophy is sound: invest in creating a solid plan that you can simply execute to. Unfortunately few tools enable users to manage detailed plans with the appropriate metrics and frequency to keep up with changes in demand.

In the last two or three years the pace of change in customer behavior has increased dramatically. Over this time the use of last year data has not been a valid indicator of trends, especially when servicing individual stores. Customers are changing their buying patterns regularly and in many cases the entire demographic makeup of shoppers in stores has shifted. The only way to keep up with these variations is more frequent updates to our understanding of customer behavior.

Traditional store planning approaches are not suited to being updated as frequently as needed to keep up with these changes. This is especially true when we are updating detail level plans to drive allocation. Allocating to an outdated plan that doesn’t reflect what demand will be is not of much help when striving to achieve strategic company objectives such as increasing volume, turns or profitability.

The underlying objective of these plans is often to ensure a presentation or image is maintained or to set a capacity ceiling in given locations. This process can often be shifted to (and is often better served in) assortment planning processes. When that objective is accommodated, the remainder of allocation execution must be more responsive than a static, manually managed store plan can be. What can you do to understand and respond to the rapid changes in customer behavior?

What you can do now

The simple answer is to update your store plans more frequently. Much more easily said than done (if feasible at all) within resource and time constraints.

Another option to consider is to change the role of your store plans. If you can limit them to becoming vehicles to define only higher level image, presentation and/or capacity requirements by driving min and/or max parameters, you then may be able to free up your allocation system to interpret the trends within recent activity and weigh them more heavily into the final allocation decision. This is true for both initial allocations as well as in season allocations. It may even be possible to shift the responsibility of defining these parameters into other, existing planning activities such as assortment planning. If that happens, you can free up valuable time to do more analysis and determine superior allocation criteria. While you may still be limited in how reactive you can be, this can enable you to continue supporting brand or lifestyle images while increasing your ability to be more responsive to the constantly changing trends of individual stores and products.

What you should consider when looking for new capabilities

The objectives of maintaining an image while still being responsive to unique store/product demand can often be difficult to balance. Technology has come a long way over the last 5 or so years in its ability to apply more intelligence to defining and solving these problems.

Look for the ability to understand, interpret and execute to changes in store and product behavior at a very granular level. With the pace of shopping patterns changing so rapidly, manual planning and updating can’t meet the objective of allocation anymore. Modern software can define the strategic economic objectives of individual products and allow allocation to maintain an image while still being free to react to the most current reality of customer shopping patterns.

In fashion this means going beyond historical sales activity. As discussed earlier in this series, understanding historical demand is hugely important to making the right decisions going forward. Even store plans created by product that could be updated daily would not be as effective as they should be if they’re based on historical sales rather than demand. Understanding behavior also means gaining insight into the seasonal characteristics of products and stores and understanding the unique selling patterns across the lifecycle of individual products.

<< 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 on 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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The Profit Lab: Forecasting doesn’t work for fashion, does it?

by Greg Wilson • June 15th, 2010 • Write a comment »

THE PROFIT LAB // Top 10 Ways to Pull Profit from Allocation

Strategy #5: Overcome limitations to forecasting by using better data

I know many of you believe, like I do, that there should be no reason for separate systems supporting allocation and store replenishment. Philosophically the objectives of these two systems are exactly the same: Get the product you have available to the stores where customers are looking to purchase it when they expect it.

So why do there continue to be two separate solutions for these very similar processes?

Answer: Forecasting limitations

In over 25 years in retail, with most of my exposure being centered on planning and inventory management processes and systems, I’ve seen numerous philosophies and initiatives come and go. One of the most intriguing has always been attempts to apply the automation existing in many forecasting, replenishment and other supply chain systems to fashion allocation. My memory is littered with examples of attempts and failures in doing this – from both colleagues and personal experience. The few who have claimed success in the past usually measure success as “ability to execute” rather than “ability to achieve better allocation results”.

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 to this 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. My experience has been that while these can do a better job than a traditional forecast, that’s really not saying much and the effort isn’t justified by the result.

So, as retailers, we have adopted an alternative approach, allocation. If we look at allocation conceptually it’s mainly 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 and use those 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 – and based on assumptions and intuition.

Most retailers have long felt intuitively that we can do better, but how?

What you can do now

Since allocation is generally a mechanism to more simply forecast sales and inventory need, short of implementing a new system we must improve the allocation data and calculations. As discussed in previous posts in this series, spending more time selecting the products we use as the base of data can have profound impact on the quality of allocation results. If we spend more time finding the data that more closely reflects the trending, lifecycle, seasonality and historical demand of the item we’re allocating, results ultimately improve.

Often there is also opportunity to improve our allocation calculations. Many existing solutions have multiple calculation choices, and some even allow us to define new calculations. Most retailers fall into a pattern of using just a small number of these (often just one). This is frequently a symptom of a difficult implementation which resulted in too much change to adopt all at once so the simplest options get used. If you have a system that has been in place for months or even years, you’re past the learning curve of changed process associated with your system. Challenge yourself to understand the objective of each available calculation and experiment with them to see if those you haven’t been using can be made to return better results. Analyze the weaknesses of each and if you have the ability to modify or add to them – try it!

What you should consider when looking for new capabilities

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 product and store types and levels. The resulting understanding of behavior across multiple dimensions is used to derive the likely behavior of the product you need to allocate.

With the best of these systems, even though the underlying logic is much more complex execution has thankfully been 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.

Footnote

Replenishment users have long been chasing the elusive “perfect demand forecast”. Interestingly, it turns out that a better forecast is only a small part of getting a better allocation result. In fact taken alone an improved forecast will often have no impact on an allocation result at all. More important than the perfect forecast is how you support it with inventory.

An imperfect forecast can drive a superior result if the decision about how to place inventory in support of that forecast is aware of:

1) The weaknesses that exist in the forecast
2) The objective you are trying to achieve with this product

This will be the subject of an upcoming post to the Profit Lab series on Allocation.

<< 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 on 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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The Profit Lab: The meaning of life… cycle

by Greg Wilson • June 11th, 2010 • Write a comment »

THE PROFIT LAB // Top 10 Ways to Pull Profit from Allocation

Strategy #4: Consider product life when creating allocations

In prior posts we’ve discussed a few things to consider when determining the base of data to use when allocating, including: locations and clusters, products and product groups. Even the best decisions in these two dimensions can be nullified if the wrong time period is selected. This is particularly important when allocating product with short lifecycles like most fashion items.

You already know most allocated products have a distinct life, be it 6 months for a fashion basic or 3 to 4 weeks for a high trend fashion item. Lifecycle exists, but how can we understand and leverage it in the context of allocation?

There are two points when lifecycle can have a significant influence on allocations. In initial allocations, understanding the anticipated life of a product can help you make a better choice of what products to use as a base when allocating. More significantly, however, when there is an opportunity to re-allocate held back inventory or secondary receipts, understanding how a product is actually behaving relative to it’s life can have a huge impact on results.

Product Life cycle at Three Different Stores

Take a look at the chart above. It represents a product and it’s behavior in three different stores throughout it’s full price life (each line is a store representing indexed sales or demand across time). The yellow store took off with this item at introduction but has been falling off ever since (a very “fresh fashion” conscious location perhaps). The blue store built to a peak and has begun to taper off (a typical or core store). But the red store has had a slow build to it’s peak (possibly a “fashion follower” location). If we can understand this lifecycle variation it becomes very apparent that we can make better decisions at different points in time.

If we’re halfway through the life of this product how can we make a better re-allocation decision? At the midpoint all three stores may have sold the same number of units. If we only use ‘sales to date’ as our base, we’ve lost the opportunity to leverage understanding of lifecycle. Both the yellow and blue stores have reached their peak. The red store is still building and has a lot of potential. If we’re re-allocating this product at that point, more of our available inventory should be going to the red store, perhaps some to the blue, but ships to the yellow store will likely result in markdowns, probably deep markdowns before it’s through.

So how do we get to this understanding so we can use it in our allocation?

What you can do now

When constrained by older allocation technologies, your main weapon to use in the fight against lifecycle is your time selection. First and foremost, validate that the time window you are selecting does not include periods of high stock-outs or high markdowns. If it does, it’s not representing the lifecycle potential.   Select product(s) with a similar lifecycle to what you expect from the allocated product for a forward period representative of the period you’re allocating into. In doing this you begin to capture the lifecycle characteristics that will influence product behavior. If re-allocating, try to include the allocated product’s recent performance together with a product of similar volume that lived for the remainder of the life cycle expected from the allocated product if you can find one.

What you should consider when looking for new capabilities

Modern technology allows more advanced allocation systems to constantly monitor product lifecycle patterns within and across products and their lives. This learning about the reality of historical lifecycles can be used as a knowledgebase to apply to new and young items. Understanding of how items behave and how they are trending enables these systems to react to the unique lifecycle characteristics of products within each store so action can be taken on allocation recommendations. This maximizes full priced selling potential, reducing markdowns significantly.

This knowledge can also trigger alerts that notify merchants when products aren’t behaving within anticipated lifecycles. Awareness can open opportunities to either acquire more product (if available) when a product is going to live longer than anticipated resulting in missed opportunity – or to accelerate markdown plans when a product is going to reach end of life sooner than anticipated leaving too much excess inventory.

<< 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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The Profit Lab: You’re biased, but it’s not your fault

by Greg Wilson • June 9th, 2010 • Write a comment »

THE PROFIT LAB // Top 10 Ways to Pull Profit from Allocation

Strategy #3: Make time to analyze your assumptions for like products and see if they were true in the past

In the last post of this series we discussed location clusters in the allocation process. Here we’ll take that a step farther and talk about merchandise grouping.

In virtually all allocation processes it’s necessary to select a product, merchandise hierarchy level or group of products to use as a base of historical data to make allocation decisions. Assuming a constant of source data (historical sales, historical demand etc.) the decision of what products or product groups can be the single most influential factor affecting the results of your allocation.

Choose a base of products that’s truly reflective of the item you’re allocating and it’s likely you’ll get a decent result. Choose a base that’s not and you almost certainly fail.

Ultimately the goal of this process is to select product(s) that have acted in a way that we expect the allocated product to act. In the process of making this decision we use our judgment to make assumptions based on what we believe to be similarities. Since many products we allocate are new, we can’t use the same item’s actual history. Even when we can there is rarely enough activity at SKU / store level to make good decisions. As a result we look for similar items to group together and give us enough data for the decisions we need to make.

In the process of deciding what to group, we tend to think about similarities of products. Similar fabrications, silhouettes, colors etc. We assume that these similarities mean items will act similarly. Sometimes that’s true. More often than most people realize, it’s not true. We’re biased in our assumptions, but we have to be. Unless you are unique in the retail industry, as an allocator you’re not afforded the time to prove out the assumptions you make – you have to execute so often and so quickly that there’s just not enough time to validate every decision for each allocation. So what’s an allocator to do?

What you can do now

Make time to do some analysis. A better allocation puts merchandise that would have been sold at markdown into a store that will sell it at full price rather than being out of stock and missing the sale completely. The truth is that making a better decision about the product base of your allocation consistently can often lead to as much as a 1% increase in revenue and margin. It may not sound like much at first but for a $1B company that’s $10M in sales and somewhere around $3-4M in profit in a typical case. That can quickly pay for a few more people, better technology or other improvements.

Take a day as an analysis day and look at products based on the characteristics you tend to group by. When you look at all blue tops individually, do they sell similarly? Is this true by store or cluster? If so, it’s a keeper criteria; if not, maybe fabrication or silhouette will have more impact as a group. The influencers will be different for different product groups, so take different passes for different product groups. Expect the things you find and results you get to be subtle. Using the better criteria will usually only shift a few packs within a given distribution. When that shift is consistently for the better and it’s multiplied across all stores, all products and for all receipts it quickly adds up to significantly better performance for your company!

What you should consider when looking for new capabilities

When looking at updated capabilities in allocation look for systems that are constantly analyzing product behavior by store. The best systems today are looking across multiple levels and groups of merchandise and location and learning about how each relates to individual product behavior within individual stores. Doing this allows them to do the analysis described above at much more detailed levels and to constantly update the results which become the basis of your allocation decisions.

Done right, results from using technology with this capability can quickly push that 1% gain mentioned above into 2-3% gains. That’s potentially $9-12M of profit per year. Enough to pay for the investment within just a few months.

<< 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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The Profit Lab: Debunking the Cluster Myth

by Greg Wilson • June 4th, 2010 • Write a comment »

THE PROFIT LAB // Top 10 Ways to Pull Profit from Allocation

Strategy #2: Learn the unique behavior of each store before clustering it

Location clusters. We all know them and some love them. They have their place, especially in larger retail organizations. It’s important to think about why they exist before determining when and how to utilize them.

For the most part they only exist to allow merchants to look at data in a way that reflects some localized perspective, but makes it more manageable than evaluating each individual location. This is fine for review purposes, but when it comes time for execution, we need to consider the uniqueness of each individual location.

There are a few different ways clusters are utilized in Allocation

Some of the most common include:

Setting constraints: Min, max, calculations and caps, inventory target ratios, presentation quantities etc.
Making adjustments: When reviewing allocations making cluster or store group level changes to allocation quantities.
When allocating: Sending X number of cases to “A” stores, Y number to “B” stores etc.

There are two realities that generally keep clusters from being effective – especially in the context of allocation

First – Cluster definitions must be created. To keep this process manageable this is executed at aggregate levels of merchandise – either hierarchy levels (categories or classes) or by manually selected groupings. This process involves making assumptions about what products will behave similarly and in the case of using hierarchy levels, forces items known to be different to participate in the groupings. This can be convenient as it tends to smooth or “normalize” some of the volatile activity out of the result. Unfortunately it virtually always colors the answer to the point that clusters have similarities to some of the products you’re applying them to but are not truly reflective of the unique behavior of any of the products you’re applying them to.

Second – Store behavior ALWAYS changes. As a result, no matter when you’ve clustered and how perfect your clustering criteria are, the cluster definition is almost immediately out of date. Conventional wisdom says that there isn’t enough change in a season or quarter to warrant revising clusters. The reality is that unbiased analysis shows us that this assumption is undeniably wrong. The change is constant and relentless. This is especially true in the schizophrenic economic conditions we’ve been experiencing in recent times.

Localization

If you’re company has localization as a goal, you can’t truly achieve that if you’re driving the allocation process with clusters as described above. Clusters can still be a part of a successful allocation strategy, but a process to uncover the unique behavior of each location is the only way to achieve true localization in the allocation process.

What you can do now

If you’re currently allocating by setting the number of cases you send to a cluster, STOP! Even the most technologically challenged organizations can usually find some source of recent historical data by store and build a spreadsheet that can use that individual store understanding to improve results. You can still use cluster level constraints (i.e. min / max) to buffer the volatility that comes from looking at store detail.

If you already have technology that looks at store history and you’re setting criteria at cluster level, evaluate the clustering criteria more frequently. Stores are changing their behavioral characteristics faster than ever before. We need to keep up with the change or results will suffer. Even if your system dynamically generates clusters based on indexes, the assumptions that went into those index definitions have probably all changed – and they’re different for different merchandise. Put the time into updating them.

What you should consider when looking for new capabilities

Modern allocation systems are capable of learning much more about how individual stores and products behave. In addition to considering demand rather than just historical sales (as discussed in the last post in this series), these systems understand a variety of levels that have been proven to influence the behavior of products within locations. They constantly monitor that behavior and utilize it to set store specific objectives, eliminating much of the need for clusters from the Allocation process. Clusters are still used to set some constraint criteria, however the individual store awareness means that this criteria is less critical to achieving the desired results. The range of these criteria is often softened (e.g. reducing mins and increasing maxs) or in many cases eliminated due to the fact that the detailed store understanding accommodates them.

<< 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.

Download this blog as a PDF HERE»

Subscribe to receive weekly updates of this series HERE»

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

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The Profit Lab: Demand, Demand, Demand

by Greg Wilson • June 1st, 2010 • Write a comment »

THE PROFIT LAB // Top 10 Ways to Pull Profit from Allocation

Strategy #1: Understand your demand to achieve better allocations

The conventional allocation process allows you to select a base of historical data to either use as a base of allocating, or to create a plan to allocate into. The assumption is that the data will reflect the way products behaved in those stores and that the pattern will repeat itself.

The true objective is to fulfill as many of the customer expectations regarding the allocated product in a given location as possible. Unfortunately historical sales fail to represent those customer expectations. This is because sales have been limited by the amount of inventory a store received. When a customer wants to buy an item and it’s not there, it’s a lost sale. We typically refer to the customers’ expectation as demand. The difference between demand and actual sales is lost sales.

If we are to do a better job of fulfilling the customer expectations, we have to allocate to demand rather than sales. If we don’t, we’re invariably creating self-fulfilling prophecies. If sales is less than demand we’ll only fill back to sales potential rather than demand potential. We’ll never capture the demand. The result… missed opportunities!

What you can do now

The key to understanding demand is accounting for situations where demand is missed. When products run out of stock there is exposure to missing sales opportunity. Short of creating complex logic to accurately assess missed opportunities, some pre-analysis of your data may lead to quick improvements.

When selecting your base of history, take a look at situations where stores reached 0 inventories. If you’re looking at a group of items, look for unexpectedly low store level inventories. Limit the time period you’re referencing to a range where low or out of stock situations haven’t had a chance to become relevant. While you may miss some trending by doing this, you’ll almost always improve the understanding of relative store selling and thus improve your allocation results.

What you should consider

If you’re considering investing in new allocation capabilities, insist on – no demand, demand! Without understanding demand you’ll consistently miss opportunities to improve your allocation results and therefore your company’s results.

All demand is not created equal. If an incorrect plan or forecast is used to derive demand when you have stock out situations, the result can be even worse than not using demand at all!

The best modern allocation systems have the ability to not only create a forecast to fill in demand, but to evaluate the quality of the forecast across multiple dimensions of merchandise and location using the most current data BEFORE using it to derive demand.

We’ll be taking a closer look at forecasting related to fashion allocation later in this series.

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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.

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

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Welcome to The Profit Lab

by Greg Wilson • May 29th, 2010 • Write a comment »

THE PROFIT LAB // Top 10 Ways to Pull Profit from Allocation

Introduction

Thank you for your interest in this series. Allocation is one of the most often overlooked areas to optimize retail and gain profit margin. I will be taking you through some successful strategies that will help you find the dollars, understand your customers and achieve efficient and strategic allocation execution.

- Greg Wilson
Director of US Field Strategy, Quantum Retail

Allocation – Your Best Opportunity to Improve Revenue and Profit

With the economy changing the way consumers are behaving and the pace of change accelerating all the time, it’s become much more challenging to get the products our customers are looking for into the right locations. What can retailers do to improve our ability to meet the expectations of our customers better in such a dynamic environment?

Large retail merchants go through a series of product activities in the process of fulfilling their customers’ expectations.

These include:

1. Selecting

2. Ordering

3. Allocating

Which of these three processes can be improved most?

Incorrect assumptions

Selecting product is often the first area retailers assume we should work on. This is usually due to the fact that it’s generally the starting point of in the retail lifecycle and selecting good products is a key factor in success. Ordering the right quantity typically follows when using this time-line based logic. Unfortunately, the allocation of product to stores is often relegated to being the last area given attention. However, this critical component of success is imperative and when it is overlooked, retailers risk failing even when everything else is right.

A significant impact on store and product performance

Allocating product is our last chance to impact what our customer has to choose from. When we’re making allocation decisions we’re making as many decisions as we have stores, for every receipt of every SKU that’s carried. That means hundreds, or thousands of decisions per product, each of which can have a significant impact on how well products and stores perform.

Get these decisions right and you can maximize returns for good products and reduce the pain of the inevitable poor products. Get them wrong and you can stifle the potential of good products and turn poor products into devastating losses.

Get the most out of your merchandise

Unlike the heavily artistic side of product selection, allocation decisions are one of the best points to leverage data and apply analysis to getting the most out of your merchandise and stores. Better understanding of product, location and ultimately customer behavior is an invaluable foundation to support improvements not only in allocation, but also in ordering and assortment decisions as well.

There are multiple process steps and related decision points that can be improved upon in allocation:

Traditional allocation systems allow you to select a base of historical data to use as either a foundation of allocating, or to create a plan to allocate into. Allocators select the products, locations and times to consider. They also choose calculations to apply and constraints to impose. They then review system-generated results and make changes. Each of these process steps and related decision points can be improved upon in any environment.

Over the next 10 weeks, 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.

Next post in series >>

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


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.

Get resources on how to adapt to today’s retail market HERE»

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Grocery Innovation Series: How to target products based on consumer buying behavior

by Vicki Raport • May 27th, 2010 • 1 Comment »

GROCERY INNOVATION // week 4

Precision assortment equals more profit

This series will be published Thursday’s and will review trends, tips and technology to optimize grocery planning. To sign up for series updates – CLICK HERE»

If you precisely target the amount of choices you offer per product, reduce overstocks and markdowns, and ensure that your assortment meets and does not exceed the needs of each of your stores, you will ultimately reduce wastage and increase sales.

“Retailers find they sell a lot more of nearly everything by reducing the number of brands on offer; but figuring out what should stay and what should go can be a tricky business.”

In an intriguing study on the impact of reducing product choices, Wal-Mart found that in many cases less is more. Marina Strauss, Retailing Reporter for The Globe and Mail tells the story of one such product Wal-Mart targeted:

Several months ago Wal-Mart Canada Corp. decided to overhaul one of the staples of its grocery business – the peanut butter aisle.

It dropped two of its five lines of peanut butter to free up scarce shelf space for cinnamon spreads. But the decision didn’t cost the retailer a single jar in sales. With fewer selections to browse, customers wound up purchasing more than before.

“Folks can get overwhelmed with too much variety,” said Duncan Mac Naughton, chief merchandising officer at Wal-Mart in Mississauga. “With too many choices, they actually don’t buy.”

Many retailers are now reducing the amount of choice on their shelves in order to simplify their offerings. The recession has changed consumer behaviors and encouraged retailers to focus on top sellers and private labels.

Strauss reports that by focusing product lines, retailers can trim costs, reduce consumer confusion, and ultimately boost sales. Reducing the number of products can help companies increase sales by as much as 40% while cutting costs by between 10 and 35%, according to a 2007 study by consultant Bain & Co.

Rationalizing an assortment is difficult. Retailers need to have a keen sense of product performance in order to pick the right products. According to this Globe and Mail report, “Evidence suggests that reducing the number of products on the shelf can improve the overall shopping experience. The average shopper takes just 2.5 seconds looking for an item and notices only half the products on a shelf,” according to research by Procter & Gamble Co., the consumer products giant.

Optimizing sizes and rationalizing products:

In order for retailers to target the right range of products on their shelf, they need an acute awareness of product behavior. There are dozens of product behaviors unique to every store. As well, product behaviors can be unique to customer segments. In order to analyze these behaviors, retailers should look at the performance of package size, brand, value, locality, and flavor as well as things like price points, life cycle, overstocks, under-stocks, amount of markdown, etc. What do these metrics tell you about your assortment of products? How do those metrics change across your stores? How do these products support your customer segmentation and brand strategies? Which stores have similar product behavior? What attributes do those products have in common? How often are you discounting those products?

One of the best ways to analyze these behaviors is to look at the profitability of each product at every location. Do not cut your assortment across your chain, but look at the unique selling patterns at each store to determine what products will sell to their unique customer base. This is a complex exercise, but one that needs to be done on a continuous basis. Your customer’s buying patterns will change – and it is necessary to acknowledge they have already drastically changed.

Consumers Adopting New Behavior to Save on Food

So what are the consumer behaviors that are affecting your sales? The Food Marketing Institute reported the following changes in grocery shopping trends:

Shoppers are economizing when it comes to food purchases. There are three stages of consumer behavior that have changed:

  1. Stage One: Shoppers save money on eating out by switching from fine dining to fast food. They also seek supermarket meal solutions and prepared foods in place of restaurant fare.
  2. Stage Two: Consumers change their saving measures in the store by buying more private brands, using coupons, buying basic ingredients, focusing on full meal deals, and shopping with a plan.
  3. Stage Three: Shoppers switch store formats and choose venues with focused or limited assortments, including superstores, warehouse clubs, and private label food services.

A majority of consumers (69%) surveyed in the study say they are eating out less. An additional 50% said they are eating out at less expensive places. All point to a significant shift in the expectations that consumers have for service and assortment from their food and grocery retailers.

The survey also showed that when deciding how to save money on their grocery bill, consumers are making plans before heading to the supermarket resulting in fewer impulse purchases. In fact, 53% say they make a shopping list, 40% search newspaper or advertising inserts, and 35% responded that they look for coupons in the mail, newspapers, and magazines.

Private Label Brands Should Become a Priority in Product Assortment Targeting Efforts

The FMI found that the effort to save money continues once shoppers are in the store. The report stated that the popularity of private brands has significantly grown, with 97% of shoppers saying they plan to purchase the same amount of private brands or more over the next year.

The following chart from the FMI report, shows consumer responses on private labels:

The shift of focus to private label brands is a logical choice for retailers. The following diagram from the FMI shows how consumers rank their product choices. Today, price is the most important factor in their buying decision followed by quality. When private labels succeed, it shows that customers are more interested in the product than the brand itself. This has caused retailers to stretch the reach of their private label brands, leveraging the appearance and placement of store-brand products.

FMI reports that some retailers are conducting in-store comparison tests to measure shoppers’ preference for store brands versus national brand alternatives. Words associated with private products in the minds of consumers include “quality,” “value,” “cheaper,” and “inexpensive.”

“Shoppers view private brands as a value-added offering in tough economic times.” - FMI

Technology to assist in product rationalization and give insight into product performance

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
  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

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.

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 customer engagement approach gives your team access to the solution from early on, so you can manage changes to your processes with ease. Quantum Retail continues to help all of its clients rapidly drive measurable and significant business value through our proven merchandising optimization solutions.

Get resources on how to adapt to today’s retail market HERE»

Learn more

Follow this series to learn more innovative practices for grocery.

To download as PDF CLICK HERE»

To sign up CLICK HERE»

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