LONDON & MINNEAPOLIS (BUSINESS WIRE)

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

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

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

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

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

About Matalan

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

About Quantum Retail Technology, Inc.

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

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

Read more about Quantum Retail»

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

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

Physical constraints

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

Operational constraints

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

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

What you can do now

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

Physical constraints

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

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

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

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

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

Operational Constraints

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

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

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

What you should consider when looking for new capabilities

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

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

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

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

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

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

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

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

What you can do now

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

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

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

What you should consider when looking for new capabilities

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

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

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

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

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

Some questions that retailers should ask themselves:

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

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

What and Where:

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

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

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

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

How Much and When:

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

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

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

Putting it all Together

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

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

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

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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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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»

STAR TRIBUNE – One weekend early in 2004, a half-dozen colleagues with long experience in retail management and technology got together with the idea of creating a software product. The goal was to dramatically improve market forecasting and simplify the complex process of inventory planning to meet rapidly changing consumer demand.

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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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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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Download this blog as a PDF»

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

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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.

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

Subscribe to receive weekly updates 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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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.

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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

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