Posts Tagged ‘allocation’

Fashion Forward – Part 4: “One Shot” Allocations Don’t Cut it

By Dan Moran, Product Strategy, Quantum Retail

Are you certain you can only make “one shot” allocations?

One thing that is certain when it comes to predicting fashion trends is that you can count on uncertainty. You can fill the assortment with products that cover all the right sizes and colors, you can set the price that should move and still hit your margin goals. But then uncertainty materializes. How do you really know how much is going to sell by store? How do you figure out how much to ship, how many times you can ship, how many units or packs, and when to send them? The best instincts and even the best system forecasts will always be off by some amount. What can you do to exploit the unexpected opportunities or limit the damage from the disappointing underachievers?

The answer to these questions, as is generally the case in a complex environment, is “it depends.” A lot of factors should be considered, including:

Previous volatility of sell through. If your previous seasons’ performance has included styles that either sold through quicker than expected or lingered with high on-hands well into clearance phases, you have lived with volatility.

Known or expected length of products’ selling lifecycles. More than likely those styles had a limited selling life cycle and a lead time that only allowed for one order or a limited number of reorders from your vendors. A “one shot” or “one and done” approach seemed like the only way to distribute the merchandise.

Current capabilities of your supply chain. If you are constrained by your distribution center to flow through all of your merchandise, or have limited pack choices, or vendors that aren’t flexible with shipments, your current capabilities may be hindering your allocation efficiency.

The economics of your inventory risk. An analysis of the true opportunity cost of missed sales compared to the real cost of marking down and disposing of excess inventory can be an enlightening and valuable exercise.

There are several points through your product’s lifecycle where you can assess your assumptions and constraints and improve your opportunity to increase sales at full price and the resulting profit.

At the point when purchase commitments must be made to vendors for merchandise far in advance of the launch of a new product, a buyer is making a high risk investment. The more information that buyers can apply to experience and intuition, the better the eventual return can be. Historical analysis can provide an understanding of some of the key attributes that customers have needed and wanted in the past. More sophisticated tools can help with modeling more accurate forecasts by evaluating the probabilities of multiple possible outcomes.

As the season approaches, an updated reading of the market trends and the behavior of the product mix in your stores can provide even better context for how inventory should be initially placed. Sales in the first few weeks of the season, coupled with the lifecycle demand patterns of previous seasons can provide a useful basis for a revised forecast for the balance of the lifecycle.

The right data can drive more informed, profitable decisions if you have the capability and flexibility to plan to rapidly react to the latest understanding of your customers’ behavior. A supply chain management concept known as postponement strategy is an approach that delays final deployment of resources until customer demand is revealed and prepared for fast response times. By postponing decisions about allocation quantities to stores, merchandise can be distributed to locations with the best probability of matching predicted demand.

Before launch, calculate how much to initially buy or what portion of the total buy to initially allocate to cover the first few weeks of sales and the lead time to ship subsequent allocations. A few weeks into the season, calculate the quantities needed to cover the revised forecast to the end of the season. You will get an earlier read on which products you may have over or under estimated initially. You’ll need to work with your vendors to see how responsive they can be or have the ability to hold back quantities in your distribution centers, poised for fulfilling store demand dynamically.

The financial justification for postponing allocation distributions to stores comes from calculating and comparing the cost of under-buying compared to overbuying inventory quantities. In an under-bought situation, the potential downsides include being out of stock, lost sales, lost gross margin and varying degrees of customer disappointment. These are opportunity costs that are not easy to precisely measure and often do not get much visibility or scrutiny, but missed margin opportunity can be of significant value. When overbought, there could be costs incurred to transfer inventory between locations, inventory holding costs, costs to dispose of discontinued products or markdown costs if the markdown sale price falls below the purchase cost. These costs are easier to measure and often get more visibility and scrutiny than the less tangible opportunity costs.

In many cases, the value of the lost margin associated with an under-buy is greater than the cost of disposing of excess inventory and it is worth chasing that profit. It can be worthwhile to evaluate alternatives that may incur incremental costs to capture that customer demand – whether that is additional ordering costs, paying for air freight to shorten lead times, or extra internal distribution center costs to manage holdbacks.

There are actions you can take to manage the uncertainty inherent in fashion and shifting preferences of your customers. Buying or shipping less to stores initially and chasing the profitable products and stores in subsequent shipments will provide a great return on your efforts. When the measures of your success are reflected in improved sell-through at full price and a high service level that incorporates the percent of customer demand fulfilled, you can be more certain that you are keeping your customers satisfied.

Look out for the next blog, on Linking Business Intelligence with Strategic Execution.

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Fashion Forward – Part 3: Optimizing Size and Pack (2 of 2)

By Ziad Nejmeldeen, VP of Science, Quantum Retail

Pack Optimization

As previously stated, Pack Optimization refers to finding the optimal configuration of one or more packs that should be utilized for a style-color. This often seeks to balance the pack’s ability to meet store/item demand with the increased handling costs of having too many pack quantities or large quantities of “eaches”, which we will refer to as Loose.

Several U.S. and Canadian retailers have estimated that the handling costs from vendor to DC to store is on the order of $0.25 – $0.50 per pack; this is true whether the pack contains 1 unit or 10. So, we want packs to contain as many units as possible. However, large packs introduce allocation inefficiencies as we are now forced to put a pack into a store to meet the demand for some sizes, even if it means putting in too much inventory of another size. So, we want the packs to contain as few units as possible. By creating a profit function that includes both of these effects, we can find the right pack size that maximizes overall profit.

There are various approaches used to accomplish Pack Optimization; we describe them here in order of complexity.

Single Pack (Bulk): Some retailers choose not to use packs that contain multiple sizes (Ratio Packs), instead relying on Bulk Packs that contain quantities of the same size. In this case, the shape component of the pack is a moot point. However, we still need to determine the volume. In the simplest case, this is a given value (or tight range of values) based on vendor or operational constraints. A more sophisticated approach relies on a data driven exercise to balance the pack’s ability to meet store/sku demand with the increased handling costs of having too many pack quantities.

Single Pack (Ratio): The shape is driven by the optimal size profile associated with the size-range and merchandise area of the item. Identifying the volume is similar to what we described above in the bulk case. The one additional complexity here is that the recommended ratio pack is also dependent on whether bulk or loose will also be utilized. For example, if we do not have bulk or loose, we are more inclined to choose a pack volume that leads to minimal shape distortion; this distortion can occur after the pack volume is multiplied by the size profile and the quantity associated with each size in the pack is rounded to the nearest whole number.

We now describe several different cases that are usually all referred to as “Multiple Packs”.

Multiple Packs (Single Pack in Store Group): The case described here allows a style-color to be ordered in multiple pack configurations, but each configuration is earmarked for a group of stores. In essence, each store group is dealing with the Single Pack case for the style-color, but the style-color can have the pack configuration vary across store groups. This problem is tackled by clustering the store size profiles to create store groups. Each group has an average size profile, and we proceed for each store group using the Single Pack case.

Multiple Packs (Launch vs. Replenishment): An item may have two types of packs that can both go to the same store, but not at the same time. This is often the case when we have one type of pack allocated at the beginning of the season (Launch or Initial pack) and a second, usually smaller, replenishment pack allocated after the item has begun to sell. We may again have operational constraints dictate the sizes of the two packs. Alternatively, we can employ a similar data-driven logic as described in the Single Pack case, but with the Initial (e.g. first 6 weeks) and Replenishment time periods broken out.

Multiple Packs (True Multiple): The most complex scenario allows for multiple pack configurations to exist for an item, with the multiple configurations eligible for allocation to the same store at the same time. Given a range of stores with different volumes and with different size profiles, it is unclear whether we should use packs that a) have the same shapes but different volumes, b) have the same volume but different shapes, or c) something in between. Answering this problem requires running a simulation that assesses packs in combinations. Each combination is measured on both its ability to meet store demand and the handling costs involved. Even the simplest two-pack case can be computationally intensive depending on the number of stores analyzed.

Order Planning/Warehouse Replenishment

Suppose we have completed Size and Pack Optimization, so that we have an entire library of size profiles defined for different size ranges, merchandise areas, and locations. Additionally, we have specified the different pack types (ratio or bulk), have settled on their configurations (shape and volume), and have made a decision on whether or not to allow loose stock. Now comes the problem of cutting a purchase order: How many units of each type of pack should be ordered? How much in loose?

We begin with a pre-season forecast of demand at style-color/store. This forecast can stem from a number of sources; e.g. like-item(s), or a spread-down of a buyer/planner’s chain forecast to store using store weights / volume groups. We can now use our size-profiles to spread the forecast further and arrive at a pre-season estimate of size/store demand.

We can now simulate an allocation to find the quantities of packs and loose that balance the value gained from fulfilling size/store demand (100% loose is best) with the cost of pack handling (100% of largest pack is best). The simulation returns the pack quantities that we wish we had if it was time to allocate today; these quantities constitute our order.

Note that this simulation is a bit more involved if we have both launch and replenishment packs since we will need to separate size/store demand into a launch and replenishment period and evaluate the two separately.

Allocation

At the time of allocation, the pack quantities have been received in the warehouse and are ready for distribution. We can now repeat the same process employed in cutting the purchase order to arrive at an updated size/store demand estimate.

If the initial allocation quantity is pre-determined (e.g. all launch packs, or fixed % of inventory is to be allocated), the allocation problem is a simplified application of the same logic employed when the purchase order was determined; we know what we want to push, we know the size/store demand, and we try to marry the two up as well as we can. On the other hand, if we are also optimizing the initial allocation quantity, the problem is a bit more complex; we must determine the appropriate amount of inventory required in each store to meet expected demand, and the right amount of buffer inventory to meet unexpected demand.

As we continue from initial allocation (before we have observed the item sell) to replenishment (after it begins to sell), we gain some important pieces of information. First, we have observed size/store demand, which we can use to improve our forecast of size/store demand; note that it is at this point that we cease to rely on the size profiles that were so critical in determining the pre-season buy and initial allocation. Second, we have observed the variability (spikiness) of demand within each location; this goes a long way in determining how much buffer inventory we need to capture unexpected demand – the more variable the demand, the more buffer inventory required.

At this point, the remaining packs can be evaluated for the profit we expect them to generate in each store based on the store’s size demand and underlying variability.  If we have a combination of both packs and loose available, we can factor in the pack handling costs into the profit calculation (this makes packs more attractive than loose), or we can rely on a pre-set preference for what gets replenished to stores first (e.g. packs, then loose).

Conclusions

It is our sincere hope that the concepts in this blog help to inform and drive improvements in the management of sizes within fashion retail. We conclude by revisiting what we believe to be the three most important ideas related to this topic:

  1. Size Ranges: Grouping products together with disparate size needs under the same size run will lead to a size profile that does not fit any item well, regardless of what care is taken in estimating demand. Use product attributes wisely to create meaningful size ranges and you can avoid this problem.
  2. Multiple Packs: There is a disconnect if you are looking for a pack solution that gives you multiple pack configurations, but you do not possess an ordering/allocation solution that can utilize multiple packs. In this case, consider the feasible alternative of having multiple packs, but with a single pack per store group.
  3. Demand Variability: When replenishing packs in season, it is important that you not only consider the observed demand in each location, but the variability of demand as well. This can change the amount of inventory that is desirable in the store, and you may now prefer replenishing a larger pack to a store in place of either a smaller one or loose stock.

Look out for next week’s blog on the benefit of multiple allocations and hold-back stock next week.

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Fashion Forward – Part 2: Optimizing Size and Pack (1 of 2)

By Ziad Nejmeldeen, VP of Science, Quantum Retail

Size and Pack Optimization (SPO) is particularly important to the fashion industry. Getting size and pack correct is the key to serving your customers well, and ensures that you get the most dollars out of your inventory investment.

Size Optimization finds the correct ratio of sizes to carry in each store, based on observed size-store demand. Within fashion, the ratio of sizes is defined across a style-color. If “Store” is too granular given existing technology and processes, stores can be clustered as part of the Size Optimization work.

Pack Optimization is often used as a general term to refer to one of three very different processes:

  1. Pack Configuration: this finds the right pack configuration(s) to be used in ordering from a vendor.
  2. Pack Ordering: this finds the optimal amount of each (pre-configured) pack (and possibly loose/bulk) that should be ordered.
  3. Pack Allocation: this optimally allocates packs (and possibly loose/bulk) to stores given known quantities at the DC.

Unless otherwise stated, any reference to Pack Optimization in this document implies Pack Configuration Optimization.

We will be making use of several terms, some of which have yet to become entirely commonplace; some definitions should help:

  • Pack (aka Prepack): A combination of several sizes belonging to the style-color; such a pack is sometimes also referred to as a Ratio Pack
  • Bulk (aka Bulk Pack): Multiple units of the same size
  • Loose/Eaches/Bin: Single units of individual sizes
  • Size Run: The “run” of sizes over which we are optimizing. For example, XS – XL vs. S – L, 2-16
  • Size Range: This is often used interchangeably with Size Run. However, we use Size Range to specifically mean a Size Run that has been combined with attributes of interest. For example, XS-XL Slim Fit or 2-16 Light Colors.
  • Size Profile: A percentage associated with each size in the Size Range such that the sum is 100%; the output of Size Optimization are Optimized Size Profiles
  • Demand: What could have sold if inventory was fully available; it is the ratio of demand across sizes within a size range that yields a size profile.

Relevance in Fashion

In this blog, we will focus on how SPO influences decisions pertaining to Order Planning/Warehouse Replenishment and Allocation. However, we note that there are several peripheral uses of SPO outside of these processes. For example, range planning (determining eligible stores within the assortment planning process) can utilize SPO to determine the categories and stores where we can increase/reduce fringe sizes.

Order Planning/Warehouse Replenishment: At the time a purchase order is placed for an item, Size Optimization informs future store/size (relative) demand while Pack Optimization specifies the pack configurations that should be utilized. These are both essential to creating a purchase order, but they are not enough. We additionally need to “simulate” the allocation of these packs to meet store demand in order to determine the specific quantities of each of the packs and/or bulk that should be ordered; more on this next week under “Order Planning”.

Allocation: When we initially allocate prior to observing in-season demand, the results of Size Optimization are used to spread a pre-season demand estimate from style-color/store down to size/store. This is critical in Initial Allocation when stores are given a specific distribution of sizes to meet anticipated demand. While Pack Configuration Optimization is not relevant in this process (the pack configurations are already specified), we will require an intelligent Pack Allocation process; the lack of such a process can severely limit the options (and therefore value) available in Pack Configuration Optimization.

Size Optimization

Demand: A key input into creating Optimized Size Profiles is proper demand estimation. Demand estimation should ideally use historic store/sku/day sales and inventory information to assess the sales that could have been when inventory is stocked out. Basing the size profiles on demand – especially demand prior to markdown – allows us to avoid repeating the mistakes of the past.

Basing size profiles on demand is superior to the traditional approach wherein sales are aggregated over some specified time period. The goal of this approach is to use sales prior to stock-outs; variants of this approach include using:

  • Fixed number of weeks after the item starts
  • All weeks prior to markdown
  • Weeks prior to some % of sizes stocking out

Regardless of which of the above we use, this approach invariably suffers from one or both of the following: i) the product’s life cycle is artificially constricted, with important periods impacting profitability omitted, ii) some sizes may still exhibit pockets of stock-outs over the period evaluated, yielding biased results.

Size-Ranges: Well-defined Size Ranges constitute a second crucial input into Size Optimization. Recall that a Size-Range segments a Size Run by attributes; these attributes can include shape or cut, color, fabric, price-point, season, to name a few. When several attribute options exist, an analysis can be conducted as part of the size optimization service to determine the set of attributes that yields a statistical significance for size differentiation.

Why is it so important to define size-ranges well? Consider the following two alternatives:

  • Poor Size Runs: The Size-Range is first and foremost dependent on the Size Run being defined well. Suppose you are a retailer where some items are carried in Small, Medium, and Large while other items are carried in X-Small, Small, Medium, Large, and X-Large. However, rather than assigning the former to Size Run S-L and the latter to XS – XL, all items are assigned to XS – XL. This is a poorly defined Size Run. Any analysis based on this Size Run will invariably lead to a Size Profile wherein the % applied to X-Small and X-Large is too low; it is left to the reader to think carefully why this would be the case.
  • Ignoring Key Attributes: Suppose we have a Ladies Pants category where all items are carried in Size-Run 2-16. However, some items in this category are classified as Petite while others are classified as Casual. If we make use of these attributes, we find that Petite has more demand in the smaller sizes while Casual has more demand in the larger sizes – we can use this information to buy properly pre-season. However, ignoring these attributes leads us to buy a mixture of the two demand profiles for all items in the category; the end result has us markdown Size 16 “Petite” and Size 2 “Casual” at the end of the season.

Optimization Frequency: An important question to consider is how often we should update Size Profiles. The answer depends on several factors:

  • What is the window of time on when the product is ordered before it begins selling?
  • Are the Size Profiles season-based? For example, did we find that there was a statistical significance in having them vary by quarter, or are they the same for the entire year?
  • Do we believe the demographics of the customer base has been changing drastically?
  • Has a merchandise reclassification recently occurred?
  • If the retailer is not private label, have new labels been carried in the store recently where the size fits may not be the consistent with other labels?
  • Is Size Optimization offered as a service or product? What is the cost of rerunning?

Let us consider the following case study to illustrate how to best determine frequency. We have a retailer who has run Size Optimization at the end of Year 1 based on data in Year 1. The retailer decided that, due an observed increase in demand for larger sizes during Christmas, Size Profile should vary by season; quarter was used as part of the Size-Range definition. Additionally, this retailer must order items for each season 16-weeks in advance (which is relatively short). Suppose we are now in Year 2, and Q1 has just ended; the retailer is assessing whether they want to re-run Size Optimization based on the last 13 weeks of data.

If Size Optimization is run, it will not update Size Profiles for Q2 through Q4 of Year 2.Those size profiles are based on corresponding quarters from Year 1 and will remain fixed; the rerun will only impact Q1 of Year 3. However, the retailer has the option of waiting until the end of Q2 and re-running Size Optimization on Q1 and Q2 of Year 2. Note that at the end of Q2, the retailer is 26 weeks away from Q1 of Year 3, which gives us 10 weeks to rerun Size Optimization and still make the 16-week order window before Q1 of Year 3 begins. In this case, running Size Optimization twice a year is a reasonable option.

Look out for Part 2 of Ziad’s blog on Optimizing Size and Pack next week.

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This Retail Life – Part 2: Insider Stories – Building Confidence for Guitar Center

“The advice I would give to executives who are looking to help their company evolve, and to reach their goals, is to really vet out what partner you want to help you do that, and what types of solutions are really required. And if you can find a solution that is holistic to many of your needs – it really can help the company operate far more efficiently than putting in several different pieces.”

LISTEN TO PART 2:

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Audio Transcript

Thanks to our audience for joining us again this week. I am Dan Brown from Mulberry Marketing, San Francisco, and I am joined with Irene Messier, former SVP of Planning and Allocation at Guitar Center, now Heading up the Product Execution Team at Quantum Retail.

Thanks for having me Dan. I’m glad to be here.

So let’s dive right in to what everyone wants to know. How did Quantum win you over? I understand the words “stroke of genius” mean something you…

Um yes – the phrase has somewhat grown to be very near and dear to my heart. First off – I had known some of the founders of Quantum before they formed Quantum and so when they approached me, I had already started understanding how truly talented some of the individuals were behind the creation of this tool. I was at Guitar Center and I was looking for a very different tool. I have been associated with several different traditional replenishment applications. And what was unique at Guitar Center, was the fact that it was $1,000, and $2,000, guitars, and our vendors were using our forecasts to actually plan their production lines and at Guitar Center, they were certainly significantly the largest player in their industry and the vendors were very reliant on good forecasting. When I got there, there was significantly room for improvement.

So I needed something that would give me “the next good order,” if you will. As most traditional replenishment systems do, they look down at the SKU Loc, they figure out what demand might be, and they look over a certain time horizon and cut it or suggest a purchase order. What I needed in a addition to that, was a vehicle that had some real strength behind order forecasting and demand forecasting across long time horizons. And that was one of the things that was very special about Quantum. I couldn’t find it anywhere I looked. So that was probably the biggest reason I engaged in conversations with Quantum early on.

Actually, one of the partners, Mike Hrabe, bought a guitar from Guitar Center, and once I became aware of the fact that, A. He was trying to speak with me, and B. He had purchased a guitar from Guitar Center, now I was dealing with a customer relation issue! It’s a funny story, it’s a joke between us. But yes, he did bribe me, in some respects, to speak with him, and I shouldn’t say that – he purchased a wonderful guitar, and I think his son loved it.

So they actually found me and when I was speaking with them, and the more conversations we had, what was really truly the stroke of genius, was that, I had, as we peeled the onion back, what I was really beginning to understand was that I didn’t have to go buy a replenishment system and a assortment planning system and a long-range forecasting system – what Quantum was able to offer and what Quantum was able to deliver was going to take probably at least two if not three capital requests and roll it all into one. So that’s where the term “stroke of genius” came from. And actually, the correct acronym is F.S.G, meaning “fantastic stroke of genius.”

The F stood for “fantastic,” ha!

(Laughs) Um, the F was really used for something else, but if anyone asks it’s “fantastic stroke of genius.” Which was really, very, very, special, and it was one of the main reasons and selling points back at G.C. to management, to say, hey we’ve got a chance to knock it out of the park. And it was a staged approach – that’s how we implemented it – but it was always with the forethought of having a very comprehensive solution.

You touched on this briefly – but what were they using before? Do you have any retail horror stories prior to using Q?

Um, I can say this because I was responsible for the team, but, we had a few hiccups there. Before I joined Guitar Center, they went from all the vendors sending product direct to stores, to putting in a distribution center, and when they did that they put in an allocation system which was very good. And they had already arranged their merchandising team with the roles of a planner and a forecaster to complement the buying staff – so a lot of the change, if you will, had already started. Going from a singular buyer doing it all, to a more holistic merchandising team, with skillsets that balance each other out, but what was evident when I got there, is that when we were looking at procuring product, and it was in a monthly cycle, so the vendors got a bunch of orders once a month, well what was driving that, and the methodology behind that was based in Excel and Access, and I was, you know, wow I wasn’t in Kansas anymore that’s for sure.

You know, you’ve got a multi-billion dollar company there, someone pulling things out of Access and working it in Excel, you know there were times when instead of cutting and pasting the receipt quantity that we wanted the vendor to ship, we’d paste our beginning of period inventories! Luckily – the vendors would call and say, “Are you sure you want this much?” Which of course, we didn’t, and they knew it. And that was one of the opportunities that was very visible when I got to Guitar Center.

There were many, many, vendors that were very, very, unsatisfied with the quality of the forecasting of the products demand. And back then – it required a lot of hand-holding and the buyer got involved and the vendors were involved, in fact, the first week I was there, one of our major vendors, actually our largest vendor at the time, spent a week in our executive board room going over SKU by SKU, each SKU’s forecast for the fourth quarter, and that isn’t what the buying team should be doing and it certainly isn’t what the vendor should be doing either. So there was definitely an opportunity to help the existing staff and the developing staff to provide a better tool that gave them a far better answer, a more sustainable answer, an answer that everyone could have a higher degree of confidence in.

So were you already looking for a new school solution? Or did you feel like you became a “change leader” for the company?

Um, yes they had been looking for a solution like Q, in fact there was some money saved aside in their budget to address that. The expectation was – you know, let’s go out and get the basic type of replenishment system, and I was able to communicate – that if all we sold was sticks and strings, that had a very high rate of sale, a very stable demand, and the vendor had a really strong supply chain – that those systems would be outstanding and our search would be over very quickly. But that was not the bulk of our revenue, and it would not address the instability in the long range forecasting because those systems today wouldn’t be able to provide that.

So there was a change agent needed in that respect, that while the teams were somewhat organized we needed to provide a tool, and to create a process such that the output was one that again, people were confident in, and then people could go back and people could do the jobs that they were really employed to do.

A buyer could go out and negotiate with the vendor great costs, a financial planner could do the open to buy and manage the quote un quote checkbook, if you will. And the forecaster really could focus in on doing the forecasting and ordering of the product.

And once we were able to deliver that and people understood, Oh, not everyone has to look at this? We actually got a good team and the team has individual roles and when there was confidence that we could all do our individual role very well, there was a lot of change that came out of that. There was a lot of ability to really leverage the existing staff in ways that we didn’t before, because everybody was so focused on – do we have the right forecasts out there so the vendor can go build the product?

Once you chose Quantum – being their first customer – you must have built a strong relationship from the start – could you talk a little bit about that?

Certainly. Again, I’d known some of the principles before they started up Quantum and I knew how talented they were and still obviously are. What is incredibly unique about Quantum is that everybody just kind of roles up their sleeves a pitches in, and even today, you can have an idea or want to bounce something out and have a shower thought, if you will, and you can approach Mike Hrabe or Vicki or Linda or Chris or Morgan and say – hey I was thinking about this – what do you think? And they really do want to hear and be engaged in how we’re maintaining, supporting, and developing their “baby” and it is very, very, refreshing in today’s environment to work with a company that you can feel that vibe, it’s being a part of something really special and dynamic.

And from way back on when we were the first customer, they were obviously fully engaged with doing things right down to asking us – hey, what’s the process flow going to be? You know – we’ll provide this tool – we’ll have alerts and work flows etc., but how are we really going to use it? Really challenging Guitar Center to think things all the way through – and re-question themselves, and go the extra mile to make sure that when we put pieces in – they were fully vetted, not only from Quantum – but from Guitar Center – and they really work – I think that is one of the key differences with Quantum as a solution provider.

When you provide a solution, if it’s not easily integrated and accepted into either the existing process or the processes as they evolve, then your solution is not optimized. And being one of the first customers of Quantum, I was able to witness first hand the principles themselves – living that attitude – that we are definitely in it together – we are here to provide a solution with you and for you – and we are both going to figure it out as we go. And I can honestly say that approach and that feeling is still evident at Quantum today – which is neat.

When you sat down with Quantum early on – how did you decide on the strategies that you would be using?

You know it’s funny, at a former retailer, I won’t use any names, one of the approaches that they took was to do portfolio management during their business planning cycle. And not every category was a stake in the ground, and not every category was a loss leader, or a traffic builder, so I was very much already accustomed to the approach that when you are planning or doing something in business that you don’t treat everything you are affecting the same way.

So for instance at this past retailer if you were a stake in the ground, you probably got more advertising, you were probably not challenged as much to increase your gross margin return on investment (GMROI), because you were a stake in the ground relative to that company, to that product offering the company had in the industry.

So dial ahead – many, many, years later, I’m working with Quantum Retailer, and one of the very unique aspects of Quantum Retail is that they don’t try and have you treat every item the same way, but you don’t have to turn a thousand dials and dial in everything in uniquely for that SKU. Whether you have 10,000 SKUs or 80,000 SKUs you can’t do it that way, or at least it’s not very effective that way.

So what the strategies allow people to do is to say, okay – I don’t bring items in to my assortment for the same reason, every item has its own reason for being part of the assortment. So because I was accustomed to that thought process already, it was a very natural and easy step to take it down to the item level. With Q, we would assign a strategy to an item, and the system would take that strategy and affect the order processing and the management of the inventory level accordingly. So I didn’t have to tweak a thousand dials to get it right. There were items that we wanted to maximize profit, there were items where we wanted to be more aggressive with sales, there were items that just rounded out the assortment that would maybe not be in every door, so I was more than happy with a little bit of a lower service level to achieve other objectives.

So in trying to get Guitar Center to understand the strategy approach we, at Guitar Center, got the merchandising management team together and we spoke. I said, well why do we bring different items in? And it seems like a silly question to be asking, but it was actually a very, very, good conversation. And the head merchandising, and the vice presidents of each of their areas felt very engaged, and felt as though they were defining how we were going to be managing the inventory flow and managing our business going forward.

So it allowed us to A. Be very surgical at a very high level, at an item level, and B. Allowed an opportunity for the merchandising teams’ executive team to be fully engaged, which was really very powerful.

So the strategies that you chose for each product are automatically kept up by the system – did the automation factor make you nervous, or what is it about that word that scares some retailers?

Well you know, there can be an expression – everybody has a little black box – all these software companies have a little black box. And you put information in, you don’t understand what happens, and then it outputs something and you’re supposed to execute your business on the output. And so for automation, particularly, in the role that I had, SVP of Planning, there and in another company, you’re looked at and you’re responsible for providing solutions that are obviously credible. It seems like a stupid thing to say, but the fear is, well if I can’t explain every single thing the system does and the automation just happens, how am I going to be sure it’s going to do everything I want in every single situation.

And you know what – there isn’t a solution that does everything absolutely perfect in every situation because for example – you could have an item that sells once every 26 weeks and all of a sudden in a month it sells two. And go figure. No system is ever going to forecast that, because it’s an aberration, if you will.

So one of the things that you need to get comfortable with – is you have to make sure that you are putting enough due diligence in, to test enough scenarios and conditions that your people operate under – scarcity of product, abundance of product, variability of forecasting, stores going into remodels, and you know, just everything that you could imagine that might impact the stores ability to sell product or the natural demand of the product. If you’ve done that and you’ve fully vetted that out, then you can say, ok you know what, the, well it isn’t a black box, but you can say that if you have 10,000 SKUs across 200 stores, no human being can do that calculation every single day when they’re doing allocation. Or you know, twice a month, when they’re re-looking at the ordering processes.

You need to have a system that can understand and do the math and the automation at the SKU Loc level, because that’s where it’s really happening. And you need to have faith that the system that’s doing that gives you the ability to – at a higher level – to go in and understand what’s truly driving the applications that are truly running your businesses.

And Quantum allows you to do that. They give you alerts, they give you work flows – you can go in at an item level and understand what’s going on. You know one of the things with Guitar Center very early on – if we were at an X number of weeks worth of supply and it looked like we had enough product out there – we could have been understocked in half our stores and overstocked in the other half of our stores. Well no human being is going to go through or could they, with hundreds and hundreds of SKUs, go through and say ok I’m going to order this product – well let me go through, by store, and see what they need and aggregate that up. It would have taken way too long.

So that automation of understanding what’s going on at a very low level and raising it up and providing someone with what is going on in a digestible format in a format that gives them alerts and work flows to understand so they don’t have to look at every single product. Because the product is operating dead on to the forecast, and the service levels are more than acceptable, there’s no reason to go in and spend a half an hour looking at that SKU. You might have to spend half an hour looking at another SKU where there could have been cannibalization or where there could have been a product entering the end of its life cycle, etc.

So that’s what the automation piece does. People at times want to be able to say that they know every single algorithm driving everything, and you’ll get bogged down in doing that. And once you have the comfort level that the application is going to execute its math, if you will, in a way in which you would expect it to, in a way in which you affirm, and then just take it from there, and then you free up your team to really start doing analysis – you’ve got forecasters going back to buyers saying, hey you know what – this SKU looks like it might be entering its end of lifecycle. Or you know what, this SKU is really taking off – and this is what I’m seeing, it’s tripped an alert three weeks running, lets go in and see what’s happening. And the buying team really starts getting very engaged because they have a growing comfort level in that, wow, I have somebody and some thing and some process that’s empowering me – and I do not need to not invest thousands of man hours doing it – but I have a vehicle and a process and a team that’s going to be able to give me this critical information and they go back and work with the vendors and its just a great cycle that you get into.

And you have a story about a big alert you received, right before the recession, don’t you? Can we hear that story?

It was the “Mother of Alerts,” what happened was – it was August and at Guitar Center in August, every SKU’s fourth quarter analysis is executed and because by that time you need to make sure that everything’s I’s are dotted and T’s are crossed. Well what happened was, from the beginning of August to the end of August, we could see the demand softening across the majority of our SKUs. We were beginning to get alerts across the board that said, downtrending, downtrending, downtrending… And what happened was the forecasted orders, and those orders result from a combination of what the current demand looks like, what your existing inventory already is, plus your future orders that you already have coming in.

And within a very short period of time, Q was saying hey – you need to cut back your orders – and it wasn’t a small amount it was a very large amount. And each week we could go in and we could see it happening and – when I answered one of the first questions – about being a change agent, you try and put a process in place and develop skillsets and put a tool in place that allows people to do their job, right? Well – what Q was witnessing and attesting to and serving up to us was so significant that at certain points in time, when you’re in a company such as Guitar Center you need to say, ok – stop, I brought the the head of merchandising in, the executive vice president of merchandising in, the vice president of merchandising in, and I said guys, look at this – this is substantially different than the forecast we gave the vendors a month ago. And you guys own the vendor relationship, I need you to understand this, and I need you to be a partner in this.

I walked into the CFO’s and the President’s offices and said guys, I believe this is rock solid. I can show you this, vet it, forwards, backwards, sideways, every which way you look at it, it is telling us to take this much inventory out and we had already started seeing softening comps, certainly nothing like what was going to happen in the next couple months, but we were seeing it starting to happen. So we sat down with the executives and the merchant community, with the President and CFO, and we all held hands and said, we think this is right. And it was a significant impact.

The buyers grew to understand and have confidence in those forecasts and went and had some tough conversations with the vendors, but I can tell you this – they were some tough conversations, but they were 1000% better than it would have been, had we not have been so proactive about it. One of the things I said to the CFO who was, as he should be during the whole acceptance process and the selling of Quantum at GC, he was very, very, challenging.

And after we put it in, the first engagement, if you will, we happened to deliver everything that was promised, in fact, the inventory reduction that we said would take two years, it happened it in a year, and he called me a sandbagger! But I went in and I said, you know, I want you to understand, if Q wasn’t in place, and I had a staff of people doing this manually, there is no way I would have suggested the amount of reductions that Q suggested, there is just no way I would have had enough confidence in what people were able to do manually, not going down to the SKU Loc level, looking at it at an item level, nor would the buyers have accepted that. Because, if we were wrong, we were leaving some serious sales on the table. And he kind of looked at me and smiled and I smiled back and thanked him for his time and left his office.

But the point was and the point still is, it was never envisioned, when we were trying to sell this solution to senior management, we made claims for service level, for sales, for inventory efficiencies, but we never, ever, even had the inkling to suggest that if a major recession is going to hit, this is going to see it and it’s going to save our skin. Because had we not done that, we would have ordered more than we needed, and we would have had the stores filled to the brim and the vendors would have gotten very very few orders first quarter, and in that vendor community it would have been significant. It would have been extremely hard to have lived through.

So while the buyers went out and had some tough conversations with the vendors, it at least allowed them to be proactive about it, and to have the vendors feel that they were privy to it as well, that they understood and knew what was happening. And I bet the vendors took that information and probably used it in other client sites as well. So I don’t want to call it a happy ending, because no recession has a happy ending, but it was a very gratifying process to actually be able to manage through some very difficult times.

That’s amazing – so after that experience – after you saw that Q really was dead on – did that instill a lot of confidence in the system?

Yes it did. Well that and confidence is, you know what’s the expression, you make money the old fashion way, we earn it every single day. Well, we, Quantum, and it’s application, really had earned the confidence every single day. Even from the pilot. From the initial pilot, within twelve weeks we were up and running and Quantum was affecting forecasts. And within 4-5 weeks – in a test and control environment – we were able to show that the forecasts that Quantum was just spitting out from the beginning, pure vanilla, were 20-25% better than what a human being was producing. And then time went on, and we trained the allocators to use it, and then we trained some of the key members from forecasting to use it, and then guess what? A trained professional with a better tool gets an even better forecast, so then it jumped as high as 29% forecast improvement.

So even from the very beginnings we were able to show very factual improvements, if you will. Another aspect of it – when we launched the Quantum initiative, we formed an executive committee, which had members from the head of the supply chain, the head of stores, so we got the people involved that would be impacted by this solution, and we had steering committee meetings. And every 4-6 weeks you were out there showing them what’s happening, showing them the differences in the processes, showing them the results, and so – you could really see as time progressed, the confidence level building and building and building.

Then once we were able to assess after the implementation of the phase one, which was delivering the order planning, forecasting, and the changes to allocation and replenishment. Basically, we were able to hit all the metrics that were promised in the analysis that was required to approve the project. The president was new at the time and he was in with the CFO as they were grilling me – and he looked over at the CFO and said okay, okay, she’s proved it okay, it works, it’s done it’s job.

(laughs)

So you know, it was icing on the cake. Again it’s hard to be happy about that recession, but it was another way that people at all levels of the company could understand the positive impact that the solution provided the company. And confidence is a great word. It really continued to solidify the confidence.

Awesome. So I understand that you now work with Quantum, can you talk to us about what that’s like?

Sure. You know I mentioned it, I think a little bit earlier. That it is being part of something special. Because it is not just a software application. It is a solution. It is applicable in grocery, it’s applicable in hard lines, it is applicable in soft lines, there is so much power behind what it can do that you really feel good going to work every single day. But really truly, it’s the people. You know, it’s the founders that are still fully engaged, and still very much care about the output. It’s working with a new person coming in and understanding, wow, this really is different, this really does help provide a different way of doing business. It is being a part of that. You know, I feel as though I’ve been part of it from the beginning, but being on this side of it – it’s a lot of fun. I am very grateful. It’s different for me, you know, I was a retailer basically all of my adult life, and so it’s a different set of shoes, but it is lots of fun and I couldn’t think of a company that I would be doing it with other than Quantum.

And we’re running out of time – but lastly, do you have any advice for retailers evaluating new technology like Q?

You know- often times in fact, I was told this – to go out and find the “Best of Breed” – and “Best of Breed” does not necessarily mean that which has been employed over and over and over again. In today’s changing retail environment, it is absolutely critical that when you implement a solution, it should be a holistic approach. There’s definitely some process changes, some system changes, maybe some people re-alignment changes, and you want to choose a solution that is truly going to be part of the solution and is going to be able to work with you as a partner to find the path that is right for that company, at that company’s point in time of its history.

I’ve worked with companies that were at early stages and really wanted to try and grow market share, I’ve worked with companies that were the largest in its industry and were really sure companies and were trying to focus on the bottom line. And at different parts in a companies life cycle they are going to be focusing on different things, you know, driving sales and growing market share – you need great processes and great people and great tools to do that. Optimizing profit and understanding, you’re mature, and you need to be extremely efficient in everything you do – you need some great people and tools and processes to do that too. And Quantum can help you in both scenarios. Which is terrific.The system is very, very, flexible.

I don’t want to sound like I’m trying to sell Quantum here, but the advice I would give to executives who are looking to help their company evolve, and to reach their goals, is to really vet out what partner you want to help you do that, and what types of solutions are really required.

And if you can find a solution that is holistic to many of your needs – it really can help the company operate far more efficiently than putting in several different pieces. If they’re looking to make a lot of changes, that’s probably something you might want to keep in mind.

Wonderful – thanks so much for joining us Irene!

Thanks for having me.

Tune in next week when we will be speaking to Greg Wilson, Director of Field Strategies at Quantum Retail.

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

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The Profit Lab: Putting it all together

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

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

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

The numbers game

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

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

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

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

Which comes first

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

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

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

The retail world is changing

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

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

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

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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.

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

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?

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?

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.

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

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.

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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 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

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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