Posts Tagged ‘allocation’

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

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

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

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

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

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

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

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

What you can do now

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

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

What you should consider when looking for new capabilities

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

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

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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: Debunking the Cluster Myth

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

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

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

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

There are a few different ways clusters are utilized in Allocation

Some of the most common include:

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

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

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

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

Localization

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

What you can do now

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

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

What you should consider when looking for new capabilities

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

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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.

Download this blog as a PDF HERE»

Subscribe to receive weekly updates of this series HERE»

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

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The Profit Lab: Demand, Demand, Demand

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

Strategy #1: Understand your demand to achieve better allocations

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

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

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

What you can do now

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

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

What you should consider

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

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

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

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

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Learn more

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

Download this blog as a PDF»

Subscribe to receive weekly updates of this series HERE»

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

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Welcome to The Profit Lab

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

Introduction

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

- Greg Wilson
Director of US Field Strategy, Quantum Retail

Allocation – Your Best Opportunity to Improve Revenue and Profit

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

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

These include:

1. Selecting

2. Ordering

3. Allocating

Which of these three processes can be improved most?

Incorrect assumptions

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

A significant impact on store and product performance

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

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

Get the most out of your merchandise

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

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

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

Over the next 10 weeks, we will be deconstructing the allocation process and exploring opportunities to improve within your current allocation processes and technology limitations. We will also review key areas to think about if you are considering investing in improved allocation capabilities.

Next post in series >>

Download this blog as a PDF»

Subscribe to receive weekly updates of this series HERE»

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


Get back in the game //

Are you ready to know exactly what your customers are asking for at every location and to have the ability to react as their wants change? If you are looking for a solution that can drive momentum for your business this year, check out the solutions offered by Quantum Retail.

Our customers see valuable results in 8 to 12 weeks and our implementation approach gives your team access to the system from early on, so you can manage changes to your processes with ease. Quantum Retail continues to help all of its clients drive positive business value more rapidly than anything seen in retail.

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

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

GROCERY INNOVATION // week 4

Precision assortment equals more profit

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

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

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

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

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

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

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

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

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

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

Optimizing sizes and rationalizing products:

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

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

Consumers Adopting New Behavior to Save on Food

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

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

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

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

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

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

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

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

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

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

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

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

In the complex task of SKU rationalization, planners and buyers need the assistance of smart technology that can give visibility to the performance of every product at every store. This kind of technology can quickly pay for itself as it optimizes your offering, reduces inventory, and increases sales.

What to look for in assortment planning and SKU rationalization technology:

  1. A system that continuously monitors business strategies, customer strategies, profitability, service levels, and stock levels
  2. Technology that utilizes the data it takes in to recommend the most profitable assortment for each store, across time
  3. The ability to optimize SKU rationalization by recommending like-product attributes for new products
  4. The ability to take in real-time data and automatically recommend inventory need based on local consumer behavior and store performance

When retailers optimize their product range based on local store demand, stock outs, and customer behavior, they will quickly become more profitable and able to compete in today’s retail market.

Get back in the game

Are you ready to know exactly what your customers are asking for at every location and to have the ability to react as their wants change? If you are looking for a solution that can drive momentum for your business this year, check out the solutions offered by Quantum Retail.

Our customers see valuable results in 8 to 12 weeks and our customer engagement approach gives your team access to the solution from early on, so you can manage changes to your processes with ease. Quantum Retail continues to help all of its clients rapidly drive measurable and significant business value through our proven merchandising optimization solutions.

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

Learn more

Follow this series to learn more innovative practices for grocery.

To download as PDF CLICK HERE»

To sign up CLICK HERE»

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Allocation Localization: A guide to creating a profitable allocation strategy

This is the part 4 of a 4 part series on Fashion Innovation and Optimization. To read parts 1-3 CLICK HERE. Look out for the full report on Fashion Innovation and Optimization next week.

KEY TOPICS IN THIS SERIES:

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

Localization

The term localization is arguably the hottest word in retail merchandising today. Getting the products to the store that the local market wants, in the right quantity, in the right color, in the right size and priced to sell it at the highest possible margin is the Holy Grail of any retailer merchandiser. Adding constraints such as price elasticity, markdowns, supply chain, knowledgeable customers is one thing but when a merchant is constrained by her own internal systems; that’s another problem entirely.

In many cases current enterprise systems and business processes are what constrain retailers from taking advantage of business opportunities. Allocation is an under-appreciated aspect of most fashion businesses today and as such, it’s constraining the work that’s being done getting an assortment localized and priced to perfection. It’s a little bit like pushing a golf ball through a garden hose. There is no such thing as a perfect plan or a perfect forecast and the closer the time-frame to an actual launch of a season of merchandise, the more accurate that plan and/or forecast is going to be. So, why is allocation the merchant’s step-child?

Today, every initiative has to show rapid, significant return on investment. If the current operating model is too restrictive the cost can outweigh the short term benefits and fail to deliver that return on investment. Allocation improvements have the potential to quickly add to the bottom line of any retailer but especially those seeking better performance by localizing assortments.

Creating an allocation strategy

Retail is complex and it gets more complex every day. A changing economic environment, a better educated consumer, global expansion and, as mentioned, localization challenges are at the heart of this complexity. We can’t eliminate these challenges, but we can embrace them and simplify the processes and the way solutions are delivered.

Allocation decisions are the last chance to get breadth and depth of the assortment right. The risks are obvious, too much inventory hurts your margins and too little hurts sales. How can we improve on the process to minimize the risk.

5 tips to creating an allocation strategy

1.    Don’t cluster your stores

This one has perplexed me for years. All this talk of localization assumes that stores need to be managed individually. It’s common for two stores to be of similar size, volume, and be only a mile or two apart and yet have drastically different customers shopping them which changes their size ratios, color ratios, and basic performance on thousands of items. So, why would anyone still cluster stores together, even at a class or department level? Instinctively, you know that clustering and localization are mutually exclusive.

2.    Don’t forget to hold back

Not every decision can be made on profitability alone. Sometimes, your store has to look great first. It’s why the presentation quantity is so often the leading driver in an initial allocation decision.  But, above having a nice presentation or complete offering of size, why allocate significantly above that?  It’s important to cover the expected sales initially but anything above the store’s demand until it can be replenished could be better served somewhere else.

The knowledge of even a day’s or a weekend’s performance by SKU should be enough to re-evaluate the expected demand of an item. Use it! If a store’s lead time is less than a week including the allocation process, why allocate it multiple weeks’ supply?

3.  Don’t allocate too far in advance

Tied closely with the recommendation above, there’s no reason to pre-allocate merchandise and stick to it. Often in order to properly order to expected demand by size, a pre-allocation is a good idea when preparing a production run or order, but once that happens there is no need to stick to it. Stores change constantly so let them and allocate just before the product goes out.

4.    Use more than just history

Most allocation systems only have one tool to perform future sales expectations, past sales. Don’t be constrained by this methodology. There is a lot more to it than just how a past item sold.  When it sold, how much of it was pre-markdown, how did stock-outs affect the would-be performance of an item. Was it different by store or are you looking only at aggregated sales? Knowing what sales “would have likely been” at a store/SKU/day level can critically empower future allocation decisions.

5.    Is there really such a thing as a like item?

Nobody seems to argue that the consumer is constantly changing. Fickle customers tastes change from season to season and often times even more often than that. So, why do we seem to all perform the same function when allocating, that of finding an item in the past that most describes the item I have now? Using attributes, detailed store performance, price sensitivity, store attributes, and demand is much more likely to generate a more accurate outcome than copying last year’s performance. Wash, rinse, repeat? I don’t think so.

Don’t overlook allocation

As an industry we have all embraced the idea that localization, in general, will generate positive results. The obvious place to generate localized offerings is in assortment planning and knowledgeable buying but don’t forget about allocation. The benefits there can be just as large, and more than likely more quickly achieved than any other merchandising process.

Learn how to improve allocation further:

We will be starting a 15-week series on improving allocation. To receive email updates as the series comes out SIGN UP HERE.


Get back in the game

Are you ready to know exactly what your customers are asking for at every location and to have the ability to react as their wants change? If you are looking for a solution that can drive momentum for your business this year, check out the solutions offered by Quantum Retail. Our customers see valuable results in 8 to 12 weeks and our implementation approach gives your team access to the system from early on, so you can manage changes to your processes with ease. Quantum Retail continues to help all of its clients drive positive business value more rapidly than anything seen in retail.

For more information on Quantum Retail’s allocation solutions, visit:

http://quantumretail.com/solutions/allocation-replenishment/introduction/

Download this blog as a PDF

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

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Fashion Innovation Series – Part 3: Replenishment Optimization – Avoiding Markdowns

This is the part 3 of a 4 part series on Fashion Innovation and Optimization. To read part 2 CLICK HERE. Look out for part 4 – Allocation: Seeking profit, a 4-part guide for creating a hold-back strategy

KEY TOPICS IN THIS SERIES:

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

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

Inventory Execution and Replenishment Optimization

Inventory execution and replenishment optimization should focus on efforts to reduce stock-outs through better replenishment and fulfillment strategies. Some stores are out of stock way too soon in the product lifecycle and others are left with far too much stock at the end, which has to be marked down. These are fundamental errors in the fulfillment operation that retailers cannot afford to make, but they happen all too often. The key operation between the initial buying decision and the final end of line markdown is in inventory execution – managing the supply of goods to minimize out of stocks, lost sales and overstocking.

If markdowns are up – Your Inventory Management system is down

Inventory management systems have helped retailers to improve in this area of inventory imbalance, but the continued use of significant markdowns suggests that things are not getting any better for retailers.

In fact, there are two separate areas where better decision making is required:

  • The initial purchase stage – deciding how much product the retailer needs in total
  • Distribution – how and when to allocate that quantity across stores and channels

Markdowns are often a fix for things that did not go to plan earlier in the product lifecycle, so improvements in product planning and inventory execution to reduce excess inventory will have a marked impact in reducing the need for markdowns and maximizing profit. Many of the mistakes being made at the product planning and inventory execution stages are as the result of simplification – aggregation of data and assumptions across multiple stores – which rides roughshod over the variability of customer profiles and demand from one store to another.

A fashionable downtown store in a major city may need a stock richer in traffic generators and high value image items, whereas an out-of-town store in a low income area may need its mix of products to be higher in value items. Fashion retailers have the added complexity of garment size, which means that they need to have a different mix of sizes too, depending on the stores location.

Most of the technology being deployed today to optimize the productivity of inventory is designed to operate at the end of the product lifecycle and is focused on price. Of course the end of the lifecycle is the time to execute markdown strategies, but in fact the most effective and profitable strategy is one based on the whole of the product life and also focuses on inventory.

Product Lifecycle management

There are three key points in the lifecycle of any product where the retailer needs to make the right decisions in order to control demand, price and profitability.

These are:

  1. The initial buy, including packaging
  2. The re-buying and distribution of the product throughout its lifecycle
  3. The pricing of the product, including markdowns

A holistic approach is recommended for managing the complete lifecycle of a product. There are a few key points that most people can agree upon:

  • Understand customer demand
  • Marry the art of merchandising with the science of execution
  • Learn and build knowledge
  • Track and react to product performance

The key is to understand customer demand at the micro or store/product level. Maximizing profitability depends upon knowing what customers wanted and when, not just what you sold.

Stock smart

Markdown Optimization has become all the rage of retailers and retail technologists, but what is a markdown and how should we optimize it? A simple definition is a reduction in price, or the amount by which a price is reduced. To mark down is to alter price in order to raise demand. At one time retailers called this exercise ‘clearance’ and marked down the price of their goods just once a year, if ever. That was in the annual sale, a time when demand was low and the retailer wanted to clear excess stock in order to make way for new products.

Today markdowns are a continuous process for the retailer. Clearance sales are seldom annual events. They may be seasonal, and in the fastest moving retailers – fashion in particular – the retailer may choose to mark down items literally every week.

5 tips to avoid markdowns:

  1. Determine the role of every product In the overall assortment and at an individual store level. Have the power to execute the inventory allocation process with a strategy necessary to meet that role.
  2. Understand the type of stock needed at every location by building better clusters or achieving store specific inventory allocation.
  3. Optimize inventory execution so that you have optimal stock in higher traffic stores and avoid overstocking lower traffic stores. You need to understand your current and forecasted customer demand at the store level and convert that into the best stock distributions, considering pack constraints.
  4. Follow a fast fashion model where product lifecycles are shorter. Constantly rotating inventory, especially in fashion keeps your store fresh and gives the customer something new to see.
  5. Create a holdback strategy. Do not push all of your inventory at once, wait to see what sells. Release inventory to high traffic or trend leading stores first to get an idea of consumer interest before allocating to all stores. Retailers can also release their assortment online to see what customers are buying – this will allow you to save on production, distribution and purchasing costs because you will have a much more accurate understanding of what products there is demand for and which products will actually be profitable.

A holistic approach

A new holistic approach to retailing integrates merchandising and fulfillment processes while managing and reporting on inventory from the store-level up, in real-time. It provides merchandising plans, goals and strategies that directly drive product fulfillment. This allows the fulfillment process to be driven by a bottoms-up view of item behavior, fused with plans, goals and strategies. Real-time performance analysis enables a rapid response if a product or location is failing to achieve its goals or has the ability to exceed them.

This concept derives trends from relatively short and recent learning to make accurate predictions of future behavior and drive decisions that maximize inventory productivity. It is unlike traditional ’number-crunching‘ approaches that rely on interpreting trends and forecasts based on huge pools of historical data. As a result this method of analysis has the flexibility to respond in real time and at a much finer level of detail (store level) than would conventionally be possible.

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Managing Markdowns: Why prevention is better than the optimization cure
Dr. Linda Whitaker, Chief Scientist, Quantum Retail

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Get back in the game

Are you ready to know exactly what your customers are asking for at every location and to have the ability to react as their wants change? If you are looking for a solution that can drive momentum for your business this year, check out the solutions offered by Quantum Retail. Our customers see valuable results in 8 to 12 weeks and our implementation approach gives your team access to the system from early on, so you can manage changes to your processes with ease. Quantum Retail continues to help all of its clients drive positive business value more rapidly than anything seen in retail.

For more information on replenishment, markdown optimization and allocation, visit: http://quantumretail.com/services/markdown-exit-management/

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

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Fashion Innovation Series: Part 1 – Size Optimization & Pack Optimization

This is the part 1 of a 4 part series on Fashion Innovation and Optimization.

KEY TOPICS IN THIS SERIES:

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

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

If you’d like to be emailed PDF’s of this series as they come out, make sure to sign up for the email series updates! (we will only send you email for our retail series reports)

In fashion retail, Size and Pack Optimization are key

Local demand changes at every store on a daily basis. Clustering stores together by store size and geography might simplify the process, but is inefficient and does not take into consideration individual store patterns for size, color, style and quantity of local demand and product preference.  Retailers need to monitor the changing demand at every store to align their assortment in the way that is most profitable and aligned to their strategic objectives.

It sounds like a no-brainer, but when supply chains become complex, retailers cannot keep up with store level demand and will send the same amounts of every product to similar store types. However, localization of store level assortments and order plans is proven to increase availability, full price sales and customer satisfaction. It is also proven to reduce overall inventory, wastage and markdowns which all erode margin.

Understanding how a product will sell through its entire life on a location by location basis – is essential for:

  1. Meeting sku/store demand: i.e. avoiding missed sales opportunities
  2. Reducing sku/store over-allocations: which would otherwise be dealt with through markdowns
  3. Minimizing handling costs: as the inventory makes its way from vendor to warehouse (where applicable) to store
  4. Reducing Markdowns: by having the appropriate level of inventory and the best assortment possible

The initial assumption of the product assortment is an important part of the process. Retailers need to know what is selling where and why, they need a strategy and goal for why that product is in their assortment and they need to make sure they can  continuously re-evaluate how they expect the product to sell – in real time. This enables retailers to understand which stores will offer the greatest potential for full price sales – and appropriately decide what inventory is best and where.

When they can pinpoint the demand at their stores – they will cut distribution costs and decrease lost sales. With the ability to assign specific pack sizes will also help retailers get the exact amount of inventory to every store and reduce markdowns.

Get the right product in the right place and fulfill based on product performance //

The objective is clear: get the right product in the right place to start with – then fulfill based on how products are really performing at each store – giving the product the best chance to sell at full price and identify when and where markdowns are truly necessary.

Size, pack and prepack innovation for progressive retailers

Size Optimization uses historical sales and inventory data at the size/store level to infer historical demand, and then aggregates demand across groups of items and/or locations. Items are grouped according to the size run, attributes of interest, or merchandise classification that they share. This aggregated demand, when normalized across the sizes that compose a size-run, yields a Size Profile. These size profiles can be used pre-season to impact the size buy for the chain, or in-season to impact store-specific size allocations.

Prepack Optimization
refers to the pre-determination of  prepacks that contain fixed quantities of each size in the size run. Like size profiles, prepacks can be defined for groups of products where the grouping is defined by size run, specific attributes, or a common merchandise classification. Unlike size profiles, prepacks are not store-specific – a given pre-pack can be allocated to several stores, if not the entire chain.

In the most trivial cases, Prepack Optimization can be considered a by-product of Size Optimization. Suppose that we want an n-pack solution, have designated that each store should only receive one type of pack, and have pre-determined the approximate number of units in a pack. Then, we can cluster store-level size profiles into n clusters, and use each cluster size profile to determine the optimal cluster pack by multiplying the size profile by the pre-determined number of units and rounding the resulting size units to the nearest whole number.

However, pack optimization becomes more interesting when each pack in a solution can go to all stores, or when the pack quantity range is broad, thereby requiring optimization of the units in the pack. In these cases, you need more sophisticated approaches to obtaining the optimized packs – approaches that utilize historical store/size demand, allocation quantities, and pack handling costs.

Localizing sizes and packs and rationalizing SKUs:

In order to optimize sizes and rationalize SKUs at a local store level you need an acute awareness of product behavior. There are dozens of product behaviors unique to every store. In order to analyze these behaviors, retailers should optimize by style, color, brand, promotion, price, and seasonality at each store.

The concept of localization works on two levels:

  1. Retailers can look at the unique behaviors of every product – to determine each stores’ selling patterns for size, color, style, quantity, brand, season, etc. With this understanding, a retailer can plan orders on a store-by-store basis to deliver the right amount of the products that customers are buying at each location, allowing the retailer to achieve the highest turn rates, reduce inventory to the appropriate levels, reduce over stocking and stock outs and ultimately increase margin.
  2. The second concept of localization comes from localizing distribution, optimizing routes, re-locating product in the most optimal way, or utilizing vendors that are in a short vicinity of each store.

Size Optimization Overview:

Size Optimization refers to finding the optimal ratio of sizes to carry for given product in a given store. After segmenting products by Size Run (e.g. XS – XL vs. 2-16) and attributes of interest (e.g. Shape, Color, Fabric), the optimal ratio is found by looking at historic demand, which incorporates actual and lost sales. Size profiles for each group of products are computed at the store level, where enough data exists. A number of Quality Assurance steps are applied to the final output to capture and correct for any exceptions. The client can use the size profiles to both impact the size buy pre-season and the store-level allocation in-season.

Pack Optimization Overview:

Pre-Pack Configuration Optimization refers to finding the optimal configurations and sizes for a combination of packs. Optimality is defined in terms of maximizing an objective function that includes handling costs, lost sales, and markdowns (or wastage).  Pack Optimization involves choosing packs such that the increased profit from sales increase and waste reduction more than offsets any increase to handling costs.

Implications of changing pack size:

As the pack size decreases:

  • Handling Costs Increase: we are ordering roughly the same quantity as before, but doing so with more packs. Assuming a given cost per pack (typically 30p), we can compute the increased cost.
  • Sales Increase: greater sales are achieved by allocating more units to a store where the pack size restriction was previously a barrier.

Ultimately, you can arrive at combinations of packs that work well together to meet store/size demand and minimize handling costs without excessive over-allocation of sizes.

Get back in the game

Are you ready this year to know exactly what your customers are asking for at every location and to have the ability to react as their wants change? If you are looking for a solution that can drive momentum for your business this year, check out the solutions offered by Quantum Retail. Our customers see valuable results in 8 to 12 weeks, and our implementation approach gives your team access to the system from the beginning, so you can manage changes to your processes with ease. Quantum Retail continues to help all of its clients drive positive business value more rapidly than anything seen in retail.

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

Download this blog as a PDF.

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Quantum Retail wins ‘Supply Chain Solution of the Year’ at the Retail Systems Awards, 2008

Quantum Retail is awarded the 2008 Retail Systems Supply Chain Solution of the Year Award for its work with leading EMEA fashion retailer, New Look.

LONDON, UK – November 17 2008. Since 2007, Quantum’s Q solution has been used by New Look to manage its inventory replenishment and allocation processes across its 600 locations. Having increased its retail space by 20% and diversified into online and franchise channels, New Look were seeking a superior replenishment solution to improve management of store/SKU demand and supply.

As well as Q’s ability to help New Look manage and maintain its stock levels and provide visibility across the entire organization, the judges were impressed with Q’s return on investment: going live in under seven months and paying for itself five months later.

Speaking after the award was announced at London’s Grosvenor House, Quantum CEO Tarik Taman said, “In Q we’ve created a next generation inventory fulfillment module that determines inventory need anywhere in the supply network. We’re very proud of what we’ve achieved but most importantly of what our clients have achieved.”

After less than 6 months of operation, New Look was able to realize an increase in full price sales across the estate by 2%, with less stock. At the same time by reducing markdowns, gross margin increased by 4%.

New Look’s Executive Chairman Phil Wrigley commented “Q has enabled New Look to move our supply chain from being buy driven to customer driven, whilst delivering impressive financial results. It is truly first class.”

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 solutions solve the most difficult and costly problems retailers face – quickly and permanently. Our Q solution is the answer for: Forecasting and Order Planning – Replenishment and Allocation – Assortment and Range Planning.

About New Look

  • New Look has 604 stores in the UK and Eire, and 265 stores in France & Belgium trading under the name Mim. In addition, New Look has 19 New Look branded stores in France and Belgium, and has recently opened 16 franchise stores in Dubai, Kuwait and Saudi Arabia.
  • New Look has a volume share of 5.6% in the Women’s Outer/Sportswear age 16 market, and is the 3rd largest retailer by volume in this market.
  • New Look also has a growing market share in Mens & Kidswear.
  • New Look is now the number 1 retailer of women’s shoes in the UK by volume, with a market share of 7.4%. (Source – TNS).
  • 38% of the British female population has purchased an item of Womenswear** from New Look in the past year (52 w/e 30th March 2008). This amounts to just under 9.2 million individuals. The average age of shoppers in New Look is 31.
  • Further information can be found on http://www.newlook.co.uk and Product and Management photos are available upon request.

**includes Women’s Outer/Sports, Nightwear, Underwear, Hosiery, Footwear & Accessories

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Rock Around the Stock: Guitar Center’s forecasts and inventory allocation now make beautiful music together

Taking its cue from the Q system, Guitar Center’s forecasts and inventory allocation now make beautiful music together. Merrill Douglas, Inbound Logistics.

MINNEAPOLIS, MNAugust 2007 – Blues, rock, country, hip-hop, salsa – American tastes in popular music run the gamut. And the sounds that are big in El Paso this year might be totally different from the sounds that are hot in Brooklyn, or Nashville, or Spokane.
So when your business is selling musical equipment, imagine how hard it is to keep each of 200 stores across the country stocked with the mix that’s in tune with the local music scene.

That’s what Guitar Center was wrestling with three years ago. Part of its challenge stemmed from the fact that its stores differ greatly in size, ranging from 5,000-square-foot shops to 30,000-square-foot big box locations.

“Also, the types of customers we deal with vary widely depending on demographic and geographical regions,” says Bret Hayden, director of business process design at Guitar Center, Westlake Village, Calif.

The products that Guitar Center carries – guitars, amplifiers, percussion instruments, keyboards, and professional audio and recording equipment – amount to 7,000 SKUs. To serve customers and keep profits high, the company must understand how each SKU performs in each store. A homegrown forecasting system, developed in Microsoft Excel and Access, wasn’t hitting the right notes.

“The forecasting system operated at the chain level, but we really needed to be looking at inventory at the store level,” Hayden says. “We needed the ability to look at each one of our SKUs, and each one of our stores, and understand how they perform differently from one another.”
In addition to a system that provided insufficient detail, Guitar Center faced another challenge when trying to understand the store/SKU relationship.

The company’s forecasting team used one set of business rules to determine the volume and mix of products to send to its distribution centers, while the allocation team used a different set to create the product mix for stores.

“We would end up with a serious disconnect between what forecasting thought was needed and what allocation thought was needed,” says Steve Johnson, Guitar Center’s director of forecast, allocation and replenishment.

Today, however, Guitar Center integrates forecasting and allocation in a single process, and is much better able to tailor each store’s product mix to local demand. These changes came about through the company’s work with Quantum Retail Technologies.

Guitar Center has served as a beta customer for Quantum, helping the Carlsbad, Calif., software firm develop its inventory optimization solution, Q.  The retailer ran a pilot implementation of Q in late 2005 and early 2006; then entered a detailed design and implementation phase to address its long-range forecasting and product allocation needs.

That version went live in the third quarter of 2006. A third phase of the implementation — adding commodity products such as guitar strings and drumsticks — was due to go live in late June 2007.

Too Much Data

Quantum developed Q to meet the needs of retailers who, over the last few decades, have increasingly moved decision-making responsibilities from store managers to home-office executives. Those executives base many decisions on sales data pulled from the stores. But their enterprise resource planning (ERP) systems can’t analyze such a vast volume of information in great detail, says Mike Hrabe, Quantum’s vice president of sales and marketing. Instead, they aggregate the data and look at average performance for categories of stores and items.

“Through that smoothing, averaging, and aggregating process, retailers have effectively eliminated much of the detail associated with how items behave at the store level,” Hrabe says.

Ignoring the store-by-store detail obscures important information, such as whether a store is stocking the right product quantity, notes Chris Allan, Quantum’s founder and head of product strategy.

“A 98-percent in-stock of a certain product across the chain doesn’t really show a complete picture,” he says. “Some locations may be out of stock for several weeks; others may be overstocked.”

Q uses data from point-of-sale systems, ERP systems, and warehouse management systems to track exactly how much inventory each store has, how fast it’s selling, and how much new stock is flowing through the pipeline. In making forecast and allocation decisions,Q also considers the role each product plays in the company’s merchandising strategy.

A popular product at a marked-down price plays the role of traffic driver, Hrabe explains. The margin is low, but it draws in shoppers who might make other purchases while they’re in the store. Another product, with a higher profit margin, is a money-maker.  Still another serves as an image item, bolstering the store’s prestige by its presence even though few people actually buy it. Think of a giant screen TV in a consumer electronics store, he says.

Products play different roles in different stores. “An image item at the Best Buy in suburban Minneapolis might be a money- maker at the Beverly Hills Best Buy,” Hrabe says.  Demand for products also changes over time. As Q recommends inventory allocations for different stores, it considers the roles the company assigns to different products at those stores; then it tracks the products’ behavior to see how well they play their parts.

More precise information about product demand and performance creates greater efficiency. “Retailers hold too much inventory for fear of losing sales, but over-inventory means lost profits,” Hrabe says.

“Retailers have unbalanced inventory because they use grade group averages and lose much of the detail. They end up with too much inventory at one store, too little at another. Q directly addresses these issues,” he adds.

At Guitar Center, the point-of-sale system feeds data into a JDA Software ERP system, which passes it along to Q. Then, Q’s recommendations and alerts pass back to JDA and to the company’s Arthur Allocation system. “As part of Phase 3, we will integrate Q with our warehouse management system, so we’ll have information regarding shipment delivery times,” Hayden says.

Each time Guitar Center adds a new product to its assortment, the buyer and planner assign it a role and a strategy. “They can also set up other types of parameters,” Hayden explains. “For example, they can plan for a display in the store for that product, or set a ‘max stock’ if the item is big and bulky.”

The company could assign those rules to each product on a store-by-store basis, but executives have decided to move one level higher, dividing stores into several “grades” based on their characteristics. Stores get different grades for different product categories.
“One store could be an ‘A’ store for drums, but a ‘C’ store for guitars,” Hayden says. “We have the ability to manage inventory using those grades.”

Besides helping Guitar Center planners determine what stock to order and how to allocate it to stores, Q monitors product performance in real time and tells planners when product performance doesn’t match the forecast. For example, Q notifies planners if an item is selling better than expected. The planners can then arrange to order larger quantities in the future.

The Missing Piece

Company officials are contemplating a possible fourth phase to the Q implementation, which would focus on assortment planning. “That’s the piece we’re currently missing in our suite of applications,” Hayden says. “We’re able to create strategies for these items, but we don’t have good visibility to how that item fits in the whole assortment.”

Quantum representatives also have been talking to executives in Guitar Center’s Music and Arts Center division, which serves the school band market through more than 90 stores. Since Guitar Center started using Q, service levels and in-stock rates have increased, with a better inventory balance across the chain.

“We don’t have as many over- and under-stocks as we had in the past,” Hayden says. Also, now that it’s monitoring performance at the store and SKU levels, the company can generate more exception reports, and can measure forecast error. Those exception reports are important because they alert planners to problems or anomalies in parts of the operation that weren’t receiving enough attention.

“Q helps maximize users’ time and makes sure they spend their work hours where they can add the most value,” Allan says. And that’s music to Guitar Center’s ears.

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 solutions solve the most difficult and costly problems retailers face – quickly and permanently. Our Q solution is the answer for: Forecasting and Order Planning – Replenishment and Allocation – Assortment and Range Planning.

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