Archive for July, 2010

Matalan Selects Quantum Retail’s Software for Store-Level Replenishment

by Quantum Retail • July 27th, 2010 • Write a comment »

LONDON & MINNEAPOLIS (BUSINESS WIRE) –
Quantum Retail, a next generation merchandising optimization software provider, is pleased to announce that Matalan, the United Kingdom’s leading value fashion and homeware retailer with more than 200 stores, has chosen Quantum’s Q software to optimize store-level replenishment activities. With a deep understanding of shopper behavior and merchandise strategies, Matalan hopes to service its customers better.

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

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

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

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

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

About Matalan

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

About Quantum Retail Technology, Inc.

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

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

Read more about Quantum Retail»

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The Profit Lab: Are you constraining your potential?

by Greg Wilson • July 27th, 2010 • 2 Comments »

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

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

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

Physical constraints

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

Operational constraints

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

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

What you can do now

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

Physical constraints

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

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

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

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

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

Operational Constraints

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

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

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

What you should consider when looking for new capabilities

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

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

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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: Allocation Metrics that Matter

by Greg Wilson • July 20th, 2010 • 2 Comments »

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

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

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

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

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

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

What you can do now

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

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

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

What you should consider when looking for new capabilities

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

<< 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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Creating a Customer Centric Supply Chain

by Linda Whitaker • July 13th, 2010 • 2 Comments »

This article, by our own Linda Whitaker, was published in RIS News»

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

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

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

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

Some questions that retailers should ask themselves:

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

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

What and Where:

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

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

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

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

How Much and When:

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

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

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

Putting it all Together

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

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

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

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

by Greg Wilson • July 13th, 2010 • 2 Comments »

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.

<< 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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Helping to simplify retailers’ inventory planning by tracing a product’s ”DNA”

by Star Tribune • July 7th, 2010 • Write a comment »

Software firm Quantum Retail earns kudos for excellence and growth

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

It took them nearly a year to develop what is now called the “Q” platform, but the ensuing results have been spectacular.

In four years their company, Minneapolis-based Quantum Retail Technology, grew from a $420,000 gross in 2005 to $9.8 million of sales in 2009 — with revenue growth on track in 2010 to reach well into double digits once again.

In August 2009, Inc. magazine ranked Quantum as the 11th-fastest-growing privately owned software company in the country.

It was not the first time the company had attracted attention. In July 2008, Quantum’s software won the “Supply Chain Excellence” award in a European competition. Four months later, the British trade journal Retail Systems named the “Q” platform “Supply Chain Solution of the Year.”

And in October 2009, Vicki Raport — Quantum’s CEO and the one who arranged that 2004 planning meeting — was named as one of nine Entrepreneurial Winning Women in a national program sponsored by the accounting firm Ernst & Young.

What’s the attraction? To answer that question, it helps to get a glimpse of the problem.

Inventory planning for large retail chains is “a very complex environment, involving tens of thousands of products in thousands of locations across the country and around the world,” Raport said.

We’re talking transaction data ranging from point-of-sale systems to a mountain of inventory figures that changes daily to the wave of information coming out of the growing radio frequency identification (RFID) chips that track the movement of product in the supply chain.

There are software products that assemble all that data, “but it’s very difficult to translate that information into action,” Raport said. That was the Quantum partners’ objective as they began developing the “Q” product.

Retek roots

Members of the founding group, all of whom once worked for Minneapolis retail software company Retek Inc., offered an ideal collection of experience and talent to develop, market and sell the software.

In addition to Raport, whose background is in retail strategy and operations, the founders include Linda Whitaker, whose expertise is in applied retail science, including predictive analytics; Morgan Day, chief technical officer in charge of product development; Chris Allan, Quantum’s chief strategy officer; Wyndham Albery, managing director in the United Kingdom, and Mike Hrabe, executive vice president of international sales.

The partners are spread around the world: Raport, Hrabe and Day are based at the Minneapolis headquarters; Wyndham and Allan are based in London, and Whitaker works out of her home in the Virgin Islands.

Together, the group fashioned a product designed to help retailers forecast and plan inventory needs, product by product and store by store, depending on the location, size and demographics of each outlet’s market area. The system works off a lengthy list of product sales and inventory histories — sort of a “product DNA,” as Raport put it.

OK, if you want to get technical, it involves 35 “performance metrics” ranging from average demand, average sales and seasonal affects to product life-cycle patterns, shelf life and maximum sales per day. Not to mention average inventory by date, actually in stock and in transit. All of which helps retailers calculate potential consumer activity.

And there’s more. All the data gathered and manipulated by the “Q” platform are applied on the basis of a retailer’s goal for each product in stock: as a profit maker, a traffic driver, a loss leader or a promotion.

Add it all up and it comes down to one simple, but crucial point: The Quantum software helps retailers “track and anticipate rapidly shifting patterns of consumer behavior,” Raport said.

The Quantum system has attracted only a handful of clients so far, but the list includes some sizable retailers, both in the United States and Europe. The group includes Wisconsin-based Kohl’s Department Stores, California-based Guitar Centers and three large operators based in the United Kingdom: New Look, a women’s fashion chain; department store chain Marks & Spencer, and, most recently, discount fashion chain Matalan.

By all accounts, it’s a satisfied clientele: We don’t have as many over- and under-stocks as we had in the past,” Bret Hayden, Guitar Center director of business process design, said in an e-mail to Raport. “Not only does ‘Q’ help us identify when to buy and how much to buy, it also helps us identify how to navigate through our supply network to support seasonality, life-cycle and promotional activity.”

New Look CEO Phil Wrigley put it a tad more succinctly: “Q enables us to synchronize our supply chain with the unique demands of each of our stores,” he wrote.

And the result, added New Look IT and e-commerce director Adrian Thompson, is that “Q actually paid for itself in five months.”

Dick Youngblood • 612-673-4439 • yblood@startribune.com

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