Posts Tagged ‘forecasting’

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

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

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

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

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

Answer:
Forecasting limitations

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

Why is it so difficult to forecast fashion? There are a number of reasons, but the primary issue is short life. Traditional forecasting systems need long periods of historical activity to identify selling trends and begin producing results they have confidence in. Add to this the complexity of sized merchandise and the data is much too granular to draw SKU / store level conclusions from. Many have come up with complex algorithms, constraints and rules that attempt to address this issue. My experience has been that while these can do a better job than a traditional forecast, that’s really not saying much and the effort isn’t justified by the result.

So, as retailers, we have adopted an alternative approach, allocation. If we look at allocation conceptually it’s mainly a surrogate to address the limitations of forecasting and store replenishment. Since the products don’t live long, we supplement our need for more historical selling time by applying our knowledge of similar products or product groups and use those to give us more data. This allows us to begin seeing selling patterns. We then apply calculations that interpret the relationships in this base of data to derive a calculated recommendation.

These calculations are simpler than forecasting routines, but together with the additional merchandise that makes up the base of data they are much less volatile and therefore return reasonably stable results. We review this result and change it based on other dimensions of data we analyze – and based on assumptions and intuition.

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

What you can do now

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

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

What you should consider when looking for new capabilities

Recently a few companies have had success applying forecasting to fashion allocation. They have done this by combining advancements in technology with innovation in retail science to understand the relationships of behavior across many different product and store types and levels. The resulting understanding of behavior across multiple dimensions is used to derive the likely behavior of the product you need to allocate.

With the best of these systems, even though the underlying logic is much more complex execution has thankfully been simplified. Since these systems also understand what you as an allocator are trying to achieve, they can execute to that automatically. Only when they cannot do what you’ve asked of them does the allocator need to intervene. Even then, issues are addressed using business logic rather than trying to manage complicated calculations, statistics or controls.

Footnote

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

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

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

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

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Learn more

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

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

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5 tips for developing a real-time response plan for customer behavior

By Greg Wilson, Director of Field Strategy, Quantum Retail

1. Set objectives –

Each product should have a role with specific objectives that can be measured and executed to. A product may be in your assortment to drive traffic, to generate profitability, to present an image or to opportunistically acquire impulse sales. Each of these roles come with unique objectives that can result in different inventory requirements.

2. Shift focus –

While forecast accuracy is important, it is not the only way to improve inventory placement. If you are adjusting forecasts to achieve different inventory results, you’re already reacting to this fact. Shift focus to finding the best way to utilize inventory to achieve goals while understanding forecast accuracy and variability are realities.

3. Waste not –

Get a deeper understanding of the impact of waste on your inventory decisions and act on it. Depending on margin, it may be more profitable to accept additional waste on some products, while other products would be better served accepting an occasional lost sale.

4. Get local –

There is no substitute for understanding product behavior at local levels. There are many ways to improve this understanding but consider those which have the most impact including:

Seasonality - If you’re working to static, periodically generated seasonality profiles, you have a great opportunity for improvement.

Time of day – Did you stock out? When? What did that mean in missed opportunities for sales? Can you replenish again today? The more detail you have in answering these questions the more efficient you can make your inventory – especially for short life, short lead time merchandise.

Day of week – Does this location have a weekend traffic boost? Does that product respond to the pattern? Understand these interactions invariably leads to better performance.

Weather impact - Does this product react differently on cold days or wet days? What does that mean to demand? And how should that affect how stores are supplied? If I can ship it tomorrow and I know it’s going to be hot, what’s the right decision? We all know these realities exist, but have you been able to execute to the reality?

5. Revisit and rationalize –

Product behavior constantly changes with the changing consumer. The item that fulfilled it’s role last year or last quarter may not be doing so now. You need to be alerted to situations where this change is happening, and have a mechanism to understand and react to the way that impacts your offerings to customers.

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For more information on customer behavior – check out our RESOURCE LIBRARY.

New Look selects Quantum Retail’s Q to optimize inventory fulfillment on path to growth

Bolt-on solution set to quickly optimize inventory forecasting and replenishment at top fashion retailer

LONDON, UK - Jan, 2008 – Bolt-on solution set to quickly optimize inventory forecasting and replenishment at top fashion retailer. New Look, a leading European apparel retailer known for its fast fashion proposition and leading designer ranges, has selected and implemented Quantum Retail’s inventory optimization solution, Q, to manage the replenishment of its 600 stores.

“As a fast fashion business with our customers at the core of everything we do, we recognized that Quantum Retail’s demand forecast model offered us an opportunity for our customers to further influence our decision-making,” said New Look Director of IT, Adrian Thompson. “It’s innovative science allowed us to continue to support our fast fashion model with speedy and accurate decisions based on our latest sales and stock figures.”

“The compelling business case that supported this investment was based on a number of metrics ranging from stock imbalance, improved service levels and a reduction in markdown,” Thompson added. “Put simply, Quantum Retail is able to more accurately reflect where our customers would like the product and at what level. It effectively bridges the gap between planning and execution.”

Quantum Retail’s Q solution has been implemented alongside the retailer’s existing Oracle retail merchandising system, initially optimizing the replenishment part of the business. Future planned phases include multi channel lifecycle management, initial allocation and reorder planning. vii.

“The successful delivery of this program was based on an exhaustive proof of concept and a speedy implementation,” Thompson said. “The Quantum team integrated seamlessly with my own. Their well thought through designs included system and integration, great business engagement and training at both commercial and functional levels. Quantum is now live with no adverse impact to either IT or the business and is already giving us confidence that the business case will be delivered successfully.”

After an initial monitoring period, service levels showed an increase in availability alongside a reduction in total inventory when compared to a control group. As the rollout of Q continues, it will be rebalancing stock throughout New Look’s 600 UK and international stores, leading to fewer stock outs and improved sales and profit. Through the use of Q’s advanced forecasting and order planning module, New Look will be able to gain greater visibility of long range inventory need and be able to optimally flow inventory to its stores.

“Q uses a proprietary approach that is designed to be the most accurate, responsive and reliable inventory fulfillment tool in the fast-changing world of retail,” said Chris Allan, head of product strategy for Quantum Retail. “At the same time Q has been designed to be a highly practical and useable solution that operates alongside existing systems for simple and quick implementation.”

“Its simple user interface means Q can be used by our allocators, rather than having to rely on experienced forecasters and mathematicians,” Thompson concluded.

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 590 stores in the UK and Eire, and 263 stores in France trading under the name Mim. In addition, New Look has 13 branded stores in France and Belgium, and has recently opened franchise stores in Dubai, Kuwait and Saudi Arabia. In the UK New Look has a 4.8% market share, making it among the leading womenswear retailers in the UK (Source – TNS).

New Look also has a growing market share in Mens & Kidswear and is now the number 1 retailer of women’s shoes in the UK by volume, with a market share of 7.3%. (Source – TNS). 25% of British women have bought an item of outerwear from New Look – amounting to over 6 million customers (Source – TNS). New Look’s competitors include H&M, Next, Top Shop and Dorothy Perkins. The average age of shoppers in New Look is 30.

Further information can be found on http://www.newlook.co.uk and Product and Management photos are available upon request.

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.

Forecasting For Success

Forecasting is a tricky business, retailers lose money every time a demand forecast is inaccurate. Sophisticated software is necessary to ensure that customer demand and in-stock products are a perfect match.

Dori Saltzman, RIS News – April 2 2007 – Forecasting is a tricky business, retailers lose money every time a demand forecast is inaccurate. Sophisticated software is necessary to ensure that customer demand and in-stock products are a perfect match.

The Guitar Center chose Quantum Retail’s Q forecasting software to help it get a handle on the “fashion-based, choppy demand” of its higher-priced merchandise. Early indications are positive: though Q is currently forecasting only 50 percent of the inventory — representing the vast majority of sales dollars — the forecasts it generates are 20 percent better than before, according to Irene Messier, executive over inventory management.

Prior to implementing Q, the Guitar Center used two separate forecasting systems. One, an automated replenishment system, was used solely on the less expensive, high velocity items — the so-called “sticks and strings.” A manual, excel-based system was used on the more expensive, slow moving items — such as $700 guitars.

The Guitar Center selected Quantum Retail to provide a system to handle both types of inventory. “We were looking for something that gave us better forecast outcome (on the slow moving items) and also hoping to find a solution that could be utilized in updating our auto-replenishment product as well,” says Messier. The Guitar Center upgraded its forecasting methods after identifying a need for greater demand accuracy for the slow-moving items. “One of the factors in a situation where you’re dealing with Excel-based forecasting is that the level of accuracy, due to the amount of information that needs to be processed, isn’t going to be nearly as accurate as using a statistics-based forecasting model,” says John Zavada, executive vice president and CIO.

The $700 guitar is the quintessential high-priced, slow moving product for which accurate forecasts are vital. Guitar Center stores risk unbalanced assortments and lost sales if forecasts are off.

In order to determine that Q could generate accurate forecasts for both the high priced and high velocity items, the Guitar Center ran a six-month pilot before rolling out the system live on the higher priced inventory. The pilot was run on a sub-section of the products. “We observed consistent month after month, better forecasting coming out of the Q application,” says Messier.

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.