Posts Tagged ‘demand’

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

by Quantum Retail • June 29th, 2011 • Write a comment »

By Dan Moran, Product Strategy, Quantum Retail

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

by Ziad Nejmeldeen • June 8th, 2011 • 1 Comment »

By Ziad Nejmeldeen, VP of Science, Quantum Retail

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

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

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

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

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

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

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

Relevance in Fashion

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

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

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

Size Optimization

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

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

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

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

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

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

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

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

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

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

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

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

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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: Demand, Demand, Demand

by Greg Wilson • June 1st, 2010 • Write a comment »

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.

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

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The truth about size optimization, SKU rationalization and localization

by Quantum Retail • February 4th, 2010 • Write a comment »

Getting the right sizes, colors, styles and quantities to the right location

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.

Optimizing sizes and rationalizing SKUs

In order to optimize sizes and rationalize SKUs at a store level you need an acute awareness of product behavior. It does not make sense to only optimize on size – if a retailer is going to take the time to assess sales and demand at a store-by-store level, they should rationalize SKUs at a local level by using store data.

There are dozens of product behaviors unique to every store. In order to analyze these behaviors and make the most of their efforts, retailers should optimize by style, color, brand, promotion, price, and seasonality at each store. But the complexity of this exercise can become time consuming when a supply chain is vast.

There are however, technologies available that can simplify this process and make it ongoing, creating a strategy for these attributes and applying it to all levels of inventory management, from order planning, allocation, replenishment, forecasting and distribution.

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. The second concept of localization comes from localizing distribution and utilizing vendors that produce products in the vicinity of each store. This type of localization is most easily applied to fresh foods and markets – where customers prefer to support their local farmers and local brands.
  2. When retailers realize that they cannot optimize sizes and packs unless they have an awareness of store demand, stock outs, and customer behavior at the local level, they quickly become more profitable and able to compete in today’s retail market.

Q – the quickest and most profitable solution for size optimization, SKU rationalization and localization

The Q system continuously monitors business strategies, profitability, service levels, stock levels and 35 different aspects of behavior for every product in every store. Q is so intelligent that it learns from data like stock outs, lost sales, slow movers, lumpy sellers, packs, sizes, colors and styles. It takes the most recent data for each item and automatically recommends inventory movement decisions driving toward your corporate objectives. Plus, it optimizes the way you phase in a new product and phase out another – ensuring that you are always reaching your optimal performance, sales and service levels, giving you the highest return on the inventory you are buying.

CLICK HERE for more information on Q

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

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Forecasting For Success

by Quantum Retail • April 2nd, 2007 • Write a comment »

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.

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