Posts Tagged ‘order planning’

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

by Ziad Nejmeldeen • June 22nd, 2011 • 2 Comments »

By Ziad Nejmeldeen, VP of Science, Quantum Retail

Pack Optimization

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

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

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

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

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

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

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

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

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

Order Planning/Warehouse Replenishment

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

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

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

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

Allocation

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

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

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

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

Conclusions

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

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

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

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

by Ziad Nejmeldeen • 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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Quantum Retail Releases Q v 10.05 to Support Complex Supply Chains, eCommerce, Enhanced Order Planning/Warehouse Replenishment and Forecasting Capabilities

by Quantum Retail • August 31st, 2010 • 3 Comments »

MINNEAPOLIS–(BUSINESS WIRE)–Quantum Retail, provider of the most advanced retail merchandise optimization systems currently available, has released the latest update to its core platform, Q. The update provides advanced execution for complex supply chains, eCommerce, and enhanced order planning/warehouse replenishment and forecasting that allows planners to manage at the day level for short life products and up to 18 months in advance for long life products, with the ability to recalculate distributions based on the most recent localized demand data ensuring extremely accurate allocation and replenishment.

Specific changes in this new version include:

  • Multi supply chain support gives flexibility in order planning/warehouse replenishment and distribution for retailers with complex supply networks and methods, such as Vendor to National Distribution Center (DC), Vendor to Regional DC, National DC to Regional DC, etc. to move stock as quickly and efficiently as possible, reducing the risk of missing a sale due to unplanned circumstances. Q now supports direct to store orders and allows users to view order quantities by location in order to get the right quantity to every local store as soon as it is needed.
  • eCommerce integration enables retailers to easily manage and integrate eCommerce inventory, warehouse or vendor availability and distribution alongside physical store locations. This permits retailers to maintain availability, so that high demand products do not go out of stock either in-store or online.
  • Enhanced order planning/warehouse replenishment and forecasting allow planners to forecast and manage short life products at the day level while users can also change to a week view and manage forecasts and order plans for 18 months out for longer life products. Planners can also test “what-if” scenarios, with the ability to change quantities as late as time of receipt based on the most up to date demand data. This means retailers are able to easily and accurately manage the real-time demand for their inventory all the way down to the local, individual store level with the Q system.

“We took extensive feedback from customers into account when implementing the latest changes to Q,” stated Morgan Day, CTO of Quantum Retail. “This latest release incorporates some important improvements to an already highly robust software offering and we will continue to improve Q to ensure our customers have the benefit of utilizing the most advanced merchandise optimization system available.”

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 solution, Q, solves 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.

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Welcome to The Profit Lab

by Greg Wilson • May 29th, 2010 • 1 Comment »

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 >>

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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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