Hardlines Optimization—Part 3: Seasonal Profiling, Understanding Your Long Term Forecast

by Quantum Retail • September 1st, 2011 • Write a comment »

By Dan Moran, Solution Strategy, Quantum Retail

Trying to predict retail sales is a little like trying to predict the weather for a week or a day within a season. March winds and April showers may bring forth May flowers, but how stormy will it be? The summertime sun may shine brightly, but how hot will it be? The winter snows will fall, but will we get any blizzards? Can you really use last year’s weather information to predict what’s going to happen tomorrow? Not exactly, but you can know the average temperature to expect, and you can know from your experience how to be prepared on a given day for it to be warmer or cooler, rainy or snowy, windy or calm, even as you know the weather forecast won’t be perfect.

Understanding Time of Year Demand Patterns

Retailers face a tremendous challenge in trying to predict a multi-dimensional future. Which products will sell at which locations or be ordered online during which time frames are the core questions to answer with any forecast. Can you predict with certainty what will sell tomorrow? Certainly not, but you can learn from many prior experiences with categories of products or individual products in your markets and in your stores. You can be prepared to have the right product at the right store at the right time, not just to meet the average demand, but to account for reasonable localized variations for that product at that store at that time.

Retailers spend a significant amount of time, effort, and analysis to capture and track product and store performance throughout the selling cycle. Your goal is to learn from experience and plan for the future within the context of that history. Viewing that historical data alone is not sufficient. You can gather insights from other sources to understand characteristics of the consumers in the neighborhoods where the stores are located, what motivates customer purchasing behavior in-store or online, and what preferences they have, not only for products purchased in the past, but the attributes of new products they are likely to purchase in the future.

From this mix of history and insight, you can also gain an awareness of expected periods of time when you can anticipate demand to peak or to fall off for groups of products at various stores. Retailers frequently make use of product categories or grouping of products by common attributes or features by store groups or clusters to make high level planning, assortment, pricing, promotion, and other merchandising decisions. Many merchandising activities are arranged around the retail merchandising calendar that breaks a year into seasons, periods, and weeks. But what useful grouping of timeframes do you have to reflect what you have learned about customer sales across time?

Seasonal Profile Management

Seasonal profiles are used within forecasting techniques, representing historical and expected distinct selling patterns for a merchandising year. Often profiles suggest the actual calendar seasons of the year – Spring, Summer, Fall, and Winter. Frequently, profiles will reflect consumer purchasing  influenced by other recurring activities such as gardening, hunting and fishing, football, baseball, or basketball seasons, school calendars, or holidays. With so many permutations of products and product groups, stores and store groups, and possible seasonal sales patterns groupings, it’s a challenge to gain benefits from the understanding of seasonal and recurring activities effect on future selling periods. It could be a set of difficult analytical and data management chores to define the most meaningful seasonal profiles, determine from which sets of products and locations those patterns can be derived, keep profiles up to date by learning from recent sales performance, and apply the right profile down to a SKU / store level during forecast calculations. Quantum Retail has developed a solution that eases these burdens for retailers.

Quantum Retail’s solution Q uses seasonal profiles to impact forecasts and inventory to account for how customer buying behavior changes over the course of the year. The profiles are derived for combinations of products and locations, often on the basis of some product or location characteristic other than the standard product hierarchy or store geographic hierarchy. For example, stores might be grouped by size or proximity to a competitor and products grouped by price range or brand rather than (or in addition to) by product hierarchy. Attributes for a known and planned seasonal timeframe, such as spring gardening or fall deer hunting might be used.

Influences on SKU’s and Store Performance

Rather than relying on a single profile updated and assigned by users at irregular intervals at a chosen level of product and store groupings, Q automatically makes use of multiple source levels of profiling information. Q evaluates additional higher level product and location groupings when applying a seasonal profile’s pattern to determine weekly demand. By evaluating the SKU / store in relation to relevant combinations such as SKU / store cluster, class / store or store / season code, Q dynamically finds the most appropriate indicator of expected future behavior.

The sets of source levels defined by the product and location levels to use and the escalation path of levels to evaluate are established as part of the Q implementation project and do not need to be maintained by users during production usage. Quantum Retail delivers the expertise and the science to make sure profiles use the right attributes that really have an effect on performance and not just preconceived groupings or aggregates with coincidental correlations. Q establishes the appropriate number of profiles and reasonable thresholds for scoring the fit of patterns to be deployed.

Seasonal Profiles Are an Integrated Component of Q

Profile weekly indices are updated automatically by Q continuously with true demand that accounts for lost sales due to out of stock situations in order to learn from and make use of recent activity. Other solutions in the market rely on separate modules for building and importing profiles on an infrequent basis based only on sales or shipments, resulting in profiles that are inaccurate and out of date.

Seasonality administration features provide a seasonal override ability to adjust the seasonal indices or ‘shift’ the seasonality of a given week. This shift functionality is useful in cases such as when holidays occur on different dates each year and the user wants to capture the fluctuation in sales over the holiday period on the new date in the current year (for example Easter). Seasonal profiles are aware of lift during promotional events and interact with Q Event Management features to accurately reflect lift in forecasts due to annual or one-time promotions.

Q learns and puts to use its knowledge about the impact of seasons and events at the most detailed level, by SKU and by store, but within the context of the behavior of similar products and similar selling locations and channels. That means that forecasts for re-orderable products will be more dynamic and more accurate. Q also applies that knowledge to make sure new products consider the correct seasonal influence in their introductory period and throughout their lifecycle, no matter how short it may be.

Profiles convey the shape of future demand and Q applies its understanding of product and location behavior to calculate the quantification and trending of demand. The nature of forecasting is that the result is a prediction of the future based upon assumptions of the past, subject to the uncertainty of unforeseen events. Q considers other metrics when allocating and replenishing to stores, such as sales variability and the predicted distribution and frequency of sales, which may play an even larger role than the forecast. Q makes intelligent decisions that guide recommendations whether or not to send case pack quantities to meet a low volume of forecast demand. Executed properly, considering these variables can compensate for inaccuracies in a forecast. Each capability of Q contributes to the overall effectiveness of the solution.

A Holistic Approach to Inventory Optimization

With the approach that Q takes to manage and apply seasonal profiles as a factor in its forecasts, the benefits are numerous. Q forecasts demand and calculates the inventory and orders needed to meet an objective rather than just trying to forecast sales. A solid forecast is a significant input into decisions on how to deploy inventory to manage selling at full price while minimizing markdowns or out of stocks.

Q provides several innovative yet practical approaches to building the components of the forecast to improve understanding of the past to model known likely influences on future performance. Q makes the use of seasonal profiles understandable for users without burying them in the complexity of the statistics and the algorithms that underlie the power that profiles provide them.

Those who follow the weather closely to help with their gardening hobby or farming industry can turn to the Old Farmer’s Almanac for useful short and long range weather forecasts as inputs to plan their planting, tending, and weeding. They can also find great tips on seed ordering, pest control, and other plant care ideas customized to their zip code. All that knowledge can help them optimize their harvest. Similarly, retailers can make use of seasonal profiles to get a read on demand and then employ the other holistic capabilities of Q to optimize their returns on inventory investments.

Look out for next week’s blog on the Death of Min/Max Replenishment.

Sign up to receive updates throughout this series.

Be the first to like.
Unlike

Hardlines Optimization—Part 2: SKU Rationalization

by Quantum Retail • August 24th, 2011 • Write a comment »

Right now is an extremely important time for retailers to optimize their assortments. This process not only can dramatically increase margin and sales, but can also help localize store-level assortments and increase the efficiency of your customer’s shopping experience. When retailers offer too many choices, it can cause headaches for shoppers and supply chains alike, force unnecessary markdowns, and ultimately will take a toll on margin.

However, going through this process can be a bit daunting and takes careful consideration on your part. Determining the proper breadth and depth of your assortment is not rocket science, but it is not something to take lightly. If you cut the wrong products, you could potentially lose some of your loyal customers if you are not careful.

But all retailers can benefit from going through the process to evaluate the performance of their products and stores. SKU reduction will help you create assortments that are easier to manage, more efficient, and more profitable – this means less stock-outs of the products that are kept in the assortment (depth instead of breadth), tighter focus on product performance, and more flexibility in vendor-level considerations like tray size or pack size choices. Additionally, it can offer a better shopping experience for the customer who may otherwise be distracted by the breadth of fringe products, and make it easier for them to come to a decision.

How is this process typically done?

Most retailers have some concept of store grade by merchandise category based on store sales performance or similar criteria. If grades are ranked from highest to lowest (e.g. A through G where A is the highest volume stores), then a product will be ranged to all grades between A and x. The choice of grade x is based on whether the product is core or is just meant to fill out the assortment – in which case it may only go to the top grades. When assessing overall product performance, a product should be removed from the assortment of grades where it is not meeting business expectations. Absent of a store grade concept, the same principal can apply to individual stores where the rate-of-sale of the product in the store can be used to determine whether it should still be assorted to that store.

What are the dangers of SKU Rationalization?

Cutting certain types of items can sometimes change the customer’s perception of your brand. For example, the danger of removing an image item, like a high-end flat-screen TV, could make your shoppers see you as having inferior technology than your competitor. Though the customer most likely will not buy the most high-end item, they want to compare the biggest and best options and feel as though they made their purchase at a place that has a respectable assortment of that item.

Another danger is that if the decision to remove a product is made solely on that product’s performance, you may be losing a product that helps drive the sales of associated products. Worse, you risk losing a key customer to competition and never regaining their business. It is important to know who is buying the products being removed, and what else they buy.

How do I avoid cutting items that top shoppers really want?

Looking at transactional data (what items sold in the same transaction) or loyalty card information (which customers are associated with the sales of those items and what those customers have spent over the last year) are two means of addressing that question.

Retailers may also make choices about which products they plan to cut from their assortments by briefly discontinuing the product’s replenishment. A good assessment of the choice can be made when a planner looks at how quickly the product stocked out and if any associated product sales slumped in the process. After this analysis, it should be fairly obvious whether or not the item should stay or be removed. Similar tests can be performed in a grouping of stores. Item performance can be analyzed in those stores and similar decisions can be made for like stores, especially those with similar item level performance and demographics.

When is a good time to rationalize SKUs?

For retailer’s that have a concept of season and have items brought in for each season, SKU rationalization should be done as part of pre-season planning. For long-living items, assortment decisions would be made at the start of the item’s life that would then be tweaked after the item starts selling (but the bulk of the decision would have been made upfront).

Which inventory should retailers focus on reducing?

SKU rationalization in many cases is more effective with longer-living merchandise because you can track an item’s progress and make reasonable adjustments. With trendy items, for example, it is more difficult (but not impossible) to base next season’s SKU rationalization on the previous season when the previous season may have been impacted by the performance of particular trends, patterns, or colors (i.e. cases for computers or cell phones).

When determining whether to add or remove SKUs to an assortment, retailers should look at three major factors:

  1. The relative value of each SKU in the assortment
  2. The GMROI of the store itself (or cluster)
  3. The local demand of each store – what shoppers are buying

The reductions or additions should be made in periodic intervals, perhaps weekly. This decision will look at these three factors and assess whether a planner should add one item to this cluster, remove two from another. It’s not a once-a-year, twice-a-year process – it’s constant. This is a big deal. Going through this process on a continuous basis will give visibility to product performance and the success of a reduced assortment.

Where to begin

Your main question: What to send to which store for what reason?

The Top 3 things to consider when beginning the rationalization process:

  1. The direct impact the SKU will have on the store’s performance through its sales contribution
  2. The indirect impact the SKU will have (through halo/cannibalization, i.e. cross-item effects)
  3. The hard-to-measure “image impact” – beyond actual dollars generated by the item or associated items, does the existence of the item in the store impact your customer’s perception of your store

What you should consider when looking for new capabilities

It is important to look for tools that will help you assess the profitability and success of each item at all of your stores. When retailers have a tool that can constantly and automatically monitor the success of their products and make recommendations on the breadth and depth of the assortment at each location, they will make the most of their time and quickly increase margin.

There are new technologies available today that can simplify this process and make it ongoing by creating a strategy for these attributes and applying it to all categories and stores.

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

What to look for in assortment rationalization technology:

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

Most software products focused on assortment give retailers the tools to assess item performance and to make removing or adding decisions. Quantum is going a step further by suggesting, by category/store, where ranges should be increased or decreased. The software will then quantify the specific assortment change recommended by suggesting how many items should be dropped or added to determine the final cluster assignment. The planner can then see the impact (a what-if) to sales/profitability/etc when the SKU rationalization is changed. This gives retailers the tools to make intelligent decisions regarding the rationalization – while still leaving the choice in the retailer’s hands.

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

Look out for next week’s blog on understanding long term forecasting and seasonal profiles.

Sign up to receive updates throughout this series.

Be the first to like.
Unlike

Echelon Magazine: Vicki Raport Profile

by Quantum Retail • August 17th, 2011 • Write a comment »

“One of the great benefits of being an entrepreneur is the ability to establish a culture for your organization that represents your values. In Quantum Retail, this translates to a diverse and inclusive culture where people of different ethnic, geographic, religious, and lifestyle preferences work together in a collaborative way for the benefit of the company, our clients, and each other.”

To read more, see the digital edition HERE»

Be the first to like.
Unlike

Hardlines Optimization—Part 1: Clustering is Not Localization

by Quantum Retail • August 17th, 2011 • Write a comment »

There was a time when it was enough to have individual store managers make the decisions of which products to carry and how much to order. As hardlines retailers have grown, their offerings have become more elaborate and competition has become more sophisticated. Customers and their behavior have become more complex as well. It’s no longer possible for people to keep track of all the variations and trends making each location unique.

For the last two or more decades, conventional wisdom has been that clustering stores is the way to address this problem. While, conceptually, grouping stores with similar characteristics makes sense – the reality is that it’s very difficult if not impossible to do this in a consistent and effective manner. From volume clusters to attribute clusters and even more recently devised mechanisms for deriving “intelligent” clusters using BI tools, all suffer from flaws that keep them from achieving the elusive goal of localization. Let’s look at some of these to understand why that is.

Inherent problems with volume based clusters:

The most widely used of these decades-old methods to cluster stores is volume clustering. Typically using historical sales for a group of merchandise presumed to be similar then finding apparently logical breaks as that volume is ranked high to low. The breaks represent boundaries which define which cluster stores fall into. Most commonly the result is a set of 10 or fewer cluster groups (although we’ve witnessed organizations that do many more). The three most common flaws in this process are:

  1. The clusters are virtually always based on historical sales
  2. The clusters are not updated frequently enough
  3. The clusters don’t incorporate any attributes of the merchandise or the stores

Typically a retailer looks at a store’s performance over the last year or compares like for like seasons to group stores that performed similarly together. The problem is that usually its only historical sales that are used to group the stores together. This creates a bit of a self-fulfilling prophecy. If a store underperformed in a department because of an inordinate amount of out-of-stocks, could the store have performed better?

The better option is to use either historical demand, or better yet, a demand forecast. When we refer to demand we look for what would have happened had the store been in stock. This allows us to understand any missed opportunity that may have resulted from out of stock situations. Of course using a true, forward looking demand forecast which includes understanding those missed historical opportunities to cluster would be the best of both worlds. Not only are you incorporating lost sales, but you would be incorporating future trends of store behavior as well.

Another problem is timing of when clusters are used and how often they are evaluated. Store assignment to clusters should be evaluated as often as possible. Customers change their shopping behavior constantly and this changes the behavior of individual stores constantly. As a result, store clusters need to be re-evaluated as frequently as possible using the most current understanding of behavior. At a minimum cluster should be updated prior to each merchandising activity that is consuming them. For example, updates should occur pre-season when an assortment plan is created (which products in which locations), again when a buy plan is made (soft commitments for how much), again when the actual order is placed (time has passed, things have changed), again when commitments to DC shipping locations (DC splits) are confirmed, again if the buy is pre-distributed to commit stock to stores, another when the goods hit the DC, and so on.

Furthermore, clusters should never be used to drive replenishment decisions. Once the goods have hit the stores, any re-allocation/replenishment activities should be based on actual, individual item/store behavior. There is simply no good reason to use clusters after the first allocation – and even then a sound argument can be made as to whether they should be used at all. This is especially true for hardlines and commodity products that don’t have the volatility or size complexity seen in hardlines. Today’s technology is capable of identifying and leveraging the uniqueness of individual products in individual stores, so each store can be treated as it needs to be. This is where true localization can truly begin to provide huge benefits.

Clustering beyond volume

Retailers use more than just volume clusters but as of yet no standards have been adopted. Climate, an attribute of physical location, is one of the most commonly seen attribute clustering criteria. While useful, categorizing a location as one of three or four climate groupings is an inexact science at best. And there are surely many other attributes of location and product that can refine the quality of the result. In traditional execution, however, more attributes leads to more groups needing to be managed. These groups are often nested into artificial hierarchies and quickly become difficult to navigate and virtually useless when complexity reaches the point that execution is impossible.

More recently many have tried to utilize Business Intelligence solutions and statistical analysis to find more refined groupings of stores that behave similarly. One of the most common flaws of this approach is that the resulting clusters have no definition of what makes them similar. While it may be accurate that they have similar behavior, that conclusion alone is useless unless a merchant understands what makes a group unique. Without that there is nothing available to guide a cluster specific decision.

Where does that leave us?

Ultimately, newer technologies that have been focused on solving these problems in retail have refined the utilization of clusters significantly. Better solutions constantly review and update clusters based on current behavior and the processes that consume clusters are able to accept and modify their conclusions accordingly and without unnecessary user intervention. They analyze and update activity across a variety of attributes proven to impact the products and locations within the grouping and offer flexibility to navigate across those without being tied to unmanageable hierarchical relationships. They also derive learning about individual products to the point that the need for clusters is either significantly reduced or eliminated in many processes throughout the management of inventory (such as replenishment). If your processes are not supporting that level of locating management and practical clustering, it’s simply not possible to achieve what is expected when discussing localization in retail.

Consider the following as you think about the quality of your clustering practices:

  1. Do you cluster only on volume or on other attributes or KPIs too?
  2. Do you cluster only on historical sales or do you incorporate missed opportunities / lost sales?
  3. Do you cluster based on a forecast of demand for the periods you’re using the clusters to represent?
  4. Do you re-evaluate your cluster assignments as often as possible?
  5. Do you re-cluster in-season?
  6. Do you cluster the stores as low on the merchandise hierarchy as is reasonable for your business?
  7. If you change cluster definitions can the systems that use them accept that change cleanly?
  8. Do you drive re-allocations or replenishment by clusters?

If the answer to any of the above questions is “no”, there is room for some self-evaluation and improvement to your localization strategy.

Look out for next week’s blog on SKU Rationalization.

Sign up to receive updates throughout this series.

2 people like this post.
Unlike

Fashion Forward—Part 7: Advanced Multi-Level Distribution for Retail

by Quantum Retail • August 4th, 2011 • Write a comment »

How to capture the most profit for your inventory investment

In fast fashion, speed is key not only in the changeover of your assortment, but in the logistics that manage your supply chain. When you are dealing with 4-8 weeks of supply in a fast fashion assortment it may seem that there is little time to do anything but allocate your stores and markdown the excess, but it is likely that you are missing sales in the process.

What it takes to turn those losses into profit is a smart holdback strategy, real-time demand awareness, and a multi-level distribution method.

For many fashion retailers, it is common practice to use distribution centers simply as a flow-through point as they allocate their stores. But by pushing all of your inventory out to stores in a one-shot allocation, you lose any chances of utilizing real-time trends from your stores to place the rest of your stock where it has the most potential.

Even if you think you have the best forecasts and order plans, nothing compares to true sales insight. If you can instead, allocate 60-80% of initial supply to cover the minimum presentation quantity, you can learn which products are selling best in which stores so you can easily re-assess your order quantities for the rest of the assortment’s life and then push the rest of the merchandise to the stores where it will make the most profit.

Retailers that can turn their distribution centers (DC’s) into a holding location for real-time replenishment will increase their profit, inventory efficiency and service levels. To take it a step further, retailers can even break down packs at the DC to smaller packs or eaches in order to ship the most optimal quantity based on profitability at every location.

An alternate approach at holding back stock is to break the order into two allocations and receive them a few weeks apart. This can be an effective alternative to retailers who have constraints about holding merchandise in their DC’s.

To make the most of your hold back process, it pays to have multi-tiered distribution centers, ranging from national (NDC) to regional (RDC) and to allow replenishment from multiple levels. By allowing replenishment from multiple levels and utilizing routing logistics to analyze the cost comparison between each level and the store, you can assess if it is more cost effective to replenish from your RDC, your NDC, direct from the vendor, direct from a franchisee or direct from another store.

Challenges for retailers

Speed: The speed of turning demand forecasting into execution is the biggest challenge for retailers. Distribution centers, buyers, and business analysts must have the right tools to act and execute on real-time demand in order to keep up in today’s cutthroat retail environment.

Data: In order to make the replenishment process faster, retailers need better data. Data needs to be real-time and a retailer’s processes need to be granular enough that they can understand the need for every item and every store, not just at the cluster level. The quality of the data is also crucial. Retailers need to be sure that they are receiving data that is trustworthy of true demand. Understanding the need at the lowest level and working that demand insight from the bottom up through the supply chain are key to optimizing supply chain performance.

Distribution center constraints: Some retailer’s distribution centers do not have the capacity to hold inventory, or the logistic power to manage real-time replenishment. But enhancing these management processes and increasing the capacity for short-life goods as well as longer life stock will ultimately pay off through full-price sales and customer service increases.

Allocation and replenishment processes: Many retailers do not find it feasible to utilize a process of holding back stock for shorter life products and end up cutting corners by doing a one-shot allocation. It will be worthwhile to do a feasibility assessment to see where you could be saving money in your distribution, allocation and replenishment processes.

Advanced Multi-Level Distribution (MLD) and Order Planning/Warehouse Replenishment Technology

These ideas seem great in theory, but without the right tools and capabilities in your supply chain, these theories may be far-fetched. One of the only tools on the market that has the intelligence to replenish in real-time through the process of multi-level distribution is Quantum’s solution, Q.

Q multi-level distribution works in conjunction with the order planning/warehouse replenishment and ordering. Q order planning and ordering utilize demand side information to request inventory at different levels of the supply chain. Q distribution takes the inventory actually available in the supply chain and moves it through the supply chain based on demand and profitability.

Allocation and Replenishment

Typically, the distribution of inventory is grouped into allocation or replenishment. The former is a top-down mechanism of looking at like item historical data to send inventory to stores for new items, or those with a very short life and limited inventory. Allocation uses rules to balance inventory across the chain when given limited goods. Replenishment is a demand based requesting of inventory, assuming an unlimited supply of goods.

The actual world sits in the middle of these two. Rarely does the supply match the demand, but demand is often available to make better decisions about where to send inventory.

  • Short-lived items can still use customer demand to request inventory
  • Long-lived items have inventory shortages
  • Items are often bought in inner packs, cases, or multi-packs, so the ideal amount cannot be requested.

MLD Functionality

Q distribution utilizes the demand side information with allocation principles of balancing inventory to distribute each incremental unit of inventory based on where it is needed the most to support inventory strategies.

Q distribution principles can be used to:

  • Request inventory where item packaging or vendor constraints do not allow for the requesting of single items.
  • Allocate inventory that is cross-docked. Even when inventory may have been requested at store level and then aggregated, at the point it reaches the DC the optimal inventory need has changed, and can be re-allocated.
  • Request inventory from a pool taking into account the desired lifespan of the product.
  • Place inventory to best meet the strategies of the individual product/locations.
  • Protect Inventory for a group of locations, or ensure that all location groups are given equal treatment.
  • Restrict allocation to individual locations to best manage supply shortages.
  • Push out inventory to locations to maximize sales from surplus.

Make more profit with smarter strategies

When you take the leap to optimize your supply chain, ordering, planning, allocation, replenishment and distribution processes, it will incrementally increase your efficiency and result in lasting profit gains for your company. Utilize today’s technology and become an industry leader.

That concludes our Fashion Forward series. Look out for the next series on Optimizing Hard Lines.

Sign up to receive updates throughout the series.

Read more on Strategies for Hold Back Stock HERE»

Read more on Strategies for Linking Your Data Across Channels HERE»

Be the first to like.
Unlike

Fashion Forward—Part 5: Linking Business Intelligence with Strategic Execution

by Richard Mills • July 14th, 2011 • Write a comment »

In retail today, the sheer amount of consumer shopping data coming into the business is overwhelming with data from every channel, lying in multiple facets of operating systems, crossing each department of your business. Even stores that traditionally carried basics or relied on single allocations per season are dabbling in some form or another with fast fashion because newness is what brings customers into the store, and that newness is what gives retailers a unique edge over the competition. But when you’re exploring quick-turn merchandise, your real-time data is even more important.

It isn’t surprising that these retailers are seeking business intelligence (BI) tools to simplify that data into usable knowledge for more insightful decision-making. Most of these traditional BI tools gather and provide insight and information into achieving business goals, but it’s linking the business intelligence with execution that actually improves performance.

Business complexities

Though BI tools are often the go-to solution for many retailers, even the best tools are not enough to help retailers make optimal decisions. The complexities in retail today require these tools to do more than just give an answer; they need to trigger an action.

Having more than one customer with different needs and wants is just the beginning. There are increasingly more and more channels of customer behavior to understand along with multiple store formats for brick and mortar stores as well as new purchasing outlets: e-commerce, f-commerce, mobile commerce, catalogs, and social media. There is no longer one single way to buy and communicate.

The business complexities don’t stop there – the inventory placement decisions retailers have to make on a daily basis are more daunting than ever. For example, let’s say you have 500 stores with three buys or receipts, multiplied by 10,000 SKUs; you’re looking at 5,000,000 decisions to make each day.

Data proliferation

This complexity breeds data proliferation. The new consumer channels and store formats provide ample amounts of information. There is so much data coming in that retailers are struggling to put it into context for solving a problem and are struggling to keep up with how shoppers are behaving on a daily basis. Therefore, it is essential to understand that historical sales don’t mean as much when behaviors and customer patterns are as erratic as they are today. Retailers need to be able to monitor how their customers are acting now. This means that BI tools need to be able to create and react to shopper insight as quickly as possible.

And when you are dealing with fast fashion you don’t have the liberty of comparing the historical sales data, because the styles and colors of last season are not applicable to the wants of your customers today. You would be shooting yourself in the foot if that were the only data you based your decisions on. You need to utilize real-time demand by transforming it into an action plan for allocating and replenishing your stores.

Failure of traditional tools

BI needs to be actionable and align these actionable decisions with tactical execution, which old systems don’t do. They also don’t give visibility to lost sales; they don’t see the areas where they could have more sales but missed out due to inventory and real demand. Most systems don’t understand product lifecycles, especially in fashion when the demand you’re seeing at the moment isn’t necessarily a true reflection of what you’ll see next week. Lastly, traditional tools remain static instead of dynamic ceasing to learn over time. Instead, they only give a limited picture of the frame of time the user inquires about.

With all that said, in order to make the most of BI, retailers need to have a strategy in place as to how to execute actionable merchandising decisions in the most effective way.

Linking the science of BI with the art of merchandising

In order to link the science of BI to the art of merchandising, retailers need to start asking strategic questions and understanding a number of components. How can that be done? It is critical to define your strategy: what are you trying to do with a product, why are you buying it, and why is it important to your assortment? In other words, determine your product’s role, goal, and operational constraints. Keep in mind that any strategy must be put into a business context and executed in a defined business process that ensures you deliver business value. Continuously analyze behaviors and use it to make predictions.

You also need to imbed the strategy into the technology, not just the process, making everything part of your systems and eliminating the gap. This, in turn, creates visibility of your business context so you know why things are happening especially when they aren’t going right. BI should also provide all the information necessary for decision-making by putting it together and eliminating data choosing.

Better data leads to better decisions

There are principles that should be applied that address and deal with all the issues of today’s retail and add value to BI information. BI data should be:

Real time: or as close to real time as you can get providing a continuous stream of information.

Predictive: in a sense that the insight is used looking forwards, not back.

Business strategy-lead: so that it has business value to maximize profitability or to maintain a certain image—whatever your strategy is, applied to the analytics.

Goal seeking: because the goal is always changing, BI needs to be adaptable, seeking to improve and evolve itself over time.

Actionable for a purpose: not just for general information.

Continuously monitoring product behavior: to better understand that product in relation to store locations and store size profiles.

A shared pool of knowledge: pool knowledge together to be used for collective learning.

Self-improving: must be able to learn because servicing technology defeats the purpose if it involves business user intervention.

Optimizing BI

So now that you have your data, it’s critical to know how to optimize your BI. You can begin with reframing your question: change your business strategy and change the way you look at your problem (i.e. replenishing inventory). Use the insight BI has gathered to predict future demand and then put it in a business context with your strategies to help understand consumer behavior and how it affects business performance. Make sure what you take away from BI is actionable and that you actually deliver results on the answers you gather.

BI is only as good as the questions you ask it. Consider asking new questions: given what you know about your customer, your product, your supply chain, how do you make the most profitable inventory movement and placement decisions, especially when there are at least 5M different decisions to be made every day?

In order to reap the benefits of the results you achieve with BI they must be measurable. If you can’t measure them, how do you know if you were successful? You cannot truly optimize your BI processes unless you can learn and adapt from your mistakes, and imbed them into the execution process, so that when you are faced with a similar situation, you can be certain of the proper way to respond.

Turning BI into automated execution

There are sophisticated new systems that give the user the ability to set up minimum constraints for their product performance that will create alerts when performance falls below those constraints, meaning that they can simplify their time by focusing on the areas of their merchandising process that need attention. The most sophisticated systems available can even use the BI it takes in from SKU/store data, and automatically review the data it needs in order to make decisions and recommendations about what to send, how much to send, and where to send it. This is the most advanced way of integrating BI with inventory execution.

Benefits of BI

BI doesn’t just give retailers the opportunity to realize their problems and create strategies that produce results, but it also leads to a more profitable, better business. BI tools help you sift through the data to see where your real moneymaking opportunities are and give you focus for greater results and better benefits. When BI is executed properly, it leads to significant benefits: increased sales, reduced markdowns, and business growth.

When a retailer can integrate their data with their inventory management processes, they will truly make the most use of both their BI solution and their inventory investment.

Look out for the next blog, on multichannel strategies for retail.

Sign up to receive updates throughout the series.

To read more on BI, check out this post on Busting the BI Myth at: http://quantumretail.com/2010/11/17/busting-the-myths-of-retail-3-business-intelligence-bi

To learn more about Q and it’s constantly learning Qi engine, that links BI with inventory execution, visit: http://quantumretail.com/q-platform/qi-engine

Be the first to like.
Unlike

Competing in UK Retail – Part 2: Creating a catalyst for retail change

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

“I don’t think this is actually about technology. I think this is about business results. I think what retailers need to look for is a partner that can understand their problems, understand their business, and can demonstrably show that they can help deliver the project that they need, deliver their business plan, and help them with their long-term strategy. Although we have the best technology, it’s really only a tool to get you the results, and that’s what Quantum is all about.”

LISTEN TO PART 2:

speaking with Wyndham Albery, VP Customer Strategy, Quantum Retail • June 2011

Audio clip: Adobe Flash Player (version 9 or above) is required to play this audio clip. Download the latest version here. You also need to have JavaScript enabled in your browser.

Audio Transcript

Thanks to our audience for joining us today. I am Dan Brown, from Mulberry Marketing in San Francisco, and I am joined with Wyndham Albery, VP of Customer Strategy at Quantum Retail in the UK.

Thanks for having me Dan.

This series is focused on the challenges that UK retailers are facing and what they need to do in order to compete in today’s challenging retail landscape. So Wyndham, is there a theme of what retailers are looking for in technology right now? What problems are they trying to solve?

Well at the moment, the real focus is on improving business performance. The key thing that retailers have at the moment on their agenda is to improve their top-line sales, make sure they can minimize their waste or markdowns, and really manage this profitably because obviously for any retailer, profit is the most important thing they have. So for our customers, it’s really doing all of those things at the same time while supporting their strategic customer offer.

Do retailers need to change their way of thinking to really solve these problems?

Well the old and traditional ways of running retail systems pretty much peaked in the late twentieth Century. So today, those solutions do not really support the complex world and the fast-moving pace of retail. You know today, compared to 20 years ago, you need multichannel, you need to be able to be multi-format, you need to be able to manage a huge number of SKUs and their rapid entry and exit. So, yeah, the world has really changed, not to mention the fact that there is a far more sophisticated consumer out there who is much more aware of the retail tricks, what’s going on, finding what they want. So, I think it’s important for people to understand that they do need new technology. Because the current stuff that’s out there with traditional systems is a bit like using a fax machine today. Yes it works, but it doesn’t really do what you need to do in today’s competitive world.

Why do retailers choose Q over the more traditional solutions?

Well I think it’s pretty simple. It’s really because the Quantum focus is about delivering results. And our customers appreciate that. You know, our goals are really to deliver their business case and measure how we’re doing against it, which they appreciate. Also, we are working to their time frames, which these days in retail is very fast. People need to get projects in and working and delivering results quickly. But of course, that doesn’t make any sense if there isn’t an ROI. And our ROI is pretty quick, you know, usually within the year. So, you know I think that’s why we’re being chosen. Essentially our projects have a fast payback, help our clients change, and move their business forwards. We are not just another IT project. Our customers see us really as someone who can bring in change and get them the payback that they are looking for for their investment.

How have UK retailers like New Look and Marks & Spencer been able to use Q to overcome the challenges they faced?

Ooh good question Dan. Our clients have a customer offer that they need to be able to fulfill. That promise is very important to them. And that promise comes in the form of having a great product, but also making sure that it is available for purchase. However, that doesn’t mean they can just bloat their supply chain with inventory. Or that they can have loads of markdown or waste at the end of the season or at the end of the day. So this balance is really very delicate but very important because it needs to be done these days at the store level. It needs to be done profitably, and really people have to remember that retailers aren’t charities. And nor are they trying to basically fill outlet malls, much to my wife’s desire, she would love it if they were full of interesting products to buy; I personally am trying to get rid of that problem. So you know, in this hard and complex market with very difficult economics, huge constraints on retailers, we are able to help them reduce their inventory, improve their availability to the customer, and really help them keep their customer offer and promise, and help them get to the profitability target they are looking for.

Your client’s vendor partners are also very much affected by Q. What has the vendor response been like for your customers?

Well I actually was talking to one of my clients about that the other day, and the feedback that I got was that their suppliers really appreciated getting forecasted orders that they can actually use. Use to plan, use to smooth their production and reduce the lead times in the supply chain–all of this is incredibly important for them. And when I asked them how ours compared, they said that ours was some of the most accurate that they had ever seen. So yes, it is very, very important for our clients to be able to provide their vendors and their suppliers with accurate forecasts.

That’s excellent. So how are Quantum projects different than those of traditional vendors?

Well we, like most things, have taken a different approach. You know, our projects aren’t just about wiring and a bit of software, our projects are quite different. They are really about bringing change to a retailer and more important than that change even is delivering the results, or the business benefit that the people are looking for. So although we are putting in software in our solution. It is really about measuring where they are today, understanding the business performance they are looking to get, helping them set up the system to do that and their processes and their goals and their targets, and really, turning on the software is probably the easy bit, what becomes interesting is getting them to achieve the change and the results that they are looking for. And we have a very structured methodology of test and control to measure our system against how the current system is doing, and making sure we are moving the whole business forward to achieve their business goals. So yes, for us it isn’t just about a project plan, this is really about a change plan for how they run their entire retail business.

You mentioned business benefit. Can you go into that in a little more detail?

Yeah well it’s a very important part to Quantum, and really our product is all about measuring the value we bring. So with the test and control we understand where their business is now, with the system we are able to set what they want to achieve, and really what we’ve done, for example with New Look, was improve their gross margin by about 4%, and that was through a combination of increasing their full price sales, and decreasing the amount of markdowns they had to take. And that was all about making sure the inventory was in the right place.

Marks & Spencer for example, we are rolling out Q to all of their categories, and again what we have seen is a better availability to capture the full price sales of their product, and minimize the amount of waste that they have to incur, because their product has a very limited shelf life–often as low as between a day and two days–so for them it’s paramount to have that stock where they are going to sell it, otherwise the waste becomes a huge burden on their business. So yes, this is what Quantum is really bringing to our clients, is that change in business performance.

Great. It’s very evident that you have strong customer relationships at Quantum, could you talk a little bit about how you build those relationships?

Great question Dan. Yeah, it’s important to us, very important to us. Because we spend a lot of time with our clients, understanding their problems. And like I said before, it’s all about getting the results that they are looking for. So we haven’t just been wiring in a piece of software, we’ve really been getting under the skin of their problems, what makes them tick, and what they are trying to do. So invariably we find is that we have a very long relationship with our clients, because there is always something else that they want to do, something else that they want us to improve. So we have some great technology and ways of looking at it. So invariably they ask us to either help refine something, or think about something new, so I’ve found that all of my client relationships go on and on, really, and it’s a great relationship that we have because it’s very mutually beneficial. In terms of, our product road map is driven by our customers, we don’t make it up, they tell us what they think is important. And that I think is a great testament to the relationship that we have with our customers.

Fantastic, thank you. Do you have any last advice for retailers that are looking for new technology right now?

Very interesting question Dan, and I hope maybe I have a point of view on this, and I’m not sure it’s the one that you’re expecting. I don’t think this is actually about technology. I think this is about business results. I think what retailers need to look for is a partner that can understand their problems, understand their business, and can demonstrably show that they can help deliver the project that they need, deliver their business plan, and help them with their long-term strategy. Although we have the best technology, it’s really only a tool to get you the results, and that’s what Quantum is all about. And really, I think if you want sexy technology, go to an Apple Store.

Ha ha. True, true. Well thank you very much for joining us Wyndham.

Well I really appreciate you asking me, Dan. And I look forward to seeing you again soon.

And thanks to our audience for listening. Join us next week, when we will be speaking with Caroline Ragouzaridis, Director of Customer Strategy at Quantum Retail.

Be the first to like.
Unlike

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.

Sign up to receive updates throughout this series.

1 person likes this post.
Unlike

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.

Sign up to receive updates throughout this series.

2 people like this post.
Unlike

Competing in UK Retail – Part 1: Facing a challenging new market

by Quantum Retail • June 21st, 2011 • Write a comment »

“‘How am I going to deliver to the customer promise,’ but also grow the business, look at international, and look at different channels to market, and bringing that all together is the biggest challenge that retailers have today in the UK and in Europe.”

LISTEN TO PART 1:

speaking with Richard Mills, Solution Architect, Quantum Retail • June 2011

Audio clip: Adobe Flash Player (version 9 or above) is required to play this audio clip. Download the latest version here. You also need to have JavaScript enabled in your browser.

Audio Transcript

Thanks to our audience for joining us today. I am Dan Brown, from Mulberry Marketing in San Francisco, and I am joined with Richard Mills, Solution Architect at Quantum Retail in the UK.

Hello. Thanks for having me Dan. It’s great to be here.

This series is focused on the challenges that UK retailers are facing and what they need to do in order to compete in today’s challenging retail landscape. So Richard, you meet with UK retailers on a weekly basis. What would you say are some of top today’s challenges and pain points that they are facing right now?

Well I think Dan, at the moment we are at a very tough economic environment, and I think the real challenge for retailers is how to be profitable and how to make money. And I think it does come down to that. I think the situation we’re in with the squeeze that’s being put on the general public, it’s very hard for retailers to actually do those things. And it’s even harder when people are looking at their discretionary spend and not spending the amount of money that they were in the past.

With that said, I do think customers are actually getting more and more demanding in terms of what they are looking for from a retailer, whether that’s price, service, quality, and convenience. And I think that these are some of the challenges that the customer is putting on the retailers–to actually deliver on those things. And on top of that you’ve got retailers looking at international growth and how they are going to grow and multichannel and incorporating that into their business.

That is an even bigger challenge of “how I’m going to deliver to the customer promise,” but also grow the business, look at international, and look at different channels to market, and bringing that all together is the biggest challenge that retailers have today in the UK and in Europe.

And what do retailers need to have to overcome these challenges?

I think what they really need to do is to figure out how they go about differentiating themselves, how do they make themselves different to their competition, how do they actually look at themselves and add value to the customer. And actually what is that? What is that value to the customer? Is that around better service? Is that about better availability? Is that around a better product offering? A better format offering? What are those things? And then once you really understand what you’re trying to target your customer with and who that customer is it’s how do I then really deliver on those things. If I really want to deliver on better service to the customer – I really need to start to understand that customer and understand that customer in every single location that I have.

So that I can actually get to that level of understanding that then I’ve got the right products to service that customer. So it starts with – okay if I really do understand those different locations, what are those products that I need to put in those locations to actually fulfill on what those customers are looking for in those locations. And then when I understand that it’s okay well when do I put them in there, so what time? Because if I’m looking at different times of the year, and I’m looking at different geographies, and different formats, and different channels, and actually that will have a difference. So it’s not just I’ve got to put that product in at the same time, it’s actually there could be a difference in different timings of those products into those locations. And also, then really, what are the quantities I need to put in there. And when I mean quantities, in a clothing environment it’s actually by size, with fitting potentially. And in a food type of environment, it could be by flavors, by colorings, and all those sorts of things. And then on top of that it’s not just about service, I’ve also got to balance what then does that mean in terms of potential markdown and wastage balanced with all the sales I’m going to make. Because all of the way through this I cannot just go after sales, I’ve also got to have a profitable business and I’ve got to balance that customer service with the sales I’m trying to achieve, and also what the potential costs are around markdowns and wastage.

Localisation is obviously a very important issue, and a major challenge for retailers that are expanding across Europe and the rest of the world. How are retailers attempting to meet this challenge right now?

Dan, I totally agree. Localisation is a big challenge. And it’s really some of the things I’ve already talked about, but if you explore that a bit further, it’s really, if I’ve got more products, with more channels, more markets, more formats, and more locations. What that then means is, I’ve got more data to deal with, I’ve got more complexity, and that means I’ve actually got more missed opportunity and more missed profit. And that’s really, those challenges, no one’s really coping with that really well in today’s environment. Some companies are actually throwing people at it, because of that complexity, but that is a real challenge. And you really need to get to that level of understanding in those different locations, what the customer really wants. And it’s like treating each of your locations as an individual and really understanding what is the product mix, what are the quantities I need to put in there and when do I need to put them in, and getting to that level of understanding is really that challenge of understanding localisation.

And how can retailers better meet the challenge of localisation?

Well I think, if you look at retailers that are leading today, they have invested in good processes in their business and good systems to support those processes. What do I mean by that? Well these systems that people have invested in actually really do get to a level of understanding of every SKU in every location whether that be international locations, local locations, and understanding what sort of sizes I need to fulfill in those locations, what colors do I need, what flavors do I need, and really, getting that level of detail, and then it’s that understanding of those items in those locations.

What do I mean by understanding? Well, firstly what you really need to understand what the demand is. What we mean by demand is not just the sales, but sales that I’ve lost and making sure that I understand that for the future, so I look into the future and not just into the past. Understanding what the time of year looks like, the seasonality, even with some retailers, understanding day of week and how that impacts, understanding lifecycles of product is particularly relevant in a fashion and clothing environment. Understanding what promotions are coming up, what events are happening in the future and how they are going to impact my business and how I deal with them.

And then once I understand that from a demand point of view, I’ve really got to take that back through my supply chain and understand how my supplies are going to fulfill on that demand for me and deliver on my supply chain, and understand how my supplies are going to fulfill on that demand for me and deliver into my supply chain in terms of some of their constraints in terms of lead times, around vehicle constraints and actually then how that flows through my business, through my own supply chain.

The list is endless, but these are the sorts of some of the things that you have to understand, and you really need to understand them at SKU/location level.

What about product performance? The top line is very important to keep up, how can retailers make their inventory investment more strategic?

I think this starts with, you’ve really got to understand what the role of the product is in your business, what are you trying to do with that product, is it a product you are making profit out of, is it a product that you are driving sales through, or is it a core product in the business and really try to understand those things and say, well ok what is the role of the product in the business, what am I trying to do with it. And then once you understand those things it’s easier for you to balance those dimentions of well, I need to balance sales with service, with markdown or waste, and actually come up with an inventory that actually fulfills on that obligation of driving profit for the business. So some items you will give up some profit to drive sales, other items you will give up some sales to minimize wastage or markdown, and it’s really understanding that combination so that actually I’m making money for the business, I’m not just throwing inventory at things and losing money for the business. And it’s really those dynamics of those things that you need to take into account.

Now, to do that – there’s another dimension. You also need to give your people time to plan ahead. Because this is all about it’s ok dealing with today, and so many people are just out there firefighting because their systems and their processes really just can’t cope with demands of today, where we’ve added in new formats, we’ve added in new product groups, we’ve added products to the range, we’ve added stores to the range, we’ve gone international, we’ve added a new channel, we’ve gone on the web, we’ve gone catalog. All of those things make big differences because each of those things need to be treated individually. And the way that a SKU behaves in those different channels and those different formats needs to be taken in to account.

Now, what you need is actually things that support your people that can actually manage by exception, show them where the things are going wrong, but automate a lot of that information that people need, in terms of understanding what the time of year profile looks like, understanding where did I lose sales, understanding what the day of week is, understanding the lifecycle. What that does is, it enables them, gives them time to focus on the future, to focus on planning their new range, planning the new items into the business, planning the next season, planning events and actually giving people the time to do those things.

That’s very insightful. Do you have any last advice you’d like to share with our listeners?

Yea, I think it’s a very challenging economic climate that we’re in at the moment. I think you have to give customer value, but I think you really, can’t give up customer value for the sake of profit as well. Because we’re all in this to make profit for the business. And that’s what it’s about really. People can just go and chase sales, but actually at some point in time that doesn’t work. You still have to make money, otherwise you can’t invest for the future. And I think that the thing to do that, if you look at the people that are still growing in this challenging environment they do invest in technology, they invest in technology that understands and learns and actually allows people to focus on the value add, allow people to focus on what makes a difference to the business, allow people to focus on new seasons, new items, and promotion activity.

Wonderful. Thanks very much for joining us Richard.

Ok, thank you very much, it’s been a pleasure Dan, thank you.

And thanks to our audience for listening. Join us next week, when we will be speaking with Wyndham Albery, VP of Customer Strategy at Quantum Retail.

1 person likes this post.
Unlike
« Older Entries
Newer Entries »