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.

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Read more on Strategies for Hold Back Stock HERE»

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

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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.

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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

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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

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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.

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

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

By Dan Moran, Product Strategy, Quantum Retail

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Fashion Forward – Part 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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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

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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.

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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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Fashion Forward – Part 1: Clustering is not localization

by Quantum Retail • May 25th, 2011 • 5 Comments »

It has been quite a while since your typical fashion retailer was able to merchandise their stores individually. It has long been common practice to group stores together based on some commonality. On the surface this makes practical sense. One merchant or allocator can’t handle hundreds of stores themselves, so grouping the stores together is a logical way to deal with the problem of location proliferation.

The problem is thinking that clustering via methods developed in the 1980s and perpetuated heavily today is the best way to localize a chain’s assortment. It simply is not. It’s high time we reevaluated clustering to see where improvements can be made. Limitations of yesterday’s merchandising systems were a legitimate reason to use simple clustering to manage the complexity. However, today’s systems are much more sophisticated and need not cripple a merchant with unnecessary constraints like before.

The most common way to cluster today consists of clustering stores based on volume. Typically, this volume is sales volume and the number of clusters varies, but basically is what the retailer is able to handle. The average number of clusters that a fashion retailer uses falls in the 6-8 range, but you could find many that use significantly more.

Unfortunately, volume based clusters have three inherent problems:

  1. The clusters are almost 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 typically only historical sales 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 a demand forecast. Demand incorporates what would have happened had the store been in stock. Of course, using a true demand forecast which includes understanding those missed historical opportunities to cluster would be the best of both worlds. Not only would you be incorporating lost sales, but you would be incorporating future trends of store behavior as well.

Another problem is when clusters are used and how often they are evaluated. Store assignment to clusters should be evaluated as often as possible. Stores should be clustered pre-season when a buy plan is made, again when the order is placed, another when the buy is pre-allocated, another when the goods hit the DC, and so on. Sometimes this isn’t practical, such as direct to store prepacking for cross-docking, but in the absence of store commitment, stores should be re-clustered. This increases the data used for clustering and makes it more current and more accurate. Furthermore, clusters should never be used in the replenishment process. Once the goods have hit the stores, any re-allocation/replenishment activities should not consider clusters. Store behavior is too erratic, especially in fashion, to rely on clusters in-season. There is simply no good reason to use clusters after the first allocation and even then it’s arguable whether they should be used at all. 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 really provide huge benefits. Refining a store’s size allotment, replenishing better sellers and, as importantly, not replenishing items that are destined for markdowns, should be store specific decisions.

There are certainly a lot of retailers using clusters that are more sophisticated than simply volume, but it is nowhere near universal adoption. Climate is by far the second most common store attribute on which stores are clustered. But are there other attributes of stores and/or merchandise that would refine the localization strategies better? I would argue both yes and no. Pre-season, it would be beneficial to add some attributes such as price point (merchandise), economic status (store), fashionability (merchandise) or similar attributes to the buy decision to better assort the stores based on consumer behavior. On the other hand, I’ve stated earlier that in-season clusters are useless. They only result in normalizing out store uniqueness. It matters not why a store is selling an item well, it only matters that it is!

I challenge you to question your clustering cadence and ask yourself these questions:

  • Do you cluster only on volume or on other attributes or KPIs too?
  • Do you cluster only on historical sales or do you incorporate historical or forecasted demand?
  • Do you re-evaluate your cluster assignments as often as possible?
  • Do you cluster in-season?
  • Do you cluster the stores as low on the merchandise hierarchy as is reasonable for your business?
  • 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 optimizing size/packs by Dr. Ziad Nejmeldeen, VP of Science, Quantum Retail.

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This Retail Life – Part 4: Quantum CEO Vicki Raport talks about transformational success for retail merchandise optimization

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

“There are a lot of retailers out there who are aggressively moving into the market, to try and figure out how they can be more consumer-driven, how they can optimize their assortments, how they can be hyper-responsive, how they can be more productive, and how they can design into their business everything that they do to be profit-seeking. So if you aren’t thinking about your business that way, one of your competitors is. So I encourage retailers to stop being confined by the way their technology behaves today, and to start thinking about what they need from their technology in the future.”

LISTEN TO PART 4:

speaking with Vicki Raport, CEO Quantum Retail • May 2011

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Thanks to our audience for joining us again this week. I am Dan Brown from Mulberry Marketing, in San Francisco, and I am joined with Vicki Raport, CEO of Quantum Retail.

Thanks for having me Dan.

For the last few weeks in these interviews we’ve been talking about the concept of “new school” vs. “old school” retail. Most of the founders and current employees have come from other retail technology vendors. Would you say people are relieved to get away from the constraints of those other companies?

Well absolutely, I mean, you don’t come to work for a small high-growth company because you want to stay with the constraints of a larger beast. You know, really, when people come to work for Quantum they come for probably three key things:

One is – they want to be part of something that is innovative. You know, both engineers and business people alike have a desire to be creative and to have their work shine as something innovative in the market place, and we think we give everyone the opportunity to do that.

The second thing is that when you come to work at Quantum vs. probably some of our bigger and older rivals, everyone matters. We are still a small enough company that the contributions of everyone in our company makes a difference to our company. And as many of us know, when you are working in ten billion dollar company, it’s hard to feel like, as a single person, what you’re doing makes a difference, and in Quantum, everyone makes a difference.

And last, but certainly not least, is that people who work with Quantum want to be close to the creation of value for the customers, they want to be able to identify the things that they are doing with the ways that they are delivering value in the market place, and we allow people to see those results. We celebrate the wins of our customers as if they are the wins of our individual company. And everybody who contributes to that value is acknowledged. And those things together are things that I don’t think you find very easily in larger companies and certainly people who are doing things the old way.

That’s great, I imagine that some of our listeners are going to want to send you their resumes.

(laughs) Well they can go out on our website and we have a nice little place that they can let us know. We are definitely in growth mode and we are still hiring.

That’s great. But let’s go back to the beginning. Why did the founders of Quantum break off and start their own company – were they fed up?

Well I think that, why people found their own companies really has a lot to do with entrepreneurial spirit, and people wanting to be able to start with their own ideas and bring something unique and innovative into a market place that they have confidence that they can add value in. So from the beginning, the founders of Quantum Retail really understood that in the market today, and certainly when we started the company, retailers were and are facing significant challenges in addressing the complexity of their business and achieving their desired productivity and profitability from their inventory and certainly on their ability to deliver on the customer brand promise in a volatile market.

And to do that they really needed something new. They needed a solution that was easy, intuitive, was not disruptive to their people and processes, and really thought about things in a new way. So when we formed Quantum Retail, we really were thinking about, “how do you add new value into an old market place?” And that was the foundation of why we started the company.

Why was a new school solution necessary?

Well first of all, the technology that most existing ERP solutions and most technologies that have been implemented in the last twenty years at retailers, are probably based on technology and thinking that was at least thirty or forty years old.

And, let’s face it the world has changed. Right, I mean five years ago, who would have thought that Apple would invent this little tablet PC called an iPad and generate a $5 Billion dollar business overnight. I mean, the world is completely different than it was thirty or forty years ago, it’s different than it was twenty years ago, and it’s different than it was ten years ago.

So New School is something that doesn’t happen just once. It happens on an ongoing basis. And what we really have to be looking at is, how do you take the new thinking and new technologies, and go after the hardest problems that are out there in the marketplace. Our expertise is in retail. And we go after those components of retail that represent high value for our customers, and can fundamentally change their ability to be profitable and successful. And existing technology and old ways of thinking, they just don’t offer those capabilities anymore.

And we really felt that we had to create a new future for retail, and you couldn’t start from where you were in the past. You just couldn’t get from there to where we are today.

So how did the founders create the concept for Q, and was it hard starting from the ground up or was that an essential component?

Well the answer is both. It is very hard to start from the ground up, but it is something that we did. We literally, you know, what we came to the party with was our understanding of the retail industry and the pain that retailers faced in utilizing technology and leveraging technology to deliver profitability and value, and we started with no technology.

We had our assets of knowledge in the marketplace, but we really started from the ground up. And that’s very hard, you have to start with a blank sheet of paper, you have to go out and do your own research, you have to have a lot of creativity, and a lot of ability to think outside of the ways that you have historically thought about solving problems, but as I said before, and to use your words, it really was essential. We could not develop our solution to be able to look at data at an atomic level, such a granular level to solve really complex problems like, localizing assortment and seeking profit, and driving to consumer demand.

We couldn’t do that with the existing technology. If we started from that, we wouldn’t have been able to get where we are, so it was absolutely essential that we do that, and it’s really foundational to why retailers are drawn to our technology solutions. We look different, we behave different, and we offer a different set of results which are much better than any of our competitors.

So you mentioned a few, what I would say are great selling points right there. But what has been the biggest selling point of Q for your customers?

Probably the biggest selling point for Q with our customers, is our ability to deliver value quickly in a highly complex business environment, and when I say highly complex business environment, now we’re talking about, all of the formats, and all of the assortments, and all of the geographies, and all of the different verticals, and all of the ways your products behave, and the different markets, and the different channels and the different customer segments, I mean, this is a tremendous amount of complexity.

And to solve that problem using old technology requires multiple solutions, being strung and cobbled together to solve many many problems, whereas Quantum and Q, comes from a holistic perspective and look at the business as a complex operating system and use a single system to solve these overarching complexities.

And really that’s why people choose Quantum. It’s why our first customer went with us. It’s why Guitar Center picked us, because they knew that to solve the different types of problems that they had in their business, they would likely have had to implement three different inventory management and forecasting solutions if they went with some of the old school technology.

That’s great, so it seems like your prospective market is growing pretty quickly – how do you keep pace with the challenge of growth?

Well, it’s not easy, but it’s a good problem to have.

(laughs)

So keeping pace with the challenge of growth starts fundamentally with hiring and retaining good people. Quantum is still very much a family, though we are now over 100 people. We vet our folks very thoroughly, we try to hire people who are experts in their field, whether it’s engineering, or whether it’s retail, or whether it’s technology. And it’s really really important that you have great people who are really motivated.

The second thing is that you have to have good customers. And so, one of the things that we’ve done as we’ve built the company, is we find retailers as clients who are looking for us, and want to stay and be very, very, engaged with us. You know, we don’t hand our solutions off to some third party systems integrator and wish our clients good luck. We are extremely engaged with them. We continue to work with all of our customers actively, even years after they’ve implemented.

So probably the biggest thing for us is, having great people, and having great clients. And that’s what’s allowing us to keep our growth and being successful and profitable, rather than being a constraint to the business.

So where is Quantum headed next, what is your vision for the next few years?

Well certainly, we want to build on the success that we’ve had. So in our view, we have been very successful over the last several years, in landing what I would like to call our anchor clients. What we’ve done is we’ve managed to land some key marquis customers in each of the verticals in retail. We have customers in fashion, we have customers in hard lines, we have a customer in departments and mass, and we have a customer in food retailing.

And these represent the major verticals in retail, and for us, we are now being invited to all the parties, we are being asked to participate in many many of the tier 1 selections, and I believe that having these anchors in all of these verticals will allow us to now aggressively go into these markets and continue to convert retailers to the Quantum way of thinking and to get our solutions and applications up and running and delivering value in more and more retailers.

So, the first is to leverage the success that we’ve had.

The second is that Quantum is always innovating, so our vision is, we really want to become the foundation for twenty-first century transformation, as the leader in merchandise optimization for retail. And what that means is three main things:

First, we need to make sure that we are helping retailers align their capabilities to buy, move, and sell merchandise when and where their customers want to shop and in the most profitable ways. And we’re going to do that by addressing not only how we respond to demand but how we shape demand as well, so we really want to be a holistic solution for merchandise optimization and we are well on our way with our platform and with our solution applications.

The second is that we want to provide this transformation through technology that enhances business decision-making and execution in a way that people want to work. Probably one of the biggest things between old school and new school is that new school technologies are designed around the way that people want to work, whether it’s sitting at a work station, or on a mobile device or an iPad, whether it’s an on-premise deployment, or on-demand SAS solution, we’re always figuring out how do people want to be working with technology and the most productive ways, and that is paramount to our vision.

And then lastly, we want to do this in a way that creates what I would call an enduring platform, and enduring means, that the platform itself and the way that we work with our technology to deliver value has to adapt to the ever-changing needs of our customer, and our customer’s customers. And so, all of that means that Quantum is going to continue to innovate, continue to leverage the assets that we have in the market place, but to continue to bring more and more value to not only our existing customers, but our future customers.

Fantastic. So how has it been leading a startup? It seems like there is a lot of energy there. Is that what makes startups so great?

Um, yep. The things that make startups so great are that you have an opportunity to really make a difference. And you know, the founders of Quantum Retail, and really all of our employees are focused on being entrepreneurs. We are constantly looking at how do we bring something new and address a component of the market that isn’t being addressed.

And that’s why you start businesses, you don’t start businesses just to make money, you don’t start businesses because you are mad at your last employer, you start businesses because you really believe that you have something fundamentally different to bring into the marketplace and that difference has value. And that’s what sits behind Quantum, and that’s why all of the people  who founded Quantum are still actively engaged in the company and are very excited about it.

Awesome. So Vicki, are there any other winningest woman awards you can get, or have you won them all now?

(laughs)

Well I might have won them all now, because I think there was actually only one, but we got a lot of airplay out of it for a couple of years. But you know Dan, one of the interesting thing about some of those awards was that those awards weren’t about me. The awards were a recognition of Quantum Retail and Quantum Retail’s success in its early stages of growth and its continued growth.

I mean, when you get awarded a fastest company award, whether it’s a fastest woman-owned company or the fastest company in Minneapolis, or an Inc. 500 company, those are really the success story of our business, that we have been able to build a successful well-run business. My hope is that the future stories aren’t about me as the winningest woman, but they’re a lot more about our clients and their successes and the value that we’ve created.

Fantastic. Any last words of advice for retailers?

Yes. You better start thinking about your business differently, and you better start thinking about how you’re going to optimize your business, and an ERP system isn’t going to do it for you. And there are a lot of retailers out there who are aggressively moving into the market, to try and figure out how they can be more consumer-driven, how they can optimize their assortments, how they can be hyper-responsive, how they can be more productive, and how they can design into their business everything that they do to be profit-seeking.

So if you aren’t thinking about your business that way, one of your competitors is. So I encourage retailers to stop being confined by the way their technology behaves today, and to start thinking about what they need from their technology in the future.

Thanks so much for joining us Vicki!

You’re welcome!

And thanks to everyone for following along with this series. If you’ve missed an episode, you can access them all at The Profit Lab Blog.

Download a PDF of This Retail Life: Changing the Game in Retail»

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This Retail Life – Part 3: Battling the big vendors simply takes a new approach

by Quantum Retail • April 27th, 2011 • Write a comment »

“Some vendors have been working on and gathering more and more solutions that cater to retailers in an end-to-end manner. Examples are solutions coming from folks like Oracle, SAP, SAS, JDA, IBM, Epicor, RedPrairie, Island Pacific, Jesta, and others. But these solutions are mainly just minor variations to the same traditional processes that have been in retail for decades now. They have yet to really achieve the ability to learn from what they “see” and promptly adapt to that with recommendations that will meet the evolving needs of today without the need for user intervention all along the way.”

LISTEN TO PART 3:

speaking with Greg Wilson, Director of Field Strategy at Quantum Retail • April 2011

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Audio Transcript

Thanks to our audience for joining us again this week. I am Dan Brown from Mulberry Marketing, in San Francisco, and I am joined with Greg Wilson, Director of Field Strategies at Quantum Retail.

Thanks for having me Dan.

It sounds like Quantum is continuing its pattern of rapid growth, but I understand there is still some push-back in the market. Why are retailers stubborn when it comes to change?

Well there are really a few reasons that retailers are a bit resistant to change.

One of them is a fear of a something new – a concern of the learning curve of a different approach. They’ve gotten comfortable with processes they’ve created and nurtured over many years, and there’s a level of re-education that’s a part of taking on any new approach. Unfortunately, many of the pitfalls of traditional approaches come directly from dated technology, its constraints, and the old thinking that surrounds it.

Another reason is that some retailers are a bit scared of the term “automated” – they see it as taking the decision making process out of their hands. There really needs to be a certain level of trust established before they’ll consider any form of automation.

And finally, a lot of retailers have made significant investments in technology over recent years and that’s what’s driving their current process. Sometimes they are hesitant to put more money into something new, or something that they could build themselves. We do actually occasionally run into retailers that try to pry information out of us and get details about how we’re solving these problems so they can build it themselves. But ultimately it’s a waste of their time though. To build something with enough capability and sophistication to solve the more challenging problems, it’s incredibly complex. It takes experts in various disciplines of business technology and the science of econometrics and other statistics related to solving the problem. So by the time they just get started in building something, assuming they could even do it, they could already been achieving value from Q.

But the old retail foundations that most retailers are still using make it impossible to change their way of thinking, right?

Well to a large degree that’s true. Traditional systems are generally like a large spreadsheet of data – and the system enables you to enter parameters or rules and calculations on data that you select – they require a significant amount of user input and manipulation, and the only way to enable that, is to set and manage this stuff at higher levels of aggregate. The problem is that this pushes you further and further from the ultimate goal which is localization, or understanding and reacting to what’s happening at each location with each product.

Some vendors have been working on and gathering more and more solutions that cater to retailers in an end-to-end manner. Examples are solutions coming from folks like Oracle, SAP, SAS, JDA, IBM, Epicor, RedPrairie, Island Pacific, Jesta, and others. But these solutions are mainly just minor variations to the same traditional processes that have been in retail for decades now. They have yet to really achieve the ability to learn from what they “see” and promptly adapt to that with recommendations that will meet the evolving needs of today without the need for user intervention all along the way.

The traditional approach is also pretty limited into the scope of what it can address to deal with the really difficult products like big ticket slow-movers, sized merchandise, highly volatile merchandise, seasonal products, short-life products, perishables, pack-constrained products, and heavily promoted items, things that are scarce or vendor-allocated, or have long lead-times.

All of these things require new models and new ways of thinking to address them really well.

So how does Quantum’s “new school” way of thinking differ from that of traditional solutions?

Well, the Q Platform that we’ve built actually enables us to solve the problems that these other vendors mainly talk about solving. It delivers on the business case every time, with proven, measurable results. Quantum has developed the concept of managing by merchandising strategy–determining the role of the product within the customer offering, such as being an image item, a traffic driver, a profit generator or a fringe assortment item, or things like that.

Users aren’t asked to select and ultimately guess at an overwhelming number of forecasting or replenishment algorithms, and to set a slew of difficult parameters around each item. Q takes the strategy and understands the objectives of the product from both a financial and a merchandising perspective and it ensures that every inventory decision that it makes is aligned with achieving those objectives.

The way that customers buy product changes over time and Q adjusts automatically to react to those changes by constantly working to ensure that alignment is maintained throughout the products’ lifecycle. This is much different from having to actively maintain ordering, allocation, and replenishment configurations for every item in every store and manually ensure that the system is set up correctly to do this like other solutions do.

In the process of understanding items, Q considers over 30 dimensions of product behavior. Things like: maximum sales, true historical demand, forecast demand, days between sales, lost sales, days between out of stocks, current inventory position, last stock-out, weeks of supply, percent in stock, etc.

But beyond these typical sales and inventory metrics, Q also understands things like:

  • When an out of stock happens, an out-of-stock on Monday has different gravity than out-of-stock that happens on Saturday, when you’re looking at things in a weekly level.
  • Variations in contributing factors like: lead times, lifecycle, and service levels, which have a significant influence on making the right decision.
  • Variability in sales, such as volatility, lumpiness, lost sales, are very important to understand. And finally, and probably most importantly, are profitability metrics such as gross margin return on inventory investment.

So these capabilities have led to retailers being able to have a high degree of automation with Q using exception management to highlight only those areas where users should be spending their time efficiently and effectively in the system.

Can you talk more in detail about product strategies? Do they change from retailer to retailer?

Yes, absolutely strategies change from retailer to retailer. Often we find people using ineffective strategies such as simple min/max replenishments, or things like that.

What they really need is a true strategy for their products.

So, rather than just setting a target service level which is a guess, usually at some group of stores of what amount of inventory hits a service level that will meet an objective, they really need to define that objective.

For example, “What am I doing in the realm of profitability for a profit generator?” I need to understand how much service allows me to capture sales, but I need to make that subject to some constraints around waste, or potential losses from carrying merchandise. I also need to be considering the volume of sales that I am generating. So profitability might not capture every sale, but it will allow me to avoid waste as a result of not capturing every single sale. So balancing all of these things has been traditionally very difficult because you have to pick a handful of parameters and manually set them at some level that is manageable. But really this evaluation should be happening all the time.

So by enabling Q to operate to this strategy, you know what the goal is, and Q can dynamically set all of the parameters as it understands about the behavior of merchandise to achieve that objective consistently.

We also see a lot of retailers falling into the trap of thinking that it’s all about forecast accuracy. But for a majority of items the value of the forecast is really diminished, if not irrelevant. You’ve got values that are significantly less than one unit, at that point the decision isn’t really about, “is the forecast .04 or .05,” it’s about what is the right inventory decision to achieve that goal given that forecast and the volatility around it.

And other vendors talk about localizing, but they’re all just talk – aren’t they?

Well yeah, they really are. And the reason for that is–because of the complexity of the problem. They are aggregating some of the criteria that is managing their processes up to higher levels to make it easier for the user to manage. But that gets you further and further away from the unique characteristics of the individual products in individual locations, which is really what’s necessary for localization.

What Q does is it enables you to set a high level parameter, but it’s a dynamic parameter that says what your objective is, and by doing that, Q can understand the behavior of each individual store and each individual product, and put that together with your objective so it is constantly achieving that objective at that SKU/store level. And only when you do that are you really achieving localization.

You mentioned that Quantum always performs on the business case, every single time. Can you tell us how that process works. How do you sit down and decide on a proper business case with new clients?

Well first of all, when you meet with Quantum, we don’t shove a big list of clients down your throat. We ask you what your problems and opportunities are – and we help you build and define a strategy for your merchandise. Every item needs to have a role and each role needs to have a goal. And that’s what makes Q so unique, it doesn’t just keep up with demand – it keeps up with each item’s strategy and the objectives of that strategy.

Let’s talk competition. You are often among the final two or three vendors of choice. How do the big vendors compare to you? Are they intimidated by you guys?

I don’t know that the big vendors are intimidated by us, as such, but they are certainly aware of us. And they are aware that there is a new approach available. And more and more retailers are coming around to understand the value of that new approach.

In addition to our system’s adaptive nature, simple and intuitive navigation, ease-of-setup, and the fact that it’s exception-driven, we are able to get in place very quickly.

Implementations happen in fewer than 20 weeks, and the system’s light footprint means a smaller investment. We can actually employ on top of some existing structures and optimize them in some cases whether that be a short term objective or part of a longer term objective.

Our customers have reported fewer than 12 months to reaching a 100 percent ROI in every single case. And this is measured through test and control groups, where we take two groups of stores that are behaving similarly. One of them becomes the test group which we manage with Q, and one is the control group, which continues being managed with existing systems. Taking this approach ensures that the returns we talk about are only possibly a result of utilizing Q.

Being a small company, how does your relationship with the client change the solution’s effectiveness?

Well from the beginning of an engagement with a retailer, we establish a relationship, we become a key component of reviewing and improving their business strategies as they affect the whole of their company. We’re looking at the larger picture – and create a business case that is specific to them – and how much profit they can expect to achieve.

Being a smaller company also allows us to build out our product to the needs of our customers. We have three core products, assortment and range planning, allocation and replenishment, and order planning. And we are always in the process of making these products better, which our current clients get to enjoy – and when they have specific needs of their own, these updates sometimes become a part of the core product.

But we also have the fun of building a strong partnership with our clients, becoming team players – their success ultimately is our success – and we are always looking out for how we can make their business better.

And can you give us a last few words of comfort for retailers looking to make the leap away from their old tech into a “new school” solution?

Yeah, I think probably the most important thing for people to understand is that new technology, even though the process and the underlying mechanics are more complex, new technology is actually easier to use, it pays for itself in months, and it improves over time. You have nothing to lose!

Thanks so much for joining us Greg!

It was good to be here.

Tune in next week when we will be speaking to Vicki Raport, CEO of Quantum Retail.

Download a PDF of This Retail Life: Changing the Game in Retail»

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