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The Retail Supply Chain

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© Copyright 2011 Self-published by Steve Keifer All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher. Limit of Liability/Disclaimer of Warranty: While author has used his best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. The views, opinions, positions and assertions contained in this book are solely the private ones of the author. Nothing herein is to be construed as reflecting in any way the views, opinions, positions or strategies of GXS, any of its affiliates or any employee thereof.

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Chapter 25

Point of Sale Data In 2004, the hype surrounding CPFR began to die down as a new

concept was introduced to the marketplace called “demand driven supply chain.” The new strategy was promoted by experts at some of the world’s leading research universities and analyst firms. The most extensive promotion of the concept came from AMR Research. AMR, which has since been acquired by Gartner, was highly regarded as the leading analyst firm covering supply chain trends. In 2004, AMR introduced its “Demand Driven Supply Network (DDSN)” vision.1 Advocates of demand driven strategies postulated that manufacturers had been basing production schedules and sales forecasts on the wrong set of inputs. Historically, manufacturers had used the previous year’s sales as a baseline for forecasts. The forecasts would be fine-tuned based upon preset shareholder expectations and available manufacturing capacity. For example, a consumer products manufacturer might have 10 different plants which it wanted to keep 85% utilized on average. The manufacturer might have a workforce of 10,000 union employees which it wanted to keep 95% utilized. These internal financial metrics would drive how many products were manufactured and shipped into the supply chain. Supply chain professionals refer to this scenario as a “push model.”

One of the key shortcomings of the push model was that it did not factor in actual end-consumer demand for products. As a result, there was usually an excess or a shortage of inventory. A consumer products manufacturer might overproduce a particular SKU because it was attempting

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to optimize use of its plant and workforce. The excess inventory would then have to be sold at a discount or reclaimed and destroyed.

Demand driven strategies espoused that supply chains should be operating under a “pull” rather a push model. Inventory should be pulled through the supply chain based on end customer demand rather than pushed out in as great of quantities as could be produced. DDSN principles stated that manufacturing schedules should be driven based on the latest information obtainable about consumer purchasing behaviors. Through a process called “demand sensing,” consumer products manufacturers would obtain data about actual sales transactions, market price points, and trade promotion effectiveness that could be utilized to adjust sales forecasts.2

To successfully become demand driven, manufacturers had to implement many of the concepts introduced with CPFR. Most of the demand data needed by manufacturers was owned by the retailers. New levels of collaboration had to be achieved in order to effectively share and benefit from the information.

One of the advantages retailers have in the supply chain is access to information about consumer behaviors. Retailers have the opportunity to interact with consumers in their stores. As a result, retailers can gather a significant amount of information about what motivates purchasing decisions. Historically, the consumer information gathered by retailers was not captured in any type of structured way. Store associates interacting with customers might relay feedback from consumers to their management or the retail head office. However, improvements in store technology have enabled retailers to capture more data about consumer behaviors in electronic formats. The information can then be stored in databases, analyzed by retailers, or shared with manufacturers.

Point of Sale Data

The biggest collection point for data in a retail store is the Point-of-Sale (POS) or what you probably refer to as a cash register. The POS is the most important technology in the retail industry. Not only does it act as the real-time billing system for a retailer, but the POS also makes a record of all

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of the purchases that occur. For each product you buy, it records the name of the manufacturer, name of the product, and the purchase price.

Many grocery retailers have recently introduced loyalty cards that offer consumers discounts on selected purchases. To register for a loyalty program a consumer provides detailed information about their identity, address, income, ethnicity, and family size. Some retailers gather extended data about hobbies, interests, shopping preferences, and technology literacy from members.

Combining loyalty card data with POS transactions enables retailers to build a very detailed profile of each shopper. They know who you are, what you bought and when you bought it. Think about how useful this information would be to a manufacturer.

Historically, retailers shared very little about how much of a particular SKU is selling on a daily basis. The demand signals transmitted from a retailer to a manufacturer consisted primarily of purchase orders. These orders might be placed weekly or just a few times month. By sharing POS transactions with manufacturers, retailers provide insight into which products are selling in which stores and in what quantities. Analyzing POS transactions can help manufacturers identify low inventory positions that could lead to a potential out-of-stock. Armed with real-time consumer demand insights, manufacturers can proactively address product availability issues before sales opportunities are missed.

Market basket data, which is an extended form of POS, can provide valuable insights on competitive offerings. Market basket data shows the full mix of products purchased by a consumer during a shopping trip. For example, suppose you purchased ten items in a store visit. Two of the ten products were manufactured by PepsiCo: a 1 Liter bottle of Diet Pepsi and a package of Fritos corn chips. With market basket data, PepsiCo would be able to gather information not only about the Diet Pepsi and Fritos chips but also the other eight items you purchased. By analyzing market basket data manufacturers may be able to identify new product ideas that they can bring to market. PepsiCo might also be able to identify marketing techniques or promotions to gain a further share of wallet from you. For example, suppose

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one of the other eight items you purchased was sour cream and onion dip made by another manufacturer, Utz. The next time you visit the store and swipe your loyalty card, PepsiCo might offer you a coupon that encourages you to buy PepsiCo’s dip in combination with Fritos chips.

Challenges with Sharing Point-of-Sale Data

Despite the potential advantages of POS of data to manufacturers, many retailers do not make the information available. The largest grocery chains have programs in place to share POS data, but in a limited capacity.

The frequency of POS data sharing can significantly impact its usefulness. For example, historically the only way retailers shared POS data was via monthly “syndicated reporting.” Specialized reporting vendors such as IRI and Nielsen would trend POS transactions across regions or product lines, then sell the analysis to manufacturers for a fee. One of the key challenges, however, was that the analysis process delayed distribution by several weeks. Thirty day old data is not useful for replenishment and other supply chain activities, and is largely not useful for marketing, pricing, or trade promotions activities either.

The granularity of POS data sharing can impact usefulness as well. Some retailers only share aggregated POS data for their entire network of stores. Such “chain level” data is much better than having no insights to shopper behavior at all. However, chain level data cannot be easily utilized for targeted marketing campaigns to reach specific geographic regions nor can it be used to drive store-level replenishment decisions. Store specific data is the optimal level of granularity needed by suppliers to perform accurate demand planning.

The breadth of POS data shared can impact the insights that manufacturers can gain. Most POS sharing is limited to basic fields such as the SKU, quantity sold, location, and sale date. Market basket, loyalty card, and retail price information are not typically shared with suppliers.

You may be wondering, “Why is POS data not shared more broadly given the value it can provide?” There are numerous reasons why. IT challenges are often cited as a reason. It is costly for the retailers to

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continuously collect, store, and share the massive amounts of data with manufacturers. Other retailers claim to lack the IT resources or budgets to develop data sharing portals or systems. However, the true reasons for the lack of collaboration are more strategic.

First, retailers and suppliers have trust issues. Grocery retailers both collaborate and compete with their suppliers. Most grocery stores have a line of private label products which are marketed exclusively by the retailers. President’s Choice is a popular brand marketed by Loblaw’s in Canada. Kirkland is a popular brand marketed by Costco in the US. Technika is an exclusive line of electronics marketed by Tesco in the UK. Sharing POS information with suppliers could both potentially help and harm retailers. The manufacturers will use the POS data to ensure higher levels of product availability. However, the manufacturers might also use the rich insights gained from consumer demographics and market basket data to more effectively compete with the retailer’s private label brands. As a result, many retailers may be less inclined to share POS data. Most will not share extended data sets from loyalty cards. Retailers believe having unique access and insight to such information provides their private label brands with a competitive advantage in the marketplace.

Second, many retailers cannot respond to issues that would become apparent if POS data was shared. For example, suppliers may identify scenarios in which the product is in the back room of the stores but out of stock on the shelves. Another example might be trade promotions that were funded by the manufacturer but not executed in selected stores. Such scenarios often have root causes that are not easily fixed within the retailer’s store operations. As a result there may be a reluctance to share information which would expose the problems.3

Most of the blame for issues with data sharing typically is cast on retailers. Yet retailers claim that manufacturer behavior influences their decisions. For example, many retailers believe that manufacturers will not do anything with the data if it is provided. Specifically, small and midsize suppliers may lack the qualified resources needed to perform analysis of POS transactions.4 There are also many instances in which the supplier lacks

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motivation to analyze sales transactions. In merchandise categories with relatively stable buying patterns and few new product introductions, there may be very little incentive for suppliers to perform POS analysis.

Most leaders in the retail industry would agree that sharing of POS data is not as extensive as it could be due to the numerous operational and competitive challenges yet to be overcome. However, the industry is making consistent progress towards increased information sharing. All industry studies indicate that sharing of POS data is increasing substantially year-over-year.

Not True Demand

Although POS data is considered the “gold standard” for demand planning in the consumer products industry, it does have limitations. POS data only measures what consumers actually purchase. It does not measure what they intended to purchase. For instance, an item out of stock cannot be purchased. The demand for out of stock items is not captured in POS data. Consequently, forecasts based upon POS may underestimate true demand for a particular SKU.

Out-of-stocks create forecast inaccuracies in more than one way. In almost half the scenarios, consumers will substitute another item for the one that is out of stock. Substitution is reflected in POS data. However, substitutions are misleading about true end consumer demand. Recall the example from the last chapter in which 8 packs of Duracell AAA batteries were out of stock at a particular store, leading consumers to substitute Energizer products. In reviewing POS data, Energizer might observe a spike in sales for its 8 pack of AAA batteries. As a result, Energizer might believe that there is more demand for its products than truly exists. When the Duracell products are replenished most customers with brand loyalty will switch back.

Another consequence of out of stocks is that consumers delay a purchase. They may select the same SKU from the same retailer but purchase it a week later than originally planned. In such scenarios, the actual

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end consumer demand is not reflected in POS data. The purchasing intervals are misrepresented.

True end consumer demand is represented by actual sales plus lost sales. However, data about most lost sales are unobserved. Customers who do not find the product they intended to purchase will substitute another, buy the SKU elsewhere, or make no purchase at all. Some retailers make a practice of asking consumers if they found everything they needed when they arrive to the checkout counters. However, many customers will not recall items they could not find unless they have a shopping list.

Another challenge with POS data is that it is historical in nature. And historical demand patterns will not always provide an accurate representation of future trends. What happened in yesterday’s stock market is not an indicator of how the markets will perform today. Last year’s weather patterns will not always be similar to this year’s patterns. And the past week’s retail sales will not necessarily be indicative of next week’s buying behaviors.

Beyond POS

If only there was a way to overcome some of these limitations of POS data. If only retailers could somehow electronically capture information about lost demand or find a way to gain insights into what consumers will purchase in the next few days or weeks, such data would be extremely valuable to forecast accuracy. Actually, there is a way. More data than ever is being captured electronically via the Internet about consumer’s future purchasing plans.

The volume of goods and services purchased over the Internet continues to grow significantly each year. Forrester Research found that 6% of overall US retail sales occurred online in 2010.5 Web-based shopping has become especially popular in categories such as apparel, computers, consumer electronics, toys, video games, movies, and music. European online sales are experiencing a similar trend. More than 20% of European consumers are now shopping online with strong future growth rates projected.6

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The growing popularity of online grocery shopping may provide Internet-based demand signals that could improve forecast accuracy for retailers. There have been several high profile online grocery failures over the past 10 years including dot-coms such as WebVan and HomeGrocer. But recently a number of successful players have emerged within major metropolitan areas with the US and UK. For example, Fresh Direct covers the New York metropolitan area, Safeway offers delivery on the West Coast, and Peapod has reach throughout much of the Mid-Atlantic and Mid-West. In the UK, retailers such as Ocado, Tesco, and Waitrose have enjoyed considerable growth in the online sector.

Online grocers are seeking out mechanisms to make shopping online more convenient and easy than visiting a store. One of the features common across grocers’ sites is a shopping list that consumers can populate with items. The shopping list might contain a list of staples that need to be replenished regularly with each delivery or it might be augmented weekly with one-time purchases for special occasions. In either case, the placement of an item on a shopping list is an indication of future purchasing plans. The shopping list, owned by the retailer, can be analyzed on a daily basis to identify demand patterns that may not have been easily predicted from historical sales results.

Online grocery stores can also track lost demand. All customer searches for items are captured digitally. Searches which result in “no results found” or that do not result in a click-through to a product display are of particular interest. These searches may be indicative of demand for merchandise that the retailer does not make available for online ordering. In other cases, a consumer might place several items in an online shopping cart but then exit the website before purchasing. The consumer may have found the shipping costs to be too high or they may have comparison shopped on a competitive site that offered better selection or pricing. In either case, capturing the abandoned shopping cart data is useful for understanding true end-consumer demand. Such behaviors could never be captured in a traditional retail store.

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Unfortunately, the potential for leveraging shopping list data to predict consumer demand is limited today primarily due to the relatively small percentage of consumers buying online. Less than 10% of online adults have purchased groceries online today.7 The statistic is not surprising considering that the option for home delivery does not exist in many areas of the US.

Early adopters of online grocery stores have been concentrated in a few demographics. For example, online ordering is popular in households without children and with affluent individuals. Both groups prefer to focus time on other activities and avoid the hassle of going to the grocery store. There are also concentrations of purchases in particular merchandise categories. For example, consumers have a general reluctance to purchase perishables such as dairy, produce, meat, and prepared foods via the web. Many shoppers prefer to visually inspect fruits, vegetables, and meat before purchasing. Dry goods such as snacks, breads, cereal, pharmacy items, and canned goods have proven more popular.

As the percentage of online grocery shoppers approaches 20 to 25% of the consumer population, the demand insights generated from end-user activity will become more statistically significant. The retailer who can identify a model for growing online grocery purchasing to a higher percentage of sales will not only capture more revenue but will also enjoy a significant competitive advantage for supply chain planning.

Online grocery stores are not the only example of how consumer demand data can be captured on the Internet. Let’s explore a few other examples from different merchandise categories in the retail sector.

Gift Registries – It is Better to Track than to Receive

In the US, it has become very common for engaged couples and expecting parents to create an online gift registry that contains a list of items they need to grow into the next stage of their lives. Expecting parents might request baby clothes, toys, books, and food utensils. Engaged couples might request silverware, fine china, linens, and other housewares. The online gift

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registry makes it convenient for friends and family around the world to purchase a thoughtful present that the receiver actually wants. It is a win-win-win for the giver, receiver, and the retailer. But there is much more benefit to be gained by online retailers and suppliers from this process.

We can exploit these gift registries to perform much better demand planning than has ever been possible. Baby and wedding registries contain a massive amount of data about future purchasing intentions. Gift registries not only show what is going to be purchased, they also provide a fairly good indication of the timeframe under which the transaction will occur. Most baby registry items will be purchased shortly before the due date or the shower. Even those items that are not purchased by friends and family will likely be picked up by the newborn’s parents. Similarly, with wedding registries, most purchasing transactions will occur shortly before or after the due date and the newlyweds are likely to buy the remaining items on the registry within six months of the wedding. No bride wants to live with seven table settings.

Gift registry data, when aggregated, could be used as an input to private label and branded supplier manufacturing plans. Furthermore, the data could be analyzed by geographic region to determine store and distribution center stocking levels for department and specialty store retailers. Retail chains that offer gift registry options are in an enviable position to leverage these demand signals. Leading registry destinations such as Target, Macy’s, Williams Sonoma, Crate & Barrel, Home Depot, and Babies “R” Us have already leveraged this data to optimize their supply chains.

Wish lists provide a similar function to gift registries, but are limited to a smaller list of online retailers such as Amazon.com and Barnes & Noble. Users typically create a wish list to provide friends and family with gift ideas for upcoming birthdays or holidays like Christmas. Much like gift registries, wish lists contain a wealth of data about future purchasing behaviors. Not only do the gift registries identify what is going to be purchased, they also provide insights into the timeframe of the transaction. For example, most wish lists require an end-user to enter their birthday.

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Chances are high that items on a wish list are going to be purchased in the weeks preceding either 1) the end user’s birthday or 2) the holiday season.

Consumer electronics, books, movies, and music are among the popular items stored on wish lists. Therefore publishing, media, entertainment, and consumer electronics companies are the most likely to benefit from the demand signals wish lists provide. But as sites such as Target and Amazon.com broaden the categories of merchandise available on their site, a more diverse set of products will appear.

Amazon.com has the Best Forward-Looking Demand Data

Online retailers that enable functions such as shopping lists, gift registries, and wish lists have a natural advantage in their demand forecasting processes. These e-commerce retailers have better insights into future consumer spending patterns than any brick-and-mortar chain does. Amazon.com is perhaps best-positioned to leverage its wish lists and gift registries for supply chain advantage. Not only does Amazon have the largest online store, but they have also proven to be far more innovative than any other retailer in their introduction of new functionality and adoption of new techniques.

Online retailers such as Amazon.com have been utilizing this forward-looking demand pattern data for years to optimize their stocking models. But this is just the beginning. The opportunity becomes more interesting when cross-analyzing data from online storefronts with brick and mortar stores. Book retailers such as Barnes & Noble could utilize demand data obtained from e-commerce sites to augment their forecasting models for physical product. Consumer electronics retailers such as Best Buy could adopt a similar process. To take it a step further, online retailers could leverage their unique insights into future consumer purchasing patterns with the product manufacturers. The data could be aggregated and sold to suppliers, much as POS transactions are today. Alternatively, collaborative relationships for exclusive merchandise or promotions could be established between buyer and supplier to create competitive advantage.

Of course, the big question is will the retailers share the data?

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References

1. Concepts taken from AMR Research reports on Demand Driven Supply Networks

2. Kara Romanow, Janet Suleski, Debra Hoffman, J. Paul Kirby and Laura Preslan, “POS Data - The Beginning of DDSN for Consumer Products Manufacturers,” AMR Research, February 5, 2004

3. Grocery Manufacturers Association, “Retailer Direct Data Report,” 2009 4. Ibid 5. Sucharita Mulpuru and Peter Hult, “US Online Retail Forecast, 2009 to

2014,” Forrester Research, March 5, 2010 6. Pattie Freeman Evans and Lauriane Camus, “Western European Online

Retail Forecast, 2009 to 2014,” Forrester Research, March 5, 2010 7. Sucharita Mulpuru, “Who Buys Groceries Online and Why,” Forrester

Research, February 12, 2010