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Thomas W. Gruen, Ph.D. Retail Out-of-Stocks: Finding a Solution.

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1 Thomas W. Gruen, Ph.D. Retail Out-of-Stocks: Finding a Solution

2 Retail Out-Of-Stocks: Finding a Solution
Thomas W. Gruen, Ph.D. Professor of Marketing University of Colorado at Colorado Springs, USA September 27, 2007

3 If you think Out-of-Stock items are not a problem…
Toilet tissue 3 © 2007 Thomas W. Gruen

4 Then why… Do we waste 21% of a shopper’s time looking for an OOS item?
Does a typical store spend $800 per week on employees solving shopper OOS issues? Do we completely satisfy customers on less than 10% of their shopping trips? Reduce the effectiveness of 1/7 of our promotions? Is one of every 13 items on a shopper’s list not going to be available? Do we encourage loyal customers to try other brands and shop at other stores? 4 © 2007 Thomas W. Gruen

5 Agenda Review what we know about retail out-of-stocks from our research Present 7 general areas that need to be addressed: Most areas focus on data Show how measurement can direct us to solutions by revealing the root causes Demonstrate our approach to reducing out-of-stocks. 5

6 Why should we pay attention to OOS?
Lost Sales & Margin For Manufacturer Lost Sales And Margin For Retailer Domino Effect On Categories Dissatisfied Customers Here are some findings from our 2002 study. These generated the interest (and another research grant) for the current study! Let’s find out why. 6

7 Two Studies 2002 GMA/FMI/CIES Study Current 2007 Study
Worldwide Extent of OOS Shopper Reaction When Faced with OOS Root Causes Current 2007 Study Aimed at Solutions Preliminary Report Completed in June Published in September 7

8 First Study’s Objectives
Examine extent of Out of Stocks Examine cause of Out of Stocks Examine consumer response to Out of Stocks …on a worldwide basis, with the objectives: 1. to present an updated and accurate “map” of facts surrounding retail out-of-stocks in the Fast Moving Consumer Goods (FMCG) industry, to examine out-of-stocks worldwide, examining rationale for similarities and differences,

9 Research Project Inputs: 52 Studies
16 previously published academic and industry studies 36 studies proprietary to this report Covering: Number of retail outlets examined: 661 Number of FMCG categories included: 32 Number of consumers surveyed world-wide: 71,000 Number of countries represented: 29 This was a huge study! So what did we find out…

10 Extent—Definitions Differ
Store-based definition: Percentage of SKUs not on the retail store shelf at a particular point in time. Measured by audits, normally in selected categories, then aggregated. Shopper-based definition: Number of times a shopper looks for an item in the store and does not find it on the expected shelf. Calculated as a percentage. Measured by estimation from store POS data. Helpful for examining fast-moving items. 10 © 2007 Thomas W. Gruen

11 Clarifying what an OOS is
OOS Event Physical lack of salable product on the shelf OOS Attributes Aspects of the OOS event(s) that can be measured and that can be calculated as an OOS “rate.” Frequency, Duration, Simultaneous events, Availability, Lost unit sales, Lost monetary sales, and Customers impacted 11 © 2007 Thomas W. Gruen

12 OOS Rates are Calculated from Attributes
Summary of OOS Rates: 1. Item OOS Event Rate 2. Category OOS Event Rate 3. OOS Duration Rate 4. Shelf Availability Rate 5. OOS Lost Unit Sales Rate 6. OOS Sales Loss Rate 7. OOS Customer Impact Rate 12 © 2007 Thomas W. Gruen

13 Worldwide Extent > 8%
Background: What We Know About OOS Worldwide Extent > 8% *Note: Europe includes all Europe including Eastern Europe Credit: Gruen, Corsten, and Bharadwaj 2002 13

U.S retail out-of-stock rate: 7.9% Promotional out-of-stock rate: 17.1%

Reliable data from three or more studies

Reflects expected patterns due to shopping and deliveries

17 More than half are OOS more than 24 hours!
3/25/2017 EXTENT: DURATION More than half are OOS more than 24 hours! 55% OF OOS LAST LONGER THAN 24 HOURS – USUAL REPLENISHMENT TIME

18 Background: Extent Interpretation and Implications
In spite of heavy investments to improve supply chains, worldwide OOS levels still average 8%, or from the shopper’s perspective, for every 13 items a shopper plans to purchase, one will be OOS. For promoted items, OOS levels average 16%, which translates to one OOS item for every 7 promoted items a shopper plans to purchase. Thus, in an industry heavily dependent upon promotions, the impact of one-seventh of promotional dollars is reduced. Sales velocity always affects the rate of OOS. 18

Background: Extent Q: HOW MUCH HAVE OOS RATES CHANGED? Coca-Cola Research Council 1996 study = 8.2% (USA only) Our GMA/FMI/CIES 2002 study = 8.3% (Worldwide; 7.9% USA) A: Not Much. But… there are so many new kinds of technology in scanning systems, databases, computer assisted ordering (CAO) systems, etc… 19

Background: Extent WHY HAVEN’T OOS RATES CHANGED? Technology improvements have been offset by process complexity SKU proliferation Promotional proliferation Store level assortment Store level planogramming Retailers face increased pressure to keep labor costs down Interesting side note: Isn’t this the way life with technology works in general? We get something new, and then we think of new ways to make life more complicated. 20

21 Substitute – same brand Substitute – different brand
SHOPPER RESPONSE THERE ARE 5 SHOPPER REACTIONS WHEN FACED WITH AN OOS: Do not purchase Purchase elsewhere Substitute – same brand Substitute – different brand Delay purchase It’s also important to mention here that in addition to knowing how consumers respond to OOS, we also know how consumers respond to assortment reductions. 21

22 How Do Shoppers Respond to Out of Stocks?
SHOPPER RESPONSE How Do Shoppers Respond to Out of Stocks? When a shopper confronts an out-of-stock: Retailers are likely to lose on Avg. 40% of the intended purchases Manufacturers lose 35% of intended purchases 22 © 2007 Thomas W. Gruen

Note differences in brand substitution across regions! 23

24 SHOPPER RESPONSE Varies Greatly by Category
Range of store switch varies from 13% to 40% Fem Hygiene buys at another store 3 times more than Towels 24

25 SHOPPER RESPONSE Grocery Store Featured Category
Source: ECR-UK 2005

26 SHOPPER RESPONSE Drug Store Featured Category
Source: ECR-UK 2005

27 Multiple Out-of-Stocks In a Single Shopping Trip Can Cause the Shopper to Leave the Store
Source: GS1 Columbia, “Diagnosis Report,” 2007 27

With an average OOS level (8%) and a shopper purchasing 40 items – statistically what % of trips will he/she be completely satisfied (i.e. no OOS)? A. 4% B. 24% C. 44% D. 64% E. Can’t tell from the information given

29 From Appendix E, p. 65

If a retailer can cut the OOS rate in half, the potential for 100% satisfaction skyrockets! Thanks to Synchra Systems, Inc. for this chart!

Sales Losses are similar worldwide, but vary greatly among categories 31

32 Calculating a company’s lost sales from OOS:
OOS Rate _______% x Category Avg Lost Sales _______% Total Category/ Organization Sales $_____ = Sales Lost to OOS $_____ Example: Avg OOS rate 8% X MFR Avg Loss 30% Category Sales $1B = Lost sales $24,000,000 Typical Retailer Sales Loss/$1B total sales is about $32 million 32

The implications of our findings suggest that the cost of out-of-stocks to retailers is greater than what has been reported in previous studies. Our findings show that a typical retailer loses about 4 percent of sales due to having items out-of-stock. A loss of sales of 4 percent translates into a earnings per share loss of about $0.012 (1.2 cents) for the average firm in the grocery retailing sector where the average earnings per share is about $0.25 (25 cents) per year.

34 Motivation – Additional Costs
Manufacturers Retailers OOS Lowers Impact of Promotions and Trade Promotion Funds OOS Distorts True Store Demand, thus Forecasting, Category Management and Related Efforts are Less Accurate and Effective OOS Increases Overall Costs of Relationship with Retailer (Increased Post-Audit Activity, Irregular Ordering) OOS Distorts True Shopper Demand thus Decreases Forecasting and Ordering Accuracy Operational Costs are Increased through Personnel Looking for OOS Items, Providing “Rain Checks” to Shoppers, Unplanned Restocking, etc. (could be $1.0 Million for 100 stores) Operational Direct Loss of Brand Loyalty and Brand Equity OOS Encourages Trial of Competitor Brands Lowered Overall Effectiveness of Sales Team Resources Direct Loss of Store Loyalty Decreased Customer Satisfaction OOS Encourages Trial of Competitors’ Stores Permanent Shopper Loss Rate is Undocumented, but Annual Cost is US$1 Million per Every 200 Shoppers Two notes for this slide: First, the impact is much larger than lost sales alone. Second, in the retailer operation quadrant, note that in the report we present a method to estimate the personnel costs. Strategic

35 Costs of Addressing OOS in Store
For Retailers: Labor Spent Satisfying Shopper OOS Questions $800/week/store for an Average Food Store About $4.1million annually – 100 stores For Shoppers: Shoppers spend >20% of the Average Shopping Trip Waiting for an Answer

36 Let’s Examine the Causes of Out-of-Stocks
Where does the breakdown occur? Supply chain? Retailer ordering? Retailer merchandising? Uneven consumer demand? 36

37 Lowering OOS Begins With Understanding the Causes of OOS
Retail store ordering and forecasting causes (about ½ of OOS) Retail store shelving and replenishment practices where the product is at the store but not on the shelf (about ¼ of OOS) Combined upstream causes (about ¼ of OOS) This leads us to focus on the two major retail components: store and shelf Credit: Gruen, Corsten, and Bharadwaj 2002 70-75 percent of out-of-stocks are a direct result of retail store practices

38 Summary of Findings of OOS Causes
3/25/2017 UPSTREAM CAUSES OF OOS Summary of Findings of OOS Causes (Worldwide) Same as previous slide but details the the upstream causes. Other Cause 4% Store Ordering Retail HQ or 13% Manufacturer 14% Distribution Center FIRST STUDY TO IDENTIFY SHELVING AS ONE OF THE KEY CAUSES 10% Store Forecasting 34% Store Shelving 25%

Store Forecasting – 35% Ineffective algorithms Long forecasting cycles Store Ordering – 13% Late order / no order Inappropriate replenishment intervals Store Stocking – 25% Inadequate or poorly allocated shelf space Shelf stocking frequency Congested backroom Warehousing – 10% Poor ordering policies Data accuracy issues Management Errors – 14% Last-minute price / promotion decisions Inaccurate or obsolete product information Manufacturer Availability – 4% Packaging, raw material or ingredient allocation Capacity issues Can skip this slide if time is short. 39

40 So… We know the extent, consumer responses, basic causes.
The implication of doing nothing is huge. Some retailers are actively addressing OOS and making progress. Given the number of potential remedies, it appears that fixing one or more OOS root causes should be a fairly easy task for retailers. However, knowing where to begin and which remedy will produce the most efficient and effective results relative to the invested resources remains a key barrier to implementation. What is next? 40

41 7 Key Areas that Impact OOS
We have to understand product flow. We have to understand measurement of OOS Due to OOS (and lots of other reasons), sales and demand are different Most of the time, inventory data is inaccurate Shelf space is often inadequate for the fast movers It helps when stores comply with plans Keeping shelves and back room straight really matters 41

42 1. We have to understand product flow (i.e., to the shopper)

43 There Aren’t That Many Fast Moving Items
New analyses of POS data provide us with a clearer picture of product movement across time. The conclusion: a relatively small number of items constitute the majority of the store’s total sales.

44 Product Movement—Lower Volume Stores
A lower volume grocery store with 50,000 items will sell: 5,000 items in a typical day In a typical week, the yellow line crosses 80% at 15%, or 7,500 items Chart provided by Standard Analytics 44

45 We have to understand SKU’s sales velocity and variability
Measurement & Focus We have to understand SKU’s sales velocity and variability …and focus on the ones that matter 45

46 2. We Have to Understand Measurement of OOS, How Measurement Points to Root Causes, and How Root Cause Understanding Points to Solutions 46

47 OOS Measurement Method 1
Manual Audit Approach Labor Intensive, costly to use ongoing Believed by Employees Data Intensive Error Prone Note that both methods can point to root causes of OOS, and these root causes can point to solutions. Note that several companies have developed algorithms to estimate OOS. We mention two of them in the presentation. 47

48 Manual Audit Example: Root Cause Percentages
Note that this was a manual audit of 600 random items across 125 stores. 1. Start with replenishment (15%)—determined by how many OOS items have inventory in the back room? 2. After the 15% replenishment has been identified, then how many of the remaining have PI > 0? In this case, PI inaccuracy is the leading cause (42%) of OOS. 3. And so on, identifying 12% due to ad, etc.

49 OOS Measurement Method 2
Perpetual Inventory System When on-hands = 0 (or less), then item is OOS Many retailers already have PI system On hand data is bad Is itself a cause of many OOS 49

50 OOS Measurement Method 3
Point of Sale Data Approach >85% Accurate (even false positives have benefit) Gives value to lost sales Calculates duration Extensive Reporting Costly to set up, cheap to run ongoing Two Partner Vendors Data Ventures Standard Analytics

51 POS Data Estimation Example
3/25/2017 POS Data Estimation Example Example 1: (3 lost sales) Example 2: (4 lost sales) Works best with fast moving items. The algorithm determines each item’s velocity (using 52 week history) Item expected velocity varies as the store velocity varies and price of the item varies When an item’s purchase cycle (expected velocity) is interrupted, that item is deemed “Out-of-Stock” 51

52 Example: Top 100 OOS Items by Store
This report helps: Identify items with consistent Out-of-Stocks. Identify day and time of OOS Events. Understand the extent that promotional activity drives Out-of-Stocks. Identify items that need modification of delivery schedules. Also: Use POS data to examine frequency attributes to show patterns 52

53 Q: What Else Can We Do with POS Generated OOS Data?
A: Find Patterns of OOS Promo Velocity Underestimated OOS Correlate with Promotion Schedule Weekend Sales Underestimated Item Consistently OOS on Weekends Inadequate Shelf Space Short Duration OOS (< 1 day), Additional Supply is Clearly On-hand Distribution Center OOS Relatively Long Duration OOS with High Correlation in Geographically Close Stores

54 Sample Assessment Patterns
Pattern 1: Promo Velocity Underestimated Store A, Fresh Express American Salad 12 oz Problem Corrected in January Simple map here—note when the OOS pattern has been identified and fixed. Copyright Standard Analytics, LLC All rights reserved.

55 What does this pattern indicate?
Tell the audience that this is the egg category before showing the information at the bottom. This store needs to add shelf space or check shelf stock and restock the large eggs shelf more frequently 55 Copyright Standard Analytics, LLC All rights reserved.

56 What does this pattern indicate?
This store probably has an inadequate replenishment schedule for fast moving PLAIN PITA BREAD. The item is usually OOS by Thursday, and back on Friday evening. It is usually OOS again by Saturday or Sunday, and replenished by Tuesday. It looks like there are 2 deliveries a week, but 4 or more are needed. 56 Copyright Standard Analytics, LLC All rights reserved.

57 What Does This Pattern Indicate?
Problem: Item sells nearly every day - very few zero-sales days Almost daily stock-outs - demand is usually not met. Full sales units /day, average sales 21 units / day. Occasional multi-day stock-outs. Solution: Increase daily supply by approx. 60%; Check shelf 3x per day. 57 Copyright Standard Analytics, LLC All rights reserved.

58 3. OOS Disguises Actual Demand
47% of OOS due to poor forecasts Lost sales is unobserved because most customers, who do not find the product that they intended to buy, make a decision to not buy, buy elsewhere, or substitute, without registering the non-purchase of the intended item with the store. Forecasting models do not include estimations of lost sales and simply forecast future demand on the basis of historical sales. Researchers have attempted to estimate demand with unobserved sales. All models conclude that that lost sales can be substantial and that it is strongly influenced by average demand and demand uncertainty. 58

59 How OOS Disguises Actual Demand

60 Silver Cleaning Polish

61 Silver Cleaning Polish



64 4. OOS Linked to Inventory Accuracy
Issue 1: Product Data Accuracy Data inaccuracy in retailers’ inventory databases comes from a variety of causes including: Merging previously independent databases; this happens due to corporate mergers, and also through the joining of previously separate systems. Accuracy with product replacements, new items that don’t get in the database correctly, and purging information on discontinued items. Manufacturers introduce temporary product changes, such as bonus packs, where a new UPC/GTIN code that follows the bonus pack, but then reverts back to the old UPC/GTIN.

65 Product Data Accuracy Small differences can have a large effect.
Third-party vendors, such as 1SYNCH, have evolved to facilitate data improvements.  The effects of data alignment on lowering OOS can be substantial as the following two pilot studies reported by Capgemini/GCI 2005 show: In Latin America (Mexico, Guatemala, and Columbia), Procter & Gamble and several retail customers reduced purchase order errors from 3.6% to 0.8%, and this resulted in a decrease in OOS items from 8% to 3%.

66 4. OOS Linked to Inventory Accuracy
Issue 2: Perpetual Inventory (PI) Accuracy Study (USA drug store chain): Out Of Stock Rates were Benchmarked by In- Store Shelf Audits: 4.1 % OOS Where OOS Matched P. I. (i.e., P.I. = 0) 8.9% OOS Where OOS Do Not Match P.I. (i.e., P.I. >0)

67 PI Accuracy Observations
45.4% of the time there was no variance 18.8% of the time there was +/- 1 unit 10% of the time there +/- 2 unit Ask, why is this so low? Improper scanning Shrink Product lost in back room Store stocker ability to make on-hand adjustments

68 PI Accuracy For Items In One Location vs. Multiple Locations
Our first step was to revisit our database. Again, we have now have over 20,000 store level items in which we can query on understand casual relationships. Based upon our hypothesis, we wanted to understand what the drivers to PI accuracy was. We started with items in only one location, meaning they were not on any end cap, profit planner, or POG in multiple locations. What we found was both compelling and concerning; If you look at items in only one location, the accuracy of the perpetual inventory jumps from an average of only 45% accurate +/- one to 52%. Similar improvements occur if you across the accuracy for +/- 1 where the accuracy increases 9% to 73% and +/2 where the accuracy increases another 9% to 83%.

69 Steps to Improving Inventory Accuracy
Focus Store Inventory Counts on: Physical OOS Negative On-Hands Zero On-Hands Other Directed Items (e.g. high shrink, fast movers) Eliminated all other Counting Reduced total cycle counts and improved accuracy Results: Increased PI Accuracy 19% Reduced Labor Costs in Inventory Accuracy by 50% Note that PI can be increased with lower effort due to focus. Note how critical this is for slow movers, because PI off by 1 or 2 delays orders of slow movers. Less critical for fast movers. 69

70 5. Peak Demand Planograms
91% of the SKUs are Allocated Shelf Space Based on case packout Many retailers use a “Red Dot” program (a work-around) 86% of the inventory on shelf is in excess of 7 days supply Cutting slowest movers to provide shelf space for the fastest may prove cost effective. 70


72 Peak Demand Planograms
Note that the study is underway and the results will be in the second edition of the report. 72


74 Fast movers are in cases
On the bottom shelf

75 6. Planogram Compliance To what degree does compliance with POG link to levels of OOS? POG Compliance involves: Distribution Space Arrangement Item Shelf Brand Arrangement SKU Level Arrangement Whole thing starts with understanding POG compliance. We developed the best practice for measuring POG compliance, which is summarized on the following slide. 75


77 Weekly OOS rate measurement and analysis provided by Standard Analytics, LLC.
© 2007 Thomas W. Gruen

78 Weekly OOS rate measurement and analysis provided by Standard Analytics, LLC.
© 2007 Thomas W. Gruen

79 Diapers Analysis 79

80 POG Compliance Study Summary
All Categories Showed Statistically Significant Relationships between Planogram Compliance and On-Shelf Availability (effect is 1% : 0.1%) With High Compliance the Benefit is Rather Small 80

81 7. Item Management There is a key need to keep items straight on the shelf Don’t cover holes Don’t hide product Keep shelf tags accurate There is a key need to effectively get merchandise from the backroom to the shelf Test of shelf accuracy on OOS levels shows strong results 81

82 Item Management Sales losses reduced by ~40% in Test stores with disciplined stocking practices versus Control 82


84 Recap and Conclusions Get the product to the store, then get the product on to the shelf. Get it right on the shelves Identify and address the fast movers For store formats with faster moving items, use POS estimation and look for patterns For store formats with slower moving items, work on PI accuracy In all cases get item data correct through data synchronization.

85 Solve high risk products with Store OOS solutions
Recommended Approach Measure & Assess High OOS risk products (fast movers) High OOS stores Shelf versus Store OOS Solve high risk products with Store OOS solutions Solve high OOS stores with Shelf OOS Solutions 85

86 Finding the Genie to Grant Our Wish: How to Solve Out of Stocks
Measurement and Assessment Root Cause Identification Apply Solutions Continual Improvement See the whole picture and address what you can 86

87 Professor of Marketing University of Colorado, Colorado Springs, USA
Contact for additional information: Thomas W. Gruen, Ph.D. Professor of Marketing University of Colorado, Colorado Springs, USA For a PDF copy of the 2002 study, you can download directly from: Also check the website for announcements on 2007 Report 87

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