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

Thomas W. Gruen, Ph.D.

Retail Out-of-Stocks: Finding a Solution

© 2007 Thomas W. Gruen

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

If you think Out-of-Stock items are not a problem…

Toilet tissue

3© 2007 Thomas W. Gruen

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?



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


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


Two Studies

1. 2002 GMA/FMI/CIES Study– Worldwide Extent of OOS

– Shopper Reaction When Faced with OOS

– Root Causes

2. Current 2007 Study – Aimed at Solutions

– Preliminary Report Completed in June

– Published in September

7

© 2007 Thomas W. Gruen 8

First Study’s Objectives

…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,

2. to examine out-of-stocks worldwide, examining rationale for similarities and differences,

Examine extent of Out of Stocks Examine cause of Out of Stocks Examine consumer response to Out of Stocks


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…

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.


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


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



Worldwide Extent > 8%

Overall OOS Extent (Averages)

7.9

8.6

8.2

8.3

0.0 2.0 4.0 6.0 8.0 10.0

USA

Europe

Other Regions

Worldw ide

Percent OOS

*Note: Europe includes all Europe including Eastern EuropeCredit: Gruen, Corsten, and Bharadwaj 2002

Background: What We Know About OOS

13


PROMOTIONAL OUT-OF STOCKS

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

Promotional out-of-stock rate: 17.1%


EXTENT VARIES BY CATEGORY…

OOS Averages by Category

9.8

7.7

7.0

6.8

6.6

5.3

8.3

0.0 2.0 4.0 6.0 8.0 10.0 12.0

Hair Care

Laundry

Diapers

Fem Hygiene

Toilet Tissue

Salty Snacks

World Avg (18 categories)

Percent

Reliable data from three or more studies


EXTENT VARIES BY DAY OF WEEK

OOS by Day of Week(Average of 13 studies)

10.9

7.3

8.7

9.1

9.8

10.0

10.9

0.0 2.0 4.0 6.0 8.0 10.0 12.0

Sun

Sat

Fri

Thur

Wed

Tues

Mon

Percent

Reflects expected patterns due to

shopping and

deliveries


EXTENT: DURATION

Duration of OOS

20%

25%

36%

19%

8 hrs or less

8 hrs to < 1 day

1 day to < 3 days

3 days or more

More than

half are OOS more

than 24 hours!

© 2007 Thomas W. Gruen18

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


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…

Background: Extent

19


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

Background: Extent

20


SHOPPER RESPONSE

1. Do not purchase

2. Purchase elsewhere

3. Substitute – same brand

4. Substitute – different

brand

5. Delay purchase

THERE ARE 5 SHOPPER REACTIONS WHEN FACED WITH AN OOS:

21


Buy Item at Another Store, 31%

Delay Purchase, 15%Substitute Same

Brand, 19%

Substitute Different Brand, 26%

Do Not Purchase Item, 9%

How Do Shoppers Respond to Out of Stocks?

When a shopper confronts an out-of-stock:

SHOPPER RESPONSE

Retailers are likely to lose on Avg. 40% of the intended purchases

Retailers are likely to lose on Avg. 40% of the intended purchases

Manufacturers lose 35% of intended purchases

Manufacturers lose 35% of intended purchases22



SHOPPER RESPONSE

REGIONS

Average Consumer Responses by Region (comparisons across 8 common categories)

31

34

27

31

15

13

17

16

19

20

16

21

26

25

32

22

9

8

9

11

0% 20% 40% 60% 80% 100%

WorldAverage

OtherRegions

Europe

USA

Bought at another store

Delay purchase

Substitute-same brand

Substitute-different brand

Do not purchase item

Note differences

in brand substitution

across regions!

23


SHOPPER RESPONSE

Varies Greatly by Category

Average Worldwide Consumer Responses by Category

13

15

18

26

32

37

39

40

19

10

19

20

16

16

17

13

19

21

24

16

15

16

14

17

37

38

28

31

30

24

20

25

12

16

11

7

7

7

10

5

0% 20% 40% 60% 80% 100%

Paper Tow els

Salted Snacks

Toilet Tissue

Laundry

Shampoo/Hair Care

Toothpaste

Diapers

Feminine Hygiene

Bought at anotherstoreDelay purchase

Substitute-samebrandSubstitute-differentbrandDo not purchaseitem

Range of store switch varies from 13% to 40%

Fem Hygiene buys at another store 3 times more than Towels

24


SHOPPER RESPONSE

Grocery Store Featured Category

Source: ECR-UK 2005


SHOPPER RESPONSEDrug Store Featured Category

Source: ECR-UK 2005


Multiple Out-of-Stocks In a Single Shopping Trip Can Cause the Shopper to Leave the Store

Source: GS1 Columbia, “Diagnosis Report,” 2007

27


QUIZ QUESTION: EFFECT OF MULTIPLE OOS IN A SINGLE TRIP• 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


% Availability 10 20 30 40 5099% 90% 82% 74% 67% 61%98% 82% 67% 55% 45% 36%97% 74% 54% 40% 30% 22%96% 66% 44% 29% 20% 13%95% 60% 36% 21% 13% 8%94% 54% 29% 16% 8% 5%93% 48% 23% 11% 5% 3%92% 43% 19% 8% 4% 2%91% 39% 15% 6% 2% 1%90% 35% 12% 4% 1% 1%89% 31% 10% 3% 1% 0%88% 28% 8% 2% 1% 0%87% 25% 6% 2% 0% 0%86% 22% 5% 1% 0% 0%85% 20% 4% 1% 0% 0%

Probability of Shopper Being 100% Satisfied# Items to Purchase

From Appendix E, p. 65


LIKELIHOOD OF 100% CUSTOMER SATISFACTIONIf a retailer can cut the OOS rate in half, the potential for 100% satisfaction skyrockets!

15%

29%

54%

82%

2%8%

30%

67%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

91% 94% 97% 99%

Store in-stock rate

Pro

ba

bili

ty o

f 1

00

% s

ati

sfa

cti

on

20 items purchased

40 items purchased

Thanks to Synchra Systems, Inc. for this chart!


IMPLICATIONS: RETAILER SALES LOSSESDUE TO OOS ARE ABOUT 4 PERCENT

Sales Losses Due to OOS

4.5

3.8

3.5

3.2

2.4

2.1

4.0

3.8

3.7

3.9

0.0 1.0 2.0 3.0 4.0 5.0

By Category

Hair Care

Diapers

Feminine Hygiene

Laundry

Toilet Tissue

Salted Snacks

By Region

Other Regions

USA

Europe

World Average

Estimated Percentage Loss

Sales Losses are

similar worldwide, but vary greatly among

categories

31


Calculating a company’slost sales from OOS:

OOS Rate _______%

x

Category Avg

Lost Sales _______%

x

Total Category/

Organization Sales $_____

=

Sales Lost to OOS $_____

Example:

Avg OOS rate 8%

X

MFR Avg Loss 30%

X

Category Sales $1B

=

Lost sales $24,000,000

Typical Retailer Sales Loss/$1B total sales is about $32 million

32


FINDINGS: IMPLICATIONS

• 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.


Motivation – Additional Costs

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)

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 LoyaltyDecreased Customer SatisfactionOOS Encourages Trial of Competitors’

StoresPermanent Shopper Loss Rate is

Undocumented, but Annual Cost is US$1 Million per Every 200 Shoppers

Manufacturers Retailers

Op

eration

alS

trategic


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

35


Let’s Examine the Causes of Out-of-Stocks

• Where does the breakdown occur?– Supply chain?– Retailer ordering?– Retailer

merchandising?– Uneven consumer

demand?

36


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)

OOS CausesWorldwide Averages

Total upstream causes

28%

In the store, not on the shelf

25%

Store Ordering and

Forecasting47%

70-75 percent of out-of-stocks are a direct result of retail store practices Credit: Gruen, Corsten, and Bharadwaj 2002


UPSTREAM CAUSES OF OOS

Summary of Findings of OOS Causes(Worldwide)

Store Ordering13%

Store Forecasting34%

Store Shelving25%

Distribution Center10%

Retail HQ or Manufacturer

14%

Other Cause4%

Same as previous slide but

details the the

upstream causes.


LET’S SUMMARIZE THE CAUSES:

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

39


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


7 Key Areas that Impact OOS

1. We have to understand product flow.

2. We have to understand measurement of OOS

3. Due to OOS (and lots of other reasons), sales and demand are different

4. Most of the time, inventory data is inaccurate

5. Shelf space is often inadequate for the fast movers

6. It helps when stores comply with plans

7. Keeping shelves and back room straight really matters

41


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

42


There Aren’t That Many Fast Moving ItemsNew 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.

Large Store Item Velocity Characteristics

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000

Number of Items

Cu

mu

lati

ve P

erce

nt

Sal

es

Avg Day

Avg Peak Day

Avg Week

Avg 4 Weeks

Avg Year


Product Movement—Lower Volume Stores

Small Store Item Velocity Characteristics

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000

Number of Items

Cu

mu

lati

ve P

erce

nt

Sal

es

Avg Day

Avg Peak Day

Avg Week

Avg 4 Weeks

Avg Year

Chart provided by Standard Analytics

44


Measurement & Focus

We have to understand SKU’s sales velocity and variability

…and focus on the ones that matter

45


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

46


OOS Measurement Method 1

• Manual Audit Approach– Labor Intensive, costly to use ongoing

– Believed by Employees

– Data Intensive

– Error Prone

47


12%

15%

9%

2%

3%

2%

1%13%

42%

Ad Replen P.I.

DC Single Chk TPR

Scnd Loc Stockrm Disc

Manual Audit Example: Root Cause Percentages



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



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

50


POS Data Estimation Example

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”

Sat Sun M on Tues Wed Thurs Fri Sat Sun

Sat Sun M on Tues Wed Thurs Fri Sat Sun

ActualPurchase

ExpectedPurchase

ActualPurchase

ExpectedPurchase

Out ofStock

Out of Stock

Mis

se

dP

urc

ha

se

Mis

se

dP

urc

ha

se

Example 1:

(3 lost sales)

Example 2:

(4 lost sales)

51


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

Retail Operations PackageTop 100 Out of Stock Items by Store1 Week Ending 9/24/2002

Store Item Code Category Description

Item was OOS on Last

Week's Report

OOS Rate

# of OOS

Events

Avg Duration of OOS (days)

0134 00000000000 Milk P Label Gallon Milk Repeat OOS 38.1% 2 1.740134 00000000000 Bath Tissue Reliable 4 Roll Tissue 33.0% 2 1.750134 00000000000 Condiments Picnic Catsup 16.3% 1 1.950134 00000000000 Snacks Party Hearty Potato Chips Repeat OOS 7.7% 1 0.860134 00000000000 Carbonated Beverage Mr Fizzy Diet Cola 2 liter Repeat OOS 81.3% 4 0.450134 00000000000 Ice Cream Sinfullicious Ice Cream Vanilla Gal 83.0% 1 6.280134 00000000000 Carbonated Beverage Mr Fizzy Cola 2 liter Repeat OOS 61.2% 5 0.320134 00000000000 Fresh Bread & Rolls P Label Hot Dog Buns Repeat OOS 20.0% 1 1.35

2002 Data Ventures

Expected Sales Velocity

Used in Determining OOS Events

Lost Item Units

Lost Dollar Sales

% of Total OOS

Dollars Lost

% OOS $ on Price Red

Date When Item First Went OOS

Time of Day When Item First Went OOS

Date When Item Lost the Most

Dollar Sales

Time of Day When Item

Lost the Most Dollar

Sales:26 183 $494 30.3% 0.0% 9/19/02 8:37 PM 9/20/02 4:09 PM

1:09 70 $485 29.7% 100.0% 9/19/02 6:09 PM 9/20/02 8:59 PM:59 43 $37 2.3% 100.0% 9/20/02 7:29 PM 9/20/02 7:29 PM

1:27 14 $29 1.7% 100.0% 9/18/02 2:21 PM 9/18/02 2:21 PM7:24 23 $23 1.4% 0.0% 9/17/02 4:58 PM 9/17/02 4:58 PM

15:03 10 $19 1.2% 100.0% 9/19/02 10:32 AM 9/20/02 10:32 AM6:19 19 $19 1.1% 0.0% 9/17/02 5:22 PM 9/17/02 5:22 PM1:54 18 $17 1.1% 0.0% 9/20/02 10:39 AM 9/20/02 10:39 AM

Report 5

52


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 UnderestimatedItem Consistently OOS on Weekends

• Inadequate Shelf SpaceShort Duration OOS (< 1 day),Additional Supply is Clearly On-hand

• Distribution Center OOSRelatively Long Duration OOS with High Correlation in Geographically Close Stores


• Pattern 1: Promo Velocity Underestimated

• Store A, Fresh Express American Salad 12 oz

• Problem Corrected in January

Sample Assessment Patterns

Copyright Standard Analytics, LLC. 2006. All rights reserved.


What does this pattern indicate?

This store needs to add shelf space or check shelf stock and restock the large eggs shelf more frequently


55


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


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 45-55 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


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


Silver Cleaning-Polish and Thanksgiving Holiday Shopping: A Real Life Example On the fourth Thursday of each November in the USA, nearly everyone universally celebrates Thanksgiving. Traditionally, families and friends gather at homes for the annual formal dinner. Grocery stores stock up on plenty of turkeys, sweet potatoes, stuffing mix, pumpkin pie ingredients, cranberry sauce, and the host of other foods that compose the traditional dinner. Given that millions of homes serve a similar fare on a single day, surprisingly few of the staple items are OOS in the stores, even on Wednesday right before Thanksgiving Day. Carol went shopping on Tuesday before Thanksgiving to get the final items she needed to serve her 20 guests. Since the silver serving dishes and utensils would grace the table, she had silver cleaning polish on her list. The store carried one SKU, as a convenience item, and on this day it was out-of-stock, since Thanksgiving also functions as an occasion for annual silver polishing. The silver needed to be polished; so on her way home, Carol stopped at the drug store, and found silver cleaning polish in stock. Next year, Carol will probably need to buy silver polish again, and while she is at the grocery store she’ll probably check to see if it is in stock, and if not she’ll stop by the drug store again and get it there. And across the USA, there are thousands of others, just like Carol, who will do the same thing. What the drug store hopes is that the grocery store doesn’t ever understand its true demand, because the grocery store has no idea how many units of silver cleaning polish it might sell. Given the lack of demand data, the grocery store will continue to under-order and run out. Meanwhile, the drug store will order enough to satisfy its regular demand plus the spillover from sales lost by the grocery store. If the grocery store could know its true demand and order accordingly next year, the drug store would be stuck with a lot of extra inventory. But by the time the grocery store figures this out, Carol will stop looking for it at the grocery store and just go to the drug store to buy it.

How OOS Disguises Actual Demand

59

Silver Cleaning Polish


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.


•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%.

Product Data Accuracy

65


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)


PI Accuracy Observations

3.6%5.4%

10.0%

18.8%

45.4%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

No Var +/-1 +/-2 +/-3 +/-4

45.4% of the time there was no variance 18.8% of the time there was +/- 1 unit10% of the time there +/- 2 unit

45.4% of the time there was no variance 18.8% of the time there was +/- 1 unit10% of the time there +/- 2 unit

67


PI Accuracy For Items In One Location vs. Multiple Locations

45%

52%64%

73% 74% 83%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

No Variance +/- 1 +/- 2

PI ACCURACY AVE PI ACCURACY - ONE LOCATION ITEMS


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%

69


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


Peak Demand Planograms

0

5

10

15

20

25

30

1 13 25 37 49 61 73 85 97 109 121 133 145 157 169 181

Mui

tiple

of P

eak

vs M

ean

Skus by Category

Peak Demand versus Mean Demand

Detergent

Diapers

Fem Care

72

Fast movers are in casesOn the bottom shelf


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

75

77Weekly OOS rate measurement and analysis provided by Standard Analytics, LLC.


78Weekly OOS rate measurement and analysis provided by Standard Analytics, LLC.



Diapers Analysis

Diapers Category: 13 Week Average for 11 Stores

60%

65%

70%

75%

80%

85%

90%

95%

100%

105%

1 2 3 4 5 6 7 8 9 10 11

Store #

ComplianceAvailability

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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

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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

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Item Management

11%

6%

10%

6%

10%

6%

10%

5%

10%

6%

12%

8%

9%

4%

0%

2%

4%

6%

8%

10%

12%

14%

Mon Tue Wed Thu Fri Sat Sun

Test vs. Control - Lost Units by Day of Week

ControlTest

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

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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.


Recommended Approach

1. Measure & Assess

A. High OOS risk products (fast movers)

B. High OOS stores

C. Shelf versus Store OOS

2. Solve high risk products with Store OOS solutions

3. Solve high OOS stores with Shelf OOS Solutions

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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

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Contact for additional information:

Thomas W. Gruen, Ph.D. Professor of MarketingUniversity of Colorado, Colorado Springs, USA719-262-3335E-mail: [email protected]

For a PDF copy of the 2002 study, you can download directly from: http://www.uccs.edu/tgruen Also check the website for announcements on 2007 Report

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

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