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On Shelf Availability: An Examination of the
Extent, the Causes, and the Efforts to Address
Retail Out-of-Stocks
Daniel Corsten1 and Thomas Gruen2
1Kuehne-Institute for Logistics, University St. Gallen, Switzerland, Unter-
strasse 16, CH-9000 St. Gallen, phone +41.71.2282430 (direct: 441) fax:
455, www.klog.unisg.ch, e-mail: daniel.corsten@unisg.ch
2Assistant Professor of Marketing, University of Colorado, Colorado
Springs, 1420 Austin Bluffs Parkway, P.O. Box 7150, Colorado Springs,
CO 80933-7150 USA, Phone: 719-262-3335, FAX: 719-262-3494,
www.uccs.edu/tgruen, e-mail: tgruen@uccs.edu
1 Introduction
Several trade associations such as the Grocery Manufacturers of America
and the Food Marketing Institute, and joint trade-industry bodies such as
ECR Europe or ECR Asia have sponsored and/or released major reports on
out-of-stocks (OOS) in the past few years (see, e.g., ECR Australasia/PWC
2001; GMA 2002; ECR Europe 2003). All of this attention to OOS points
to one thing: availability of products to the customer is the new battle-
ground in the fast-moving-consumer-goods (FMCG) industry. Moreover,
our research shows that 75 percent of the responsibility for OOS rests at
the store level, but our research also found that improved availability will
be found through retailers and suppliers working together.
In an era where retail competition is so fierce than ever, retailers con-
tinue to search for ways to enhance performance. In our view, retailers are
not spending enough attention to examining their own shelves, where ac-
cording to our research retailers can boost earnings per share up to five
percent by addressing their OOS issues. After all, where else can a retailer
find so much potential revenue without spending to attract new customers?
In the pages that follow, we summarize and elaborate on the findings of
our OOS research project (Gruen, Corsten and Bharadwaj 2002; described
in Appendix 1).
132 D. Corsten and T. Gruen
2 What Is the Extent of Shelf Out-of-Stocks?
The average worldwide out-of-stock rate we found was 8.3 percent, as is
shown in Figure 1 (see Appendix 2 for discussion of what makes up an
OOS). The average of the reported highs in the studies was 12.3 percent,
and the average of the lows was 4.9 percent. This is similar to, the primary
USA benchmark of 8.2 percent that was reported in the 1996 Coca-Cola
Research Council. Our finding also falls within the range of two other re-
cent studies. A 2002 GMA study on direct-store-delivery in the USA re-
ported an out-of-stock rate of 7.4% with categories ranging from 3.2% to
11.2%. ECR Europe’s 2003 on-shelf-availability study reported an out-of-
stock rate of 7-10% with categories ranging from 5% for canned food to
18% for fresh meals and even 32% for ladies stockings. Keep in mind that
the studies used slightly different measurement methods, different people,
measured different categories, and examined different durations and dif-
ferent daily and weekly factors. All of these can affect the measurement of
out-of-stock rates. Regardless of the method, when all factors are consid-
ered together, the averages regress to an uncanny similarity. This provides
a sense that the findings are reliable in the aggregate.
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
Ot her Reg io ns
Worldwide
Percentage OOS
Fig. 1: Composite OOS Averages
When we split Europe into its northern and western region (Norway,
Denmark, Sweden, France, Belgium, Netherlands, Germany, Switzerland,
Austria, United Kingdom, Finland) and into its southern and eastern region
(Portugal, Spain, Greece, Poland, Hungary, Czech Republic, Slovakia), we
found that countries within each of these two areas showed similarities in
OOS rates, but differences between the two regions were substantial.
Northwest Europe showed the lowest OOS rates of any region in the
On Shelf Availability: An Examination to Address Retail Out-of-Stocks 133
world, while Southeast Europe showed the highest. OOS rates in “other
regions” (South America and Asia) were lower on average although details
varied.
We found several factors to affect OOS rates that were consistent across
geographies. First, for promotional effects, our research consistently found
OOS rates to be higher on promoted items than on non-promoted items. In
some cases, the differences are minor, but in most the difference is sub-
stantial – even though promoted items should be receiving retail store
managers’ attention. While the differences vary among studies, in general,
we found a 2:1 ratio of promoted vs. non-promoted OOS rates. Second,
OOS rates varied by day of the week with Sunday and Monday having the
highest levels, and levels decreasing throughout the week. This pattern
makes sense when one considers that shopping will be highest on week-
ends, while retailer ordering and delivery to stores does not occur until
Monday and Tuesday.
3 What Are the Consumer Reactions to Shelf Out-of-
Stocks?
We also looked at a worldwide study of more than 71,000 consumers that
was conducted in a series of 29 studies across 20 countries across a variety
of FMCG categories. The results of this analysis are presented in Figure 2.
Academic research has identified and categorized up to 15 possible con-
sumer responses to an OOS, though typically, managerial researchers
measure five primary responses (see, e.g., Campo et al. 2000; Emmelhainz
et al. 1991; Fitzsimons 2000; Progressive Grocer 1968; Schary and Chris-
topher 1979). All five responses result in direct and/or indirect losses to
both retailers and manufacturers. These are: (1) buy item at another store
(store switch), (2) delay purchase (buy later at the same store), (3) substi-
tute-same brand (for a different size or type), (4) substitute-different brand
(brand switch), 5. do not purchase the item (lost sale).
In the aggregate, delay of purchase and not purchasing at all are rea-
sonably similar worldwide. The major overall difference between USA
and European consumers is the lower willingness of USA consumers to
switch brands. European consumers are almost 50% more likely to switch
to a competing brand when faced with an OOS on the desired item (see
Figure 3). Alternatively, USA consumers are more likely to substitute a
different package size or variation within their preferred brand. Thus, in
the aggregate, USA consumers act in a more brand loyal manner than do
134 D. Corsten and T. Gruen
consumers outside the USA. Store switching is greatest outside the USA
and Europe. Europeans are the least likely to switch stores due to OOS.
Worldwide Consumer Responses to OOS
(Average across 8 categories)
Buy Item at
Another Store
31%
Delay Purchase
15%
Substitute --
Same Brand
19%
Substitute --
Different Brand
26%
Do not
Purchase Item
9%
Fig. 2: Composite worldwide consumer responses
Average Consumer Responses by Region
(Comparisons Across Eight Comm on Categorie s)
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
%
World Av er age
Other Regions
Eu r op e
USA
Bought at Another Stor e
Delay Purchase
Substitute - Same Brand
Substi tute - D iffer ent Brand
Do not Purc hase Item
Fig. 3: Composite consumer responses by region
Consumer Responses vary considerably by category. Figure 4 shows the
worldwide average for several of the categories examined in the study.
Several factors affect the consumer response to OOS items. Traditionally,
these have been categorized based on the nature of the category, type of
product, type of consumer, the immediacy of need, and the general brand
loyalty. However, all of these factors interact, making it difficult to de-
velop a generalized scheme to help determine the likelihood of a con-
sumer’s reaction.
On Shelf Availability: An Examination to Address Retail Out-of-Stocks 135
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 Towels
Salted Snacks
Toilet Tissue
Laundry
Shampoo/Hair Care
Toothpaste
Diapers
Feminine Hygiene
Bought at Another
Store
Delay Purchase
Substitute - Same
Brand
Substitute - Different
Brand
Do not Purchase
Item
Fig. 4: Consumer responses by category
To present a generalized approach, we found that there are three primary
drivers that interact and cause the consumer to take one action over an-
other. Using economic theory, Campo et al. (2000) present the opportunity
cost of not being able to consume the product immediately, the substitution
cost of decreased utility of a less preferred alternative, and the transaction
cost of the time and effort required to obtain the preferred item. When the
opportunity cost of not being able to immediately consume the product is
high (for example when one runs out of diapers), the consumer will either
substitute or find the item at another store. Alternatively, a low opportunity
cost will lead to either purchase delay or cancellation. When the substitu-
tion cost of using a less preferred brand is high (for example in the case of
feminine hygiene and laundry), the consumer will take any action except
to substitute another brand. When the transaction cost is high in terms of
the time and effort required to purchase later or elsewhere, the consumer
will either substitute or cancel purchase. This perspective explains why
consumers tend to switch more in some categories than others. For exam-
ple, we found that feminine hygiene has low substitution, since these are
very personal products and there is a high substitution cost. However,
when the brand is less personal e.g., paper towels, more substitution be-
tween brands may occur.
136 D. Corsten and T. Gruen
4 What Is the Cost of Shelf Out-of-Stocks to the Retailer?
While most studies concentrate on the sales loss to the retailer created by
OOS items, the total “cost” of OOS can be divided into four areas: 1) re-
tailer shopper loss risk, where shoppers permanently switch stores due to
OOS situations; 2) retailer sales loss risk, where consumers buy the OOS
item at another store, cancel their purchase, or substitute a smaller and/or
lower priced item; 3) manufacturer shopper loss risk, where consumers
switch to a competitor’s brand within a category, not only for the immedi-
ate purchase but also for ongoing purchases; and 4) manufacturer sales
loss risk, where consumers substitute a competitor’s item or cancel a pur-
chase. The key to understanding the implications of OOS (as well as the
benefits of addressing OOS at the retailer) is that the four areas of loss are
interdependent. A reduction in the sales loss to the retailer also reduces the
resulting shopper loss risk, the risk to the supplier, and the resulting sup-
ply-chain inefficiencies.
Fig. 5: Computed losses due to OOS by region and category
Most of the attention in measurement has been in the area of retailer
sales loss, which is typically estimated based on the following formula:
Percentage of consumer responses that negatively affect the retailer x
OOS Extent.
Figure 5 shows that the worldwide benchmark average is 3.9% sales
loss at retail due to OOS items. The regional averages as well as world-
wide averages by category are also presented. The chart shows that overall
sales losses are similar worldwide, with a narrow range from 3.7%-4.0%.
Sales Losses D ue to OO S
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
Fem inine Hygiene
Laundry
T o ile t T is s ue
Salted Snacks
By Region
Other Regions
USA
Europe
W orld Average
Estimated Percentage Loss
On Shelf Availability: An Examination to Address Retail Out-of-Stocks 137
However, category sales losses vary dramatically from 2.1% to 4.5%. Re-
gardless of how the data is cut, the implication is still the same: both the
manufacturer and the retailer have created value for the consumer, but
nearly 4% of this effort is wasted because the retailer cannot extract the
value from the consumer due to OOS items.
Fig. 6: Consumer responses to OOS negatively affect information flows
Other implications of OOS include logistics and information inefficien-
cies in the supply chain. Irregular, fill-in, and “rush” orders due to OOS
situations cause logistics-fulfillment inefficiencies. These are subject to
“demand amplification” or the “bullwhip effect”, where small shifts at the
retail level become magnified further up the supply chain. Information in-
efficiencies are created when the ordering signals sent up the supply chain
reflect a pattern other than true consumer demand. What is worse, out-of-
stocks not only disappoint customers, but perpetuate themselves and drive
up costs throughout the supply chain. When a retailer needs to reorder a
product, the buyer will typically examine the sales history of that product.
When the item has been out of stock, the sales history data provides inac-
curate information on what is the necessary purchase quantity to meet ac-
tual consumer demand. If the out-of-stock has not been detected, then the
buying decision will most likely be too low to meet the normal customer
demand plus those consumers who delayed purchase until the retailer re-
ceived additional stock. Alternatively, if the buyer is aware of the OOS
situation, the tendency may be to over-order, because the buyer is unable
to determine the permanent customer loss to the brand caused by the OOS
through brand substitution or to the store due to store switching. In sum,
the OOS forces the buyer to work with a greater margin of error, and this
increases the variability in the ordering, as summarized in Figure 6.
When consumers
xSwitch Stores
xDelay Purchases
xSubstitute Sizes
xSubstitute Brands
xDon’t Purchase
Intended Items
Inaccurate Picture to the
Supply Chain of
xProduct Mix
xProduct Levels
xProduct Flow
This
Sends
138 D. Corsten and T. Gruen
5 What Are the Root Causes of Shelf Out-of-Stocks?
Previous studies have placed most of the responsibility for OOS on retailer
store ordering and forecasting practices. Our research confirms this, as
Figure 7 shows. Between two-thirds and three-fourths of OOS are caused
in the store, while one-fourth to one-third is due to upstream causes.
Worldwide, the two greatest causes are inaccurate forecasting (34%), an
indicator of increasing demand volatility, and shelf-replenishment (25%).
The latter is particularly surprising when compared to the much-cited 1996
Coca Cola Research Council study. That study attributed a higher percent-
age to ordering (19%) and forecasting (54%), but it traced an average of
only eight percent of the OOS situation to product being available in the
backroom but not on the shelf.
Summary of Findings of OOS Causes
Worldwide Average
Store
Forecasting
13%
Store Ordering
34%
Store Shelving
25%
Distribution
Center
10%
Retai l HQ or
Manufacturer
14%
Other Cause
4%
Fig. 7: Composite average causes of OOS
Figure 8 shows how OOS causes vary by region. We were surprised to
find that, in the USA, significantly more causes of out-of-stocks are attrib-
uted to ordering practices (51%) than in Europe (32%). On the other hand,
in Europe, there seem to be more problems with regard to replenishment
(47%) than in the USA (32%), particularly shelf replenishment (i.e. when
the product is already in the store). This is counterintuitive, as one would
have guessed that smaller back rooms and efficient transport networks in
Europe would alleviate this cause. Somewhat striking, 72% of all OOS
On Shelf Availability: An Examination to Address Retail Out-of-Stocks 139
across the world are caused in the store, by poor store processes, late and
insufficient ordering, incorrect forecasts, or shelf restocking problems. Re-
tailer store managers must simultaneously manage thousands of stock-
keeping units and work with hundreds (often thousands) of simultaneously
promoted items (which cause demand to fluctuate), while keeping person-
nel costs in reason (Dubelaar et al. 2001). Furthermore, retailers face com-
plementary issues, such as shrinkage that becomes more difficult to control
as inventories increase. Thus, it is not surprising to see a strong linkage of
out-of-stocks with store ordering practices.
OOS Causes by Region
18%
11%
9%
13%
33%
49%
22%
38%
15%
11%
9%
10%
13%
11%
15%
14%
3%
10%
4%
21%
34% 25%
11% 1%
0% 25% 50% 75% 100%
U.S.
Europe
Asia
World Average
Store Forecasting Store Ordering
Store Shelving and Merchandising Distribution Center
Retail HQ or Manufacturer Cause Other Causes
Fig. 8: Regional differences in OOS causes
However, the real story is more complex. Broadly speaking, causes of
out-of-stocks tend to be assigned to one of the following three general
processes, detailed in Figure 9.
140 D. Corsten and T. Gruen
Fig. 9: Root Causes of OOS by Supply Chain Level
Exhibit 9
Root Cause
Planning Ordering Replenishing
Store
xIncongruence between shelf
capacity and replenishment
frequency.
xProduct purchasing
frequencies.
xLarge number of SKUs in
assortment.
xData (bad POS data,
inaccurate records).
xForecasting (inaccurate
forecast, long cycles).
xInventory (inaccurate
inventory or book-stocks).
xOrdering (no order, late order,
wrong order, ba ckorders).
xStaffing (insufficient or busy
staff).
xBackroom (congested).
xReceiving (receiving errors,
inaccurate records).
xShelf replenishment
(infrequent, late or no shelf
filling).
xPlanogram (bad execution and
compliance).
xShrinkage (damage, theft).
Distribution Center
xData (bad data, inaccurate
records).
xForecasting (inaccurate
forecast).
xInventory (inaccurate
inventory or book-stocks).
xOrdering (no order, late order,
wrong order, ba ckorders).
xTransportati on (shipping,
loading).
xReceiving (loading errors,
inaccurate records).
xStorage (put away/ break
pack).
xReplenishment (infrequent,
late or no store
replenishment).
xLead times (long and
infrequent).
xShrinkage.
Wholesaler/Retail Headquarter
xAssortment (new or
discontinued item).
xData and communication
(master data).
xPlanogram design and
implementation (shelf
allocation).
xPromotions and pricing
decisions.
xAdvertising and display
planning.
xStore layout and service
levels.
xData (bad data, inaccurate
records).
xForecasting (inaccurate
forecast).
xInventory (inaccurate
inventory or book-stocks).
xOrdering (no order, late order,
wrong order, ba ckorders).
xAvailability (shortage).
Supplier
xAssortment (new or
discontinued item).
xData and communication
(master data).
xPromotions and pricing
decisions.
xAdvertising and display
planning.
xData (bad data, inaccurate
records).
xForecasting (inaccurate
forecast).
xInventory (inaccurate
inventory or book-stocks).
xOrdering (no order, late order,
wrong order, ba ckorders).
xAvailability (packaging, raw
materials and ingredients).
On Shelf Availability: An Examination to Address Retail Out-of-Stocks 141
They are described as follows: 1) ordering practices, which is when the
retail store may have ordered too little or too late, so that the warehouse
could not deliver before the retailer ran out of the item, or when the retailer
forecast may have misjudged demand for an item and ordered an insuffi-
cient supply; 2) Replenishment practices, which is when the product is in
the store (often in the backroom, but also sometimes in another area of the
store) but not on the shelf when the consumer comes to buy the product, or
when the warehouse may have insufficient inventory to meet demand and
“scratches” the retailer’s order; and 3) planning practices, which is when
the item may have been discontinued but not communicated to the retailer,
the manufacturer may not have shipped adequate inventory, or there may
be a product “drought”, namely the manufacturer is unable to produce
enough to meet demand.
6 How Can On-Shelf Availability Be Improved?
The previous discussion showed that the majority of the root causes are in
the store, however, that’s not the place to start in order to find a solution.
Ideally, a sustainable on-shelf availability management process consists of
a set of linked decisions on category tactics and shelf space allocation, as
well as the mode, frequency and quantity of ordering and replenishment.
An integrated process must address the three supporting pillars of process
responsiveness, operational accuracy, and incentive alignment in order to
effectively address the root causes of out-of-stocks.
Remedy 1: Process Improvements
Assortment Planning and Space allocation. Given the continuously chang-
ing and growing assortments, most stores end up in a dilemma where they
allocate relatively too little shelf space for fast movers and too much shelf
space for slow movers (Corstjens and Doyle 1981). Fast movers are par-
ticularly susceptible to out-of-stocks and, counter-intuitively, we found
that fast movers often get less than their fair allocation of shelf-space given
their sales potential. Clearly, a fast mover or a promoted item with high
demand volatility needs more, rather than less, shelf space to fulfill con-
sumer demand at any given moment otherwise it runs the risk of being out-
of-stock.
Automatic Ordering systems: Traditionally, store managers evaluate in-
ventory by walking through the store, and order products based on intui-
142 D. Corsten and T. Gruen
tion rather than on accurate forecasts. This of course leads to lost sales be-
cause near and complete out-of-stocks are spotted too late. While shelf-
replenishment remains, even today, a predominantly manual process,
automatic or computer-assisted store ordering has emerged as a key lever
for better on-shelf availability. Spain, for instance, has improved availabil-
ity by more than 66% (i.e. from 13-15% OOS to 5%) in test stores that
moved from manual ordering to computer-assisted store ordering.
EDI, Internet and Real-time Ordering: Batching orders disrupts the
product flow to the shelf, and causes the well-known “Bullwhip Effect”
throughout the supply chain (Lee 2002). To address this, many retailers
have already increased their ordering frequency, implemented EDI and
internet ordering, introduced mixed truckloads, adapted minimum pack
sizes, reworked delivery schedules and automated ordering to break
batches. Tesco has gone even further, by exploring how its systems can
pass orders continuously to its suppliers, rather than once a night.
Inventory Control: Retailers and suppliers can and should work together
to reduce total supply chain inventory. While, intuitively, most would
think that supply chain inventory levels positively correlate with on-shelf-
availability, we found the contrary to be true. Higher supply chain inven-
tory actually correlates with higher out-of-stock rates! This apparent para-
dox can be explained by the fact that retailers with lower inventory levels
tend to manage their supply chains better and have their inventories in the
appropriate places.
Remedy 2: Improve Operational Accuracy
Automatic Availability Measurement: Advanced technology-based solu-
tions have emerged that automate out-of-stock measurement and detection.
For example, Sainsbury’s has introduced an automatic “Shelf Availability
Monitor” (SAM). This system tracks the sales transaction data (rather than
the inventory) for a store’s top 2,000 products, and can be used to flag
items that may be out of stock. It is in use at most stores, with regular re-
ports highlighting where sales have been missed, how long items have
been unavailable, and converting these numbers into a cash figure of lost
sales. Furthermore, it tracks a store’s sales in 15-minute blocks, and stores
can plan their activities to ensure products are available when there is
likely to be shopper demand. Since the introduction of SAM in early 2001,
there has been an improvement across the company of 1% on-shelf avail-
ability. Another solution has been developed by Data Ventures and Procter
& Gamble. The “Item Velocity Monitor” predicts with 90% accuracy the
On Shelf Availability: An Examination to Address Retail Out-of-Stocks 143
out-of-stock status for items that move four or more times per day. This
can provide a real-time signal to store managers and does not depend on
store inventory records. These new solutions all share the ability to utilize
technology (as opposed to inventory or manpower) to address out-of-
stocks items on a rapid basis. This provides the potential benefits of re-
duced out-of-stock levels without committing high cost labor to address
the problem.
Inventory Record Accuracy: Inventory inaccuracy presents a major ob-
stacle to on-shelf-availability, and needs to be addressed. This is crucial,
since ordering and inventory models assume that inventory records are ac-
curate. However, recent research indicates that inaccurate inventory levels
and misplaced stock-keeping-units are both significant and expensive, con-
tributing to a profit reduction of more than 10 percent (Raman and Ton
2001). Due to data inaccuracy, retailers have to hold larger safety stocks,
which increase the inventory costs. In addition, when inventory records
(that are based on point-of-sales data) differ significantly from physical in-
ventory levels, retailers cannot effectively use point-of-sales data for in-
ventory management purposes.
Automatic Identification: One exciting technological advancement that
provides great hope for improving on-shelf availability is based on the
emerging technologies of intelligent tags or smart chips such as those pro-
moted by the Massachusetts Institute for Technology’s Auto-Identification
center. Recently, Procter & Gamble and SAP announced a joint trial of the
use of radio-frequency identification (RFID) transponders. In Rheims,
Germany, Metro introduced a test of this technology in a retail store.
Remedy 3: Improve Incentive Alignment
Ordering Incentives: Store managers are faced with a confusing array of
incentives. We found that many retailers penalize their store managers for
out-of-stocks instead of encouraging them to improve on-shelf-availability.
Simultaneously, store managers are generally liable for stolen merchandise
and other sources of shrinkage. This encourages managers to purposely
keep shelves “empty” or lock up merchandise behind the counter. Fur-
thermore, when store managers are penalized for high inventory they will
reduce stocks despite the risk of out-of-stocks. Hence, rather than penaliz-
ing inventory, stores should focus on on-shelf availability.
Incentive System: An even larger problem than the mixed incentives to
managers is the lack of connection between headquarters buyers and the
retail store managers. Buyers determine which products should be held by
144 D. Corsten and T. Gruen
the stores, but often they do not base their decisions on store sales informa-
tion, nor do they account for the store managers’ understanding of their
shoppers’ behavior. Rather, the buyers’ decisions are governed by a range
of functional factors, including purchasing term negotiations, margin, and
volume-based performance incentives. To compensate for this, many
stores deviate from the list of products prescribed by the buyers at the
headquarters.
Change Culture: To motivate associates to have a real passion for avail-
ability retailers such as Delhaize or Safeway have begun to create a culture
with a passion for availability. They have launched comprehensive initia-
tives involving supply chain and store associates, internal competitions and
awards. By setting tough targets, aligning incentives and controlling the
process, they have achieved a change in employee attitudes to availability.
7 Conclusions
What does one conclude from all of this? There are several lessons.
xFirst, all of the studies we examined point to a common concern: OOS
has been, is, and will continue to be a problem. The aggregate extent
we found of 8.3 percent (and the similar results found through other in-
dustry studies) continue to (and should) raise alarms throughout the
FMCG industry.
xSecond, OOS is costly. While the total costs to the supply chain have
not been investigated, we found that, worldwide, average sales loss due
to OOS is 3.9%.
xThird, not all OOS are the same. A slow moving item that is OOS will
be less costly to the store than a fast moving item. Similarly, consumer
substitution varies extensively among categories, affecting the retailer
and manufacturer to different degrees.
xFourth, duration of OOS is important. While techniques for measuring
the duration of OOS are fairly new, the impact of long-term OOS prob-
lems affects not only the sales of the item, but also the likely potential
of a consumer to switch stores.
xFifth, most of the responsibility for lowering OOS rests in the retail
store. Unfortunately, manufacturers have placed their resources to-
wards lowering OOS on solving supply chain problems. This focus
will need to shift, if the problem of OOS is to be effectively addressed.
xSixth, as we examined consumers across the world, we found that con-
sumers are indeed localized in their choices. However, when their
choice is taken away through an item being out-of-stock, consumers
On Shelf Availability: An Examination to Address Retail Out-of-Stocks 145
behave in a similar manner globally. In the end, the retailers (and their
supply chains) that satisfy customers on this issue will be those more
likely to succeed.
In summary, improving availability is imperative but it comes at a price.
Reducing OOS requires initiatives that cut across functional boundaries
and may require a fundamental rethinking of retailer processes. We believe
most retailers have not yet reached the threshold where it will cost them
more not to reduce the incidence than it will cost them to invest in solu-
tions. Clearly, there is a minimum out-of-stock rate where cost to reduce
further is more than the benefit. In fact, in some categories occasional out-
of-stocks can be even beneficial, as certain availability may eventually in-
crease price competition. Regardless, out-of-stock (or its counterpart,
availability) remains a major issue not only for retailers, but also for all
parties in the supply chain.
8 References
Campo, K., Gijsbrechts, E., and Nisol, P. (2000), Towards understanding
consumer response to stock-outs, Journal of Retailing, 76 (2), pp. 219-
242.
Coca-Cola Research Council / Andersen Consulting (1996), Where to
Look for Incremental Sales Gains: The Retail Problem of Out-of-Stock
Merchandise. The Coca-Cola Research Council, Atlanta, GA, USA.
Corstjens, M. and Doyle, P. (1981), A Model for Optimising Retail Space
Allocations, Management Science, 27 (7), pp. 822-833.
Dubelaar, C., Chow, G., and Larson, P. D. (2001), Relationships between
inventory, sales and service in a retail chain store operation, Interna-
tional Journal of Physical Distribution and Logistics Management, 31
(2), pp. 96-108.
ECR Australasia and PriceWaterhouse-Coopers (2001), A Guide to Effi-
cient Replenishment and Reducing “Stock Outs” within the Grocery
Industry.
ECR Europe and Roland Berger (2003), Optimal Shelf Availability: In-
creasing Shopper Satisfaction at the Moment of Truth.
Emmelhainz, M. A., Stock, J. R., and Emmelhainz, L. W. (1991), Con-
sumer Response to Retail Stock-outs, Journal of Retailing, 67 (2), pp.
138-147.
Fitzsimons, G. J. (2000), Consumer Response to Stockouts, Journal of
Consumer Research, 27(September), pp. 249-266.
146 D. Corsten and T. Gruen
Grocery Manufacturers of America and Roland Berger (2002), Full-shelf
Satisfaction. Reducing Out-of-Stocks in the Grocery Channel: An In-
Depth Look at DSD Categories.
Gruen, T. W., Corsten, D., and Bharadwaj. S. (2002), Retail Out of Stocks:
A Worldwide Examination of Extent, Causes and Consumer Responses,
Grocery Manufacturers of America, Washington D.C.: 2002.
Lee, H. (2002), Demand Based Management, ECR Journal, 2 (2), pp. 61-
73.
Progressive Grocer (1968a). The Out-of-Stock Study: Part I. October, pp.1-
16.
Progressive Grocer (1968b). The Out-of-Stock Study: Part II. November,
pp.17-32.
Raman, A., DeHoratius, N., and Ton, Z. (2001), Execution: The missing
link in retail operations, California Management Review, 43 (3), pp.
136-152.
Schary, P. B. and Christopher, M. (1979), The Anatomy of a Stock-Out,
Journal of Retailing, 55(2), pp.59-70.
Appendix 1: The Research Study Description
This paper is based on a report entitled, Retail Out-of-Stocks: A Worldwide
Examination of Extent, Causes, and Consumer Responses (Gruen, Corsten
and Bharawaj 2002). This report presents what is believed to be the largest
and most current single compilation of findings regarding the extent,
causes, and consumer responses to retail out-of-stock (OOS) situations in
the fast moving consumer goods (FMCG) industry. This is also the first
study that enumerates OOS on a worldwide basis. Funded by a grant from
the Procter & Gamble Corporation, the study was conducted in 2001-2002.
The inputs for this report come from 52 studies that examine OOS. This
includes previously published results of 16 industry and academic studies,
as well as the results from an additional 36 studies proprietary to this re-
port. To provide a sense of the extensiveness of the studies that were used
to develop this report, consider the following:
xNumber of retail outlets examined: 661
xNumber of FMCG categories included: 32
xNumber of consumers surveyed world-wide: 71,000
xNumber of countries represented: 29
xStudies addressing extent of OOS: 40 (of 52 total studies)
xStudies addressing the root causes of OOS: 20 (of 52 total studies)
On Shelf Availability: An Examination to Address Retail Out-of-Stocks 147
xStudies addressing the consumer responses to OOS: 15 (of 52 total
studies)
The basic process used in the research followed five general steps:
1. Collect and review published and unpublished OOS studies
worldwide.
2. Collect and review related research on OOS from academic and
applied sources.
3. Delineate findings from research.
4. Isolate limiting factors.
5. Synthesize findings and determine areas of consensus, trends and
key findings.
More specifically, to develop this report, information was collected and
synthesized from the following general sources:
xPreviously published industry reports and studies of out-of-stocks.
xNew data provided by two large-scale consumer studies conducted
in 1999-2000 (one in the U.S. and a second identical study con-
ducted in 19 countries outside North America).
xNew data provided by studies of three retailers’ scanner and inven-
tory data conducted in 1999-2001.
xNew data provided by a series of traditional store audit studies
conducted in 1998-2000.
xVarious academic articles published from 1962-2001 on out-of-
stock studies.
xIndustry press and articles that addressed and/or reported on other
out-of-stock studies.
The academic and industry studies provided background and theory re-
garding the way out-of-stocks have been measured, the likely consumer re-
sponses to out-of-stocks, and the value of addressing the issue at the retail
level. The majority of the academic studies focused on consumer responses
and provided important theoretical and categorical approaches to examin-
ing consumer response data. The industry studies were examined to pro-
vide baselines for evaluating the information we would then examine from
the new studies. The review of the industry studies led us to systematically
arrange the information contained in all studies into the following catego-
ries:
xMethodology.
xCategories examined.
xExtent of out-of-stocks found.
xConsumer responses.
xRoot causes identified and assigned.
148 D. Corsten and T. Gruen
xEfforts examined / suggested to address out-of-stocks, the costs
and returns.
The logic of the arrangement is straightforward. First, the methodology
was reviewed to determine any likely limitations or concerns faced when
examining the data from the study. This methodology also provided a way
to categorize the studies. Second, the categories examined were listed in
order to make comparisons among the studies that examined the same or
similar categories. Consumer responses to OOS situations tended to vary
widely among categories, thus category identification is a key variable.
Following general categorization, examination of the extent of out-of-
stocks in the report was the logical place to begin, since it answers the
question: “Is there a problem?” After identifying the extent, the logical
next question is: “Does the OOS matter?” This is answered by examining
the consumers’ responses to OOS situations. The search for the cause to
the problem leads to the next question: “Who is responsible for causing the
problem?” This leads to the final questions: “Can and should it be fixed?
If so, how?”
Appendix 2: Measuring Out-of-Stocks
The definition of what makes an OOS affects the extent that gets reported
in studies. While many variations exist, recent studies tend to settle on a
consumer-based definition. Two general alternative definitions emerge
based on the method of measurement. As the first and most accepted ap-
proach, the OOS rate is measured as a percentage of SKUs that are out-of-
stock on the retail store shelf at a particular moment in time; i.e., the con-
sumer expects to find an item that the store usually carries, but it is not
available. Normally, the OOS rate is reported for each category individu-
ally, and then the categories are averaged (normally unweighted average)
to create and report an overall rate for the study. Due to the number of
studies that have used this approach, a major advantage of using this
method is the availability of excellent baselines. The limitations to this
type of measurement include the arbitrary nature of selection of the catego-
ries, frequency and timing of the audits, duration of the study, and human
error that can and does enter from many sources. In addition, differences in
sales volume are not taken into consideration hence this definition does not
indicate lost sales.
A second definition of an OOS is the number of times a consumer actu-
ally looks for the SKU and does not find it. The percentage rate is calcu-
lated as the number of times the consumer does not find the SKU divided
On Shelf Availability: An Examination to Address Retail Out-of-Stocks 149
into the sum of the times the consumer does find the SKU plus the number
of times the consumer does not find it. Instead of relying on physical au-
dits, the second approach is measured through the use of models that de-
termine OOS rates from store scanner and inventory data. This view pro-
vides the advantage of determining the extent of out of stocks that actually
matter to the retailer and the upstream supply chain members. The major
limitation of this method is that the OOS rates are estimated based on his-
torical sales patterns, and thus can only be calculated for SKUs that sell
with a minimum frequency (thus, it cannot detect OOS for very slow mov-
ing products). Few studies have used this method, and therefore baselines
do not readily exist.
