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On Shelf Availability: An Examination of the Extent, the Causes, and the Efforts to Address Retail Out-of-Stocks

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,, e-mail:
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,, e-mail:
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)
0.0 2.0 4.0 6.0 8.0 10.0
Ot her Reg io ns
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
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-
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
Delay Purchase
Substitute --
Same Brand
Substitute --
Different Brand
Do not
Purchase Item
Fig. 2: Composite worldwide consumer responses
Average Consumer Responses by Region
(Comparisons Across Eight Comm on Categorie s)
0% 20% 40% 60% 80% 100
World Av er age
Other Regions
Eu r op e
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
0% 20% 40% 60% 80% 100%
Paper Towels
Salted Snacks
Toilet Tissue
Shampoo/Hair Care
Feminine Hygiene
Bought at Another
Delay Purchase
Substitute - Same
Substitute - Different
Do not Purchase
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
0.0 1.0 2.0 3.0 4.0 5.0
By Category
Hair Care
Fem inine Hygiene
T o ile t T is s ue
Salted Snacks
By Region
Other Regions
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
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 Ordering
Store Shelving
Retai l HQ or
Other Cause
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
34% 25%
11% 1%
0% 25% 50% 75% 100%
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
xIncongruence between shelf
capacity and replenishment
xProduct purchasing
xLarge number of SKUs in
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
xBackroom (congested).
xReceiving (receiving errors,
inaccurate records).
xShelf replenishment
(infrequent, late or no shelf
xPlanogram (bad execution and
xShrinkage (damage, theft).
Distribution Center
xData (bad data, inaccurate
xForecasting (inaccurate
xInventory (inaccurate
inventory or book-stocks).
xOrdering (no order, late order,
wrong order, ba ckorders).
xTransportati on (shipping,
xReceiving (loading errors,
inaccurate records).
xStorage (put away/ break
xReplenishment (infrequent,
late or no store
xLead times (long and
Wholesaler/Retail Headquarter
xAssortment (new or
discontinued item).
xData and communication
(master data).
xPlanogram design and
implementation (shelf
xPromotions and pricing
xAdvertising and display
xStore layout and service
xData (bad data, inaccurate
xForecasting (inaccurate
xInventory (inaccurate
inventory or book-stocks).
xOrdering (no order, late order,
wrong order, ba ckorders).
xAvailability (shortage).
xAssortment (new or
discontinued item).
xData and communication
(master data).
xPromotions and pricing
xAdvertising and display
xData (bad data, inaccurate
xForecasting (inaccurate
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-
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
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.
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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
The basic process used in the research followed five general steps:
1. Collect and review published and unpublished OOS studies
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-
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.
... The textile and apparel sectors provide employment to 75 million workers worldwide; the apparel market was valued at US$3 trillion in 2017 and has continued to expand (Fashion United, 2018). Lost sales, delay (hereinafter referred to as backlogging), brand switching, and store switching constitute usual consequences of stockout at retailers' stores (Corsten and Gruen, 2005). The responses not leading to lost sales can be categorized into active response of customers to stockout, including backlogging, brand switching, and store switching. ...
... Classic research on supply chain modeling assumes lost sales as the only response of customers to stockout, in which customers quit shopping without their intended purchase. However, an empirical study by Corsten and Gruen (2005) demonstrated that an active response was more common in cases of product shortage: customers tended to delay the purchase, switch to a similar brand offered within the same store, or switch to another store where they can buy the preferred brand. ...
... For consistency, common responses to stockout are defined corresponding to existing empirical studies. Zinn and Liu (2001) Corsten and Gruen (2005), 9% of outstock situations were found to lead to no-purchase (lost sales); 31% of consumers switched to other store to buy the same product; 19% substituted to another product of the same brand; 26% chose a different substitutable brand; and 15% delayed the purchase of the preferred product. Although existing empirical studies predominantly report on the response to stockout of grocery and hygiene products, active response to stockout seems to be common for fashion products as well (Zinn and Liu, 2008). ...
Full-text available
Customers actively respond to stockout of products in one of the following ways in retailing: they switch to a substitutable products, or switch to another store where the stockout brand is available, or delay purchase (backlogging). We introduce a model of customers’ active response to stockout of fashion brands. We model response to stockout of two brands sold by two stores. We define delay (backordering) and brand and store switching as active response to stockout. Unlike in the majority of earlier papers on substitutability, we consider realistic responses to a stockout, such as backlogging, or switching brands and/or stores. In particular, one interesting aspect of the response to a stockout is central to our research: how the opportunity to backorder or switch between brands and stores affects the profitability and the optimal order size of retailers. Furthermore, given the proliferation of retail brands, retailers of fashion products must consider how the variety of products might affect inventory and pricing decisions in the presence of strategic consumers. We developed a supply chain model consisting of stores that sell substitutable products at regular prices over a finite season, ending with clearance sales at reduced prices. The presented model includes active response of customers to a stockout between two substitutable brands sold by two stores belonging to a single retailer. Extensive numerical study was implemented in order to better understand the effect of response to stockout on optimal order sizes and equilibrium prices. The main analytical results and numerical experiments presented in this study are that active response to stockout improves the omnichannel retailer’s expected profits in the following ways: (i) backlogging brings additional revenue while, brand and store switching allows additional profits; (ii) the optimal inventory can be reduced, which helps decrease holding costs. The implication of our findings for retail managers is that retailers should consider the response to stockout and strategic consumers in their ordering and pricing decisions. Omnichannel fulfillment offers additional opportunities for retailers to benefit from active responses to stockout. The research is organized as follows. In Chapter 1, we present the background of the research, research questions and the significance of the research. In Chapter 2, we review related literature. There are five academic streams associated with this research: fashion supply chains, response to stockout, partial backlogging, omnichannel retailing, and pricing with strategic consumers. Chapter 3 introduces a base model, structure of supply chain, and associated assumptions. In addition, the same chapter presents the active response to stockout model (the main model in this research). In order to facilitate the study of the effect of response to stockout on optimal inventory and expected profits in omnichannel retailing, we extended the newsvendor model. In Chapter 4, we investigate how response to stockout positively affects pricing decisions using the concept of rational expectations. In Chapter 5, we conduct numerical experiments to find out how the presence of active response of customers to a stockout and holding costs in the newsvendor model would change the optimal order size and profitability. We conducted separate numerical experiments to find out how active response of customers to a stockout would affect pricing policy. Chapter 6 discusses the positive implications of active response of customers to a stockout for managers and consumers. Chapter 7 presents a summary and proposes topics for future research.
... The literature has many studies investigating the OOS problem. Some of the researchers focused on identifying OOS situations (Papakiriakopoulos & Doukidis, 2011;Papakiriakopoulos et al., 2009), measuring OOS (Corsten & Gruen, 2005), understanding the effects of OOS (Gruen & Corsten, 2007;Musalem et al., 2010), analyzing the main root causes (Ehrenthal & Stolzle, 2013;Fernie & Grant, 2008) and investigating customer responses in OOS situations (Zinn & Liu, 2008;Van Woensel et al., 2007). ...
Full-text available
The purpose of this study is to predict the share of visual inventory (SOVI), which is defined as the number of stock-keeping units (SKUs) of a company’s products, calculated as a percentage of the total SKUs on the display of all products. Research studies in the past have focused mainly on the impact of inventory, which includes back end and visual inventory, on sales but less attention has been given to the impact of SOVI on sales. To address this research gap, this study attempted to create an analytics model to predict SOVI at the category of soft drinks level using four predictor variables namely point of purchase display, channel/sub-channel, package group, product category, and derived variable gross national income (GNI). The results were encouraging confirming the effectiveness of such a model. The researchers utilized a data set collected over a period of 18 months (February 2016 to July 2017) by a soft drink firm headquartered in the United States. Based on the findings, it is suggested that this prediction model can be utilized by other researchers and practitioners to predict SOVI of other soft drinks, fast-moving consumer goods (FMCG), and food and beverage companies.
... Understanding how strategic consumers (also known as forward looking consumers) weigh their gain fro m future d iscounts against stockout risks in case of delayed consumption is important for development of right pricing strategy. Four alternative responses of customers represent common outcomes of product shortage: lost sales, brand switching, store switching, and delay (Corsten and Gruen, 2005). We define delay (backorder), brand and store switching as "active response to stockout". ...
Conference Paper
Full-text available
We develop a game-theoretical model of a supply chain consisting of two retailers that sell two substitutable products at regular prices over a finite season which ends with clearance sales at reduced prices. There are strategic consumers who anticipate future discounts and non-strategic consumers who purchase at the regular price if it is less than their reservation price. When a product stockout happens, a customer may switch to a substitutable product, switch to another store, or delay purchase. We find that such active response to stockout is likely to result in lower stock levels which would affect the propensity of strategic consumers to wait for future markdowns. This could mitigate negative consequences of product shortages for retailers and allow charging higher regular and clearance prices. We compare systems with alternative compositions of consumers and levels of active response to stockout and discuss how retailers could control strategic consumer behavior.
... In this customer-centric world, the adoption of IoT can improve the customer journey and one of the industries that is working actively in enhancing proactive shopper experience is retail (Vargheese & Dahir, 2014). More particularly, the on-shelf availability is a key metrics for retailers to improve the customer experience as 31% of consumer response to out of shelf situation by purchasing the product at another store (Corsten & Gruen, 2005). ...
Full-text available
The rise of new digital technologies is making firms conduct a variety of initiatives to explore their benefits. The improvements can provide businesses with competitive advantages that can lead to many potential benefits. However, in practice, the process of digital transformation is accompanied by many obstacles like regulation in the initiation phase and insufficient digital skills, culture and mindset issues to change, and lack of vision in the execution phase. Therefore, success with digital transformation requires companies to work in a variety of components continually. Successful implementation of digital transformation goes beyond technology and requires creating a transformative vision, plugging the skills gap, engaging the organization, creating a nimble and agile organization structure, and developing a digital strategy.
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This study aims at identifying different outcomes of stockout-based substitution for optimal inventory depending on modeling settings. The optimal order size in mathematical modeling of the stockout-based substitution depends on the underlying assumptions in the reviewed studies. The difference between related models in microeconomic theories with several streams of research in management science, as this review demonstrates, is that the former focus on competing firms, whereas the latter mostly describes the same independent firm that owns an assortment of substitutable products. The substitution can increase the expected profits of firms and change their inventory levels if managers adapt business strategies to prevailing market conditions. Another implication of the substitution is important for sustainable supply chain management in that policymakers and retailers might reduce inventory waste by encouraging consumers’ certain responses to stockout.
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Practitioners have long known that item-level tracking of products with the aid of radio frequency identification (RFID) has an exceptionally high potential for application with omnichannel fulfillment. Investments in both RFID and omnichannel expanded, but there was a lack of studies to assess their benefits. The introduction of omnichannel retailing is a big challenge for managers since it demands higher stocking levels due to the higher shrinkage cost. This research focuses on modeling one of the promising RFID applications: in-store merchandise tracking on item level. This model shows how a combination of consumers' response to stockout in omnichannel retailing coupled with item-level RFID tracking ensures inventory reduction, leading to higher on-shelf availability and lower cost. Thus, a modeling approach allows defining the value of the costly investment in the in-store location tracking in omnichannel fulfillment. A comparison is made between three scenarios: no-omnichannel and no-RFID; omnichannel without RFID; and omnichannel with RFID. In addition, this study examines under which parameters of costs, backlogging, brand and store switching, and other conditions one scenario could be more profitable for a retailer than another. The main finding of the study suggests the retailer's profitability could increase significantly as a result of the combined effect of investment in omnichannel fulfillment and item-level RFID. At the same time, the optimal inventory could be reduced. This study supports evidence of the higher return on investment in item-level RFID tracking of fashion products in omnichannel retailing.
Out-of-stock (OOS) problem is a significant reason of the decline of goods sales in offline supermarkets since the frequent lack of goods on the shelves can reduce the enthusiasm of shoppers. For this purpose, it is necessary to effectively detect the OOS situation, which can ensure that the products are replenished in time. In this paper, an out-of-stock detection method based on deep learning is proposed. We first introduce the Faster R-CNN algorithm to obtain location information. Then the Faster R-CNN algorithm is followed by three out-of-stock detection methods: canny operator, gray level co-occurrence matrix, and color features for out-of-stock detection. The experimental results show that the method based on canny operator performs better, which can achieve a recall rate of 83.9%, a precision rate of 91.7% and a F1-Measure of 87.6% in real scene dataset.
Build-to-order (BTO) supply chains have become commonplace in industries such as electronics, automotive and fashion. They enable building products based on individual requirements with a short lead time and minimum inventory and production costs. Due to their nature, they differ significantly from traditional supply chains. However, there have not been studies dedicated to demand forecasting methods for this type of setting. This work makes two contributions. First, it presents a new and unique data set from a manufacturer in the BTO sector. Second, it proposes a novel data transformation technique for demand forecasting of BTO products. Results from thirteen forecasting methods show that the approach compares well to the state-of-the-art while being easy to implement and to explain to decision-makers.
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Effective inventory management is critical to retailing success. Surprisingly, there is little published empirical research examining relationships between retail inventory, sales and customer service. Based on a survey of 101 chain store units, this paper develops and tests a series of hypotheses about retail inventory. Seventy-five percent of the store owners/managers responded to the mail survey. As expected, significant positive relationships were found between inventory, service and sales. Specifically, support was found for the theory that inventory is a function of the square root of sales. Also, greater product variety leads to higher inventory, and service level is an exponential function of inventory. Finally, demand uncertainty was found to have no apparent effect on inventory levels.
In spite of making substantial investments in information technology planning systems, retailers are struggling with two execution problems—" inventory record inaccuracy" and "misplaced stock keeping units (SKUs)"—that are hurting their performance and ability to satisfy customers. At one leading retailer, sixty-five percent of their inventory records were inaccurate (i.e., recorded inventory levels did not reflect actual inventory levels). Misplaced SKUs, at another leading retailer, prevented one in six customers who requested help from a sales associate from finding the products that were available in a store. These execution problems reduce profits by more than 10%. Moreover, performance along these two dimensions of execution varies substantially among stores within the same chain that use identical information technology. By examining the systematic differences that exist among stores, this article identifies the drivers of inventory record inaccuracy and misplaced SKUs and recommends steps retailers can take to improve operational execution in their chains.
2,810 consumers were interviewed regarding their response to 5 items (removed from the shelves by the researchers) that were out of stock. 375 Ss could not find the specific items they wanted. 32% of these Ss purchased a different brand, 41% purchased a different size or variety of the same brand, 13% delayed purchase, and 14% went to another store. Perceived product risk, urgency of need, and produce usage were factors in the decision to substitute. Repeat brand purchase patterns did not influence the decision to substitute but did influence the specific substitution made, if an item was substituted for what was out of stock. In those cases where no substitution was made, store loyalty influenced the decision to delay the purchase or to go to another store. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
The allocation of scarce shelf space among competing products is a central problem in retailing. Space allocation affects store profitability through both the demand function, where both main and cross space elasticities have to be considered, and through the cost function (procurement, carrying and out-of-stock costs). A model is developed which uniquely incorporates both effects. A case study is used to estimate the parameters and the problem is solved within a geometrical programming framework. An extensive comparison with alternative procedures suggests this general model leads to significantly different allocation rules and superior profit performance.
Manufacturers as well as retailers can suffer important losses as a result of stock-outs. The magnitude of these losses depends on specific consumer reactions, which have been found to vary with product, consumer, and situation factors. This paper presents a conceptual framework that integrates the major determinants of consumer reactions to stock-outs. The theoretical relationships provide explanations for the marked differences in stock-out effects observed in previous studies. Moreover, the framework can be empirically implemented, allowing retailers and manufacturers to determine how much each factor contributes to stock-out losses. We collect survey data to provide evidence on the relevance of the framework and the direction and importance of the effect of different consumer behaviors.
We explore consumer responses to stockouts, both in terms of consumer satisfaction with the decision process, and in terms of subsequent store choice behavior. A series of four laboratory experiments involving stockouts in a consumer choice context are run. The results of these experiments suggest that consumer response to stockouts is driven in large part by two factors: the effect of a stockout on the difficulty of making a choice from the set, and the degree of personal commitment to the out-of-stock alternative. We show that personal commitment to an out-of-stock choice option is a function of preference for the option, whether the option is included in the consumer's consideration set, and the degree to which the stockout announcement is personally directed. We find that as personal commitment to the out-of-stock option increases, consumers react substantially and negatively to the stockout -- they report lower satisfaction with the decision process and show a higher likelihood of...
Retail Out of Stocks: A Worldwide Examination of Extent, Causes and Consumer Responses, Grocery Manufacturers of America
  • T W Gruen
  • D Corsten
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.
Where to Look for Incremental Sales Gains: The Retail Problem of Out-of-Stock Merchandise. The Coca-Cola Research Council
  • Coca-Cola Research Council