Transcript No Slide Title

Analysis and Modeling of Inventory Shrinkage
November 12, 2001
Summary
The current economic downturn will increase inventory shrinkage from shoplifting
by $1 billion in 2002, based on the economic model developed in this report.
The graphic shows the incremental cost triggered by rising unemployment and
growing strain on household spending, compared with what might have been
expected if the economy had continued to grow at its 2000 pace.
Inventory Shrinkage from Shoplifting
(Billions of Dollars, Actual vs. Predicted)
$16
$1 billion increase in shrinkage
from shoplifting because of
economic downturn.
$15
$14
$13
$12
$11
$10
$9
$8
$7
1993
2
1994
1995
1996
1997
1998
1999
2000
2001
2002
Source: Retail Forward Inc., U.S. Department of Commerce, University of Florida Retail Security Survey, and FBI Uniform Crime Reports
Summary
Table of Contents:
Page
Summary
2
Background on Total Shrinkage
4
Focus on Shoplifting Shrinkage
8
An Economic Model of Shoplifting
10
The Modeling Approach
12
Population as Another Key Driver
15
The estimated cost is based on an economic
model that found that shoplifting trends can be
explained by changes in unemployment claims,
the savings rate and retail prices.
The growing population of youth age 15 to 24
represents another factor that will tend to
increase inventory shrinkage from shoplifting.
These economic factors should reverse the trend
of recent years during which shoplifting eased as
a source of inventory shrinkage.
In total, shrinkage from all sources  shoplifting,
employee theft, vendor fraud and paperwork
error  reached an estimated $39 billion in 2000.
Shoplifting accounted for one-third of the total.
3
A Positive Recent Trend
During the booming economy of
recent years, inventory
shrinkage – from all sources –
has seemingly remained under
control.
Total Shrinkage as a Percentage of Retail Sales
2.0%
1.9%
1.8%
1.7%
In 2000, total shrinkage slipped
to 1.7% of retail sales, according
to a survey by the University of
Florida.
1.6%
1.5%
1.4%
1.3%
1.2%
1.1%
1.0%
1993
1994
1995
1996
1997
Source: University of Florida National Retail Security Survey
4
1998
1999
2000
Over the last eight years,
shrinkage has trended slightly
downward from nearly 2.0% of
retail sales in 1994.
Differences by Retail Segment
Not surprisingly, segments such as book and music stores that sell smaller-sized
products have higher inventory shrinkage rates compared with segments such as
consumer electronics and furniture stores that sell larger products.
Although these rates
by segment are
volatile year-to-year,
they show that the
economic slowdown
in 1996 resulted in a
noticeable increase
in shrinkage rates
for discount stores,
department stores
and furniture/home
furnishings stores.
Shrinkage as Share of Sales by Type of Retailer
(Average Since 1993)
Consumer electronics & appliances
0.9%
Furniture & household furnishings stores
1.0%
1.3%
Shoe stores
Supermarkets & grocery stores
1.6%
Sporting goods stores
1.6%
Total Retail
1.8%
Home centers, lumber & garden supply
1.8%
1.8%
Jewelry & optical stores
Department stores
1.8%
1.8%
Apparel stores (men, women & children)
Discount stores
2.0%
Drug stores & pharmacies
2.1%
Convenience, liquor wine & beer stores
2.1%
Toy & hobby stores
2.1%
Recorded music & video stores
2.3%
Book stores/ greeting card & novelty shops
2.3%
0.0%
0.5%
1.0%
Source: University of Florida National Retail Security Survey and Retail Forward Inc.
5
1.5%
2.0%
2.5%
Total Costs Still Rising
Despite the slight decline in the overall shrink rate in recent years, inventory
shrinkage measured in dollars rose to an estimated $39 billion in 2000 from $30
billion in 1993 as the total retail market grew briskly over that time span.
The losses are estimated at
retail value by applying the
annual shrinkage rates to
total retail sales.
Total Inventory Shrinkage
(billions of dollars)
$45
$40
$35
$30
Total retail sales, excluding
motor vehicle sales and food
service, registered $2.3
trillion in 2000, according to
the U.S. Department of
Commerce.
$25
$20
$15
$10
$5
$0
1993
1994
1995
1996
1997
1998
1999
2000
Source: University of Florida Survey, U.S. Department of Commerce and Retail Forward Inc.
6
2001
A Shift in Sources of Shrink
During the economic boom of recent years, slower growth in shoplifting
contributed to a striking shift in the sources of shrinkage.
As shoplifting became a significantly smaller share of shrinkage by 2000,
employee theft grew to be a larger share of shrinkage compared with 1996.
Sources of Shrinkage in 1996
Sources of Shrinkage in 2000
Vendor Fraud
5%
Vendor Fraud
6%
Paperwork Error
19%
Employee Theft
38%
7
Source: University of Florida National Retail Security Survey
Paperwork Error
18%
Shoplifting
36%
Employee Theft
44%
Shoplifting
33%
Shoplifting Lagged in Good Times
As shoplifting grew slowly during the boom times, it noticeably lagged the growth
of total inventory shrinkage.
Shrinkage from shoplifting is
estimated at nearly $13 billion
in 2000, which is only
modestly higher than the
$12.2 billion estimated for
1996.
Inventory Shrinkage by Source
(billions of dollars)
$45
$40
Total Shrinkage
$35
$30
$25
$20
$15
Shrinkage from Shoplifting
$10
$5
$0
1993
1994
1995
1996
1997
1998
1999
2000
Source: University of Florida Survey, U.S. Department of Commerce and Retail Forward Inc.
8
2001
Shoplifting Cases Declined
The slow growth in shrinkage from shoplifting corresponds with a dramatic
decline in the number of shoplifting cases reported by the FBI since 1996.
This decline in shoplifting was likely the result of a booming economy.
Plummeting unemployment rates and strong income growth dramatically reduced
the factors that cause people to shoplift.
As a result, the number of
shoplifting cases is likely to
increase significantly in 2001
and 2002 as the U.S.
economy deteriorates.
Shoplifting Cases
(in thousands)
1,600
1,400
1,200
1,000
800
600
400
200
* Cases prior to 1975 derived from crime reports data
1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000
9
Source: FBI Uniform Crime Reports and Retail Forward Inc.
Shoplifting Will Rise Again
The expectation that an economic downturn will increase retail inventory
shrinkage is confirmed by an economic model of shoplifting.
The model shows that most of the change in shoplifting over time can be
explained by three variables: unemployment claims, the savings rate and retail
prices.
Shoplifting tends to increase
with rising unemployment
claims, a growing savings
rate and higher retail prices.
Shoplifting Cases
(Actual vs. Predicted)
1,600
1,400
Actual
1,200
1,000
Predicted
The model predicts a jump in
shoplifting in 2001 and 2002,
assuming the recent rise in
jobless claims and saving
persists into 2002.
10
800
600
400
200
1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002
Source: FBI Uniform Crime Reports and Retail Forward Inc.
$1 Billion Cost
The predicted increases in shoplifting cases translate into $1 billion of
incremental shrinkage losses in 2002 for retailers from shoplifting alone.
These losses are calculated by comparing shoplifting shrinkage given higher
unemployment claims and savings to the shrinkage that would have been
expected if unemployment claims and savings remained at their 2000 pace.
Inventory Shrinkage from Shoplifting
(Billions of Dollars, Actual vs. Predicted)
$16
$1 billion increase in shrinkage
from shoplifting because of
economic downturn.
$15
$14
$13
$12
$11
$10
$9
$8
$7
1993
11
1994
1995
1996
1997
1998
1999
2000
2001
2002
Source: Retail Forward Inc., U.S. Department of Commerce, University of Florida Retail Security Survey, and FBI Uniform Crime Reports
The Modeling Approach
The economic model emerged from a
statistical modeling process that
considered a range of potential
explanatory variables.
Considered were 15 macroeconomic
variables, plus another 14 population
age brackets.
A number of these variables, including
certain population variables, showed a
significant relationship to shoplifting.
The three variables in the model were
the combination that together best
explained movements in shoplifting.
12
These were the variables considered as potential
explanatory variables in the modeling:
Retail Sales Less Autos & Gasoline
Income
Consumer Credit
Saving Rate
Housing Starts
Employment
Manufacturing Employment
Unemployment Rate
Unemployment Claims
Unemployed Persons
Price Index for Retail Sales Less Autos & Gasoline
Core Consumer Price Index (excluding food & energy)
Gasoline Prices
10-Year Treasury bond rate
Federal Funds Rate
Population in five-year age brackets
(i.e., Age 10-14, Age 15-19, etc.)
The Economic Model
The economic model actually models the change in shoplifting cases over time
instead of the level number of cases.
The equation for the model is expressed this way:
Shoplifting = .17 Unemployment Claims + .06 Savings Rate + .92 Prices
Each coefficient is essentially
an elasticity measure that
tells how much shoplifting will
increase for each percent
increase in the explanatory
variable.
For example, a 10% increase
in unemployment claims
would be expected to
increase shoplifting by 1.7%.
Shoplifting Cases
20%
(Percent Change, Actual vs. Predicted)
15%
Predicted
Actual
10%
5%
0%
1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001
-5%
-10%
Source: FBI Uniform Crime Reports and Retail Forward Inc.
13
The Key Drivers
Saving Rate
Unemployment Claims
(average monthly claims in thousands)
700
600
500
Prices in Retail Channels Excluding Autos
12
12%
10
10%
8
8%
400
6%
6
300
4%
4
200
2%
100
2
-
0
1970
1975
1980
1985
1990
1995
2000
Source: U.S Bureau of Labor Statistics and Retail Forward Inc.
14
(Percent Change)
0%
1970
1970
1975
1980
1985
1990
1995
2000
Source: U.S Bureau of Economic Analysis and Retail Forward Inc.
Each spike in
unemployment claims
during a recession is
associated with an
increase in shoplifting.
The recent falloff in
shoplifting coincided
with a big decline in the
saving rate as incomes
and wealth soared.
As claims now reach
1991 recession levels,
shoplifting should be
pushed higher.
As households tighten
their belts again,
shoplifting should be
pushed higher.
1975
1980
1985
1990
1995
2000
-2%
Source: U.S Bureau of Economic Analysis and Retail Forward Inc.
Shoplifting tends to rise
amid high inflation as
goods are priced out of
consumers’ reach.
As the recent tame price
environment continues,
shoplifting should not be
pushed higher by price
pressures.
Population as a Driver
Although a population variable was not among the key explanatory variables, the
modeling generally showed a positive relationship between shoplifting and the
age brackets under age 35 – and conversely a negative relationship between
shoplifting and the age brackets above age 45.
The link between shoplifting
and age is also evident in the
age distribution of persons
arrested for theft.
Age Distribution of Theft Arrests in 2000
35%
30%
30%
25%
Persons between the ages of
15 and 24 represented the
highest share of persons
arrested for theft in 2000.
20%
15%
15%
12%
9%
10%
9%
9%
7%
4%
5%
2%
1%
1%
0%
0%
Under 10 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65 plus
1%
Source: FBI Uniform Crime Reports and Retail Forward Inc.
15
The Threat from Ages 15-24
The potential threat represented by the population of persons age 15 to 24 is
growing.
In recent years the numbers in this group have begun to grow as the Echo
Boom, the children of the Baby Boom, reach their teen years.
As a result, this growing
youth population
represents another factor
that should increase
inventory shrinkage from
shoplifting in the coming
years.
Population Age 15 to 24
45,000
40,000
35,000
30,000
25,000
20,000
15,000
10,000
5,000
1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002
Source: U.S. Bureau of Census and Retail Forward Inc.
16