Clearance Pricing &
Inventory Management for
Retail Chains
Stephen A. Smith
J. C. Penney Professor & Associate Director
The Retail Workbench
Santa Clara University
Dale D. Achabal, Ph.D.
L.J. Skaggs Professor & Director
Retail Management Institute
The Retail Workbench
Founded at Santa Clara University in
1991
Mission
:
To improve decision making in
general merchandise retailing by applying
science to the art of retailing
Corporate Sponsors: Retail Department
and Specialty stores
Faculty in Marketing, Operations and MIS
from SCU and other universities
Drivers of the Retail Industry
in the 21
st
Century
Consumers’ Demand for Greater
Choice
Better
Information Systems
Detailed market info (POS + Web)
Desktop computing power
Merchandise Trends in
Department and Specialty
Stores
More products in the assortment
More fashion merchandise
Shorter seasons
Result: Clearance Markdowns(CMDs)
increase as % of Sales
3 3 %
3 1 %
2 6 %
2 1 %
1 6 %
1 1 %
6 %
Markdowns as
Percentage of D
ollar Sales
Retail Supply Chain Features
Long lead time & just 1 order
for fashion and private label
75% - 100% of merchandise sent
directly to stores for presentation
Buyers’ Management of CMDs
Viewed as mistakes
Hope springs eternal
Seasonal demand evaporates
Must clear at any price
But selling half at 50% off >$
selling all at 80% off
Additional Constraints
for CMDs
Clearance prices must be
non-increasing
Inventory must be revalued to each
new clearance price
(Weekly) Markdown budgets by
merchandise category
Same markdown at all stores (for
simplicity)
Analytical Approach to
CMD Management
Sales Forecasting Model
Clearance Price Optimization at
Store and Item Level
Financial Performance
Measurement
Sales Forecasting Factors
Seasonal variations
end of season drop
Holidays & Store Events
Percent Markdown
Advertising
Remaining On Hand Inventory
Store Presentation
Broken assortments
Forecasting Model for
Weekly Item Sales
Baseline
Seasonal
Mechandising
Sales
x
x
Sales
Effect
Effects
=
Based on Retail Workbench
empirical studies
Merchandising Effects tailored to
each retailer
Example Merchandising Effects
Model
p
= the current percent markdown
A
= feature advertising space in percentage of a page
A
0
=
smallest ad size (typically a line list, which is 10% of a page)
I
= current on hand inventory
I
0
=
base inventory level sometimes called “fixture fill.”
d(k,t) = 0,1
indicators for store events
∏
=
k
t
k
d
k
p
e
I
I
A
A
e
d
I
A
p
M
(
)
(
,
)
0
0
)
,
,
,
(
µ
τ
α
γ
Initial Estimation of
Model Coefficients
Stage 1
Historical Data
Weekly Forecasts &
Adjustment of
Certain Coefficients
Stage 2
New Sales Data
Update Coefficients for:
Base Sales
Forecasting Model Hierarchy
Parameter Type
Department or Class
Seasonal Variations
Items or SubClass
Merchandising Effects,
Base Sales
Store or
Forecast Allocation
Metro Area
Sizes &
Forecast Allocation
Clearance Price Optimization:
Inputs
Forecasted Sales for remainder of
the season
On Hand Inventory at each store
“Out-Date” (End of Season)
Unit Salvage Value of Unsold
Merchandise
Decision Variables
p(t) = markdown price in week t
I
0
= initial inventory level
(may be fixed)
I(t) = remaining inventory in week t
s(t) = s(p(t),y(I(t)),t) = sales in week t
where y(I(t)) = inventory effect on
sales
Optimal Control Problem:
Maximize Gross Margin
value.
salvage
unit
and
cost
linear
piecewise
a
)
(
where
)
(
)
(
)
(
'
subject to
)
(
)
(
)
(
)
(
max
0
0
0
0
0
0
=
=
=
≥
−
=
−
−
+
∫
∫
t
t
e
e
t
t
e
c
I
c
s
dt
t
s
I
t
s
t
I
I
c
s
I
c
dt
t
s
t
p
e
e
Solution Properties
Optimal weekly sales trajectory is
proportional to seasonal effects.
Optimal price trajectory depends on I(t).
Step function approximation works well
.
determines
)
(
'
/
1
.
conditions
boundary
from
come
at time
,
where
,
)
(
(
ln
1
))
(
(
0
0
I
I
c
p
t
y
p
y
t
I
y
p
t
I
P
e
e
e
e
e
e
=
−
+
=
γ
γ
Financial Performance Measures
Revenue Capture Rate =
Revenue Obtained during CMD Cycle
Units at CMD Start
∗
Original Retail Price
Inventory Sell Through =
average % of inventory sold each week
Mid-Size Retailer with over 300 stores:
• Increased Profitability
–
Capture rate increased by 10-15%
– > $15 Million per year revenue increase
• Faster Inventory Conversion = Fresh Assortment
–
Inventory sell through increased by 15 - 20 %
– Shortened Markdown Cycle by 20%
• Significant Labor Savings on “re-pricing”
• Better Markdown Dollar Forecasting
- Forecast E
rror percent cut in half at chain level
Case Study
Spotlight Solutions Results
Recent Pilot Results
Control
Group
Spotlight
Stores
Stores
$GM / $Revenue
44%
48%
% of Inventory Sold
62%
71%
Length of Season
13 wks
11 wks
Optimal Markdowns are
Targeted by Item and Location
0 10 20 30 40 50 60 70 80 90 100
None 25% Off 33% Off 40% Off 50% Off 60% Off
Typical 1st Markdown Decision Optimal Markdown
