Chapter 12
Managing Merchandise
Assortments
McGraw-Hill/Irwin
Retailing Management, 7/e
© 2008 by The McGraw-Hill Companies, All rights reserved.
12-2
Merchandise Management
Merchandise
Planning
System
Chapter 13
Buying
Merchandise
Chapter 14
Managing
Merchandise
Assortments
Chapter 12
Retail
Pricing
Chapter 15
Retail
Communication
Mix
Chapter 16
12-3
Questions
■ How is the merchandise management process
organized?
■ Why do the merchandise management processes differ
for staple and fashion merchandise?
■ How do retailers evaluate the quality of their
merchandise management decisions?
■ How do retailers forecast sales for merchandise
classifications?
■ How do retailers plan their assortments and determine
the appropriate inventory levels?
■ What trade-offs must buyers make in developing
merchandise assortments?
12-4
Merchandise Management
Process by which a retailer offers the correct
quantity of the right merchandise in the right
place at the right time and meets the company’s
financial goals.
■ Sense market trends
■ Analyze sales data
■ Make appropriate adjustments in
prices and inventory levels
c) image100/PunchStock
12-5
Merchandise Management and
Investment Portfolio Management
■ Dollars to invest in inventory
■ Invest in “hot” merchandise
■ Save a little for opportunities
(open to buy)
■ Monitor portfolio of
merchandise (stocks)
■ Sell losers (markdowns)
Traders on the stock exchange floor
manage a portfolio of stocks, and retail
buyers manage a portfolio of merchandise
inventory. Both continuously assess the risks
associated with their purchase decisions.
12-6
Buying Organization
Merchandise Group
Department
Classification
Category
SKU
Each merchandise group is managed by
a general merchandise manager (GMM),
senior VP
Departments are managed by
a divisional merchandise manager (DMM),
A group of items targeting the same
customer type, such as girls’ sizes 4-6
Each buyer manages several merchandise
categories (e.g., sportswear, dresses,
swimwear, outerwear categories for girls’
sizes 4-6
The smallest unit available for inventory control
Size, color, style
12-7
The Buying Organization
Ryan McVay/Getty Images
Merchandise Group…………Men’s wear
Department………….……….Young Men’s wear
Classification………….……..Pants
Category……………………..Jeans
Sock Keeping Unit (SKU)…..Levi, 501, size 26
waist, 32 inseam
12-8
Merchandise Classifications
and Organization
12-9
Merchandise Category –
The Planning Unit
A merchandise category is an assortment of items that
customers see as substitutes for each other.
Vendors might assign products to different categories
based on differences in product attributes
Retailers might assign two products to the same
category based upon common consumers and buying
behavior
12-10
Category Management
■ The process of managing a retail business with the
objective of maximizing the sales and profits of a category
■ Objective is to maximize the sales and profits of the entire
category, not just a particular brand
Breakfast cereal category vs. Kellogg Corn
Flakes
Men’s knitted shirts vs. Polo shirts
Diary product category vs. Carnation milk
products
12-11
Category Captain
Selected vendor responsible for managing a category
Vendors frequently have more information and analytical skills
about the category in which they compete than retailers
■ Helps retailer understand consumer behavior
■ Creates assortments that satisfy the customer
■ Improves profitability of category
Problems
■ Vendor category captain may have different goals than
retailer
12-12
Antitrust Considerations



The vendor category captain could
collude with retailer to fix prices
It could block brands from access to shelf
space
Category captains need to temper zeal
for control over retailers
■ Actions to avoid antitrust problems



Divulge all information obtained from the
retailer to the other brands in the category
Appoint another large brand as a
“category advisor”
To circumvent potential collusion in price
setting, refuse to serve as captain for two
retailers in the same market
Stockbyte/Punchstock Images
12-13
Evaluating Merchandise Management
Performance - GMROI
Merchandise managers have control over
■ The merchandise they buy
■ The price at which the merchandise is sold
■ The cost of the merchandise
Merchandise managers do not have control over
■ Operating expenses
■ Human resources
■ Real estate
■ Supply chain management
■ Information systems
SO HOW ARE MERCHANTS EVALUATED?
12-14
GMROI
Productivity Measures
Inventory
Input
Gross
Margin
Output
A measurement of how many gross margin dollars
are earned on every dollar of inventory
investment made by the buyer
12-15
GMROI
Gross Margin Return on Investment
GMROI = Gross Margin Percent x sales-to-stock ratio
= gross margin
net sales
=
x
net sales
avg inventory at cost
gross margin
avg inventory at cost
Inventory Turnover
= (1 – Gross Margin Percent) x sales-to-stock ratio
12-16
How do buyers influence GMROI?
GMROI = Gross Margin Percent x sales-to-stock ratio
= gross margin x
net sales
net sales
avg. inventory at cost
=
gross margin
avg. inventory at cost
Components that buyers can control:
■ Gross margin component:

Price:
• Prices that buyers set
• Prices that buyers negotiate with vendors
■ Sales-to-stock ratio component:

Popularity of the merchandise buyers buy
12-17
ROI and GMROI
Asset Productivity Measures
Strategic Corporate Level
■ Return on Assets = Net Profit
Total Assets
Merchandise Management Level
■ GMROI
=
Gross Margin
Avg. Inventory at Cost
12-18
Illustration of GMROI
Merchandise categories with different margin/turnover profiles
can be compared and evaluated
Canned food
Fresh Bakery
Canned food
Fresh Bakery
12-19
GMROI for Selected Department
in Discount Stores
12-20
Measuring Sales-to-Stock Ratio
■ Net Sales/Average Inventory at Cost
■ Retailers report on an annual basis
■ If the sales-to-stock ratio for a three-month
season is 2.3, the annual sales-to-stock ratio will
be 9.2
■ Estimation of average inventory


Use information system: averaging the inventory in
stores and distribution centers at the end of each day
Divide the sum of the end-of-month (EOM)
inventories for several months by the number of
months
12-21
Managing Inventory Turnover
Calculation

Inventory turnover =

Inventory turnover =

Average inventory =
Net Sales
Average inventory at retail
Cost of goods sold
Average inventory at cost
Month1 + Month2 + Month 3 +…
Number of months
■ Inventory Turnover helps assess the buyer’s performance in managing
asset (merchandise inventory)
■ But focusing on increasing inventory turnover can actually decrease RMROI
■ Buyers need to consider the trade-offs associated with managing Inventory Turnover
12-22
Inventory Turnover
Month
■ EOM January
■ EOM February
■ EOM March
■ Total Inventory
Retail Value of Inventory
$22,000
33,000
38,000
$93,000
■ Average inventory = $93,000 ÷ 3 = $31,000
12-23
Inventory Turnover and
Stock-to-Sale Ratio
Inventory turnover =
(at retail)
Net Sales
Average inventory at retail
Inventory turnover =
(at cost)
Sock-to-Sales Ratio =
Cost of goods sold
Average inventory at cost
Net Sales
Average cost of
inventory
12-24
Advantages of Rapid Turnover
■
■
■
■
Increased sales volume
Less risk of obsolescence and markdowns
Improved salesperson morale
More resources to take advantage of new
buying opportunities
12-25
Approaches for Improving Inventory Turnover
■ Reduce number of categories
■ Reduce number of SKUs within a category
■ Reduce number of items in a SKU
BUT if a customer can’t find their size or color or
brand, patronage and sales decrease!
another approach…
12-26
…another approach
To improve inventory turnover
■ Buy merchandise more often
■ Buy in smaller quantities which should reduce average
inventory without reducing sales
BUT by buying smaller quantities
■ Buyers can’t take advantage of quantity discounts so
■ Gross margin decreases
■ Operating expenses increase
■ Buyers need to spend more time placing orders and
monitoring deliveries
12-27
Merchandise Planning Process
12-28
Types of Merchandise Management
Planning Processes
Two distinct types of merchandise management systems
for managing
■ Staple (Basic) Merchandise Categories





Continuous demand over an extended time period
Limited number of new product introductions
Hosiery, basic casual apparel
Easy to forecast demand
Continuous replenishment
■ Fashion Merchandise Categories



In demand for a relatively short period of time
Continuous introductions of new products, making existing
products obsolete
Athletic shoes, laptop computers, women’s apparel
■ Discussed in Chapter 13 in detail
12-29
Merchandise Management Process
1. forecasting sales
2. Developing an assortment plan
3. Determining the appropriate inventory level
12-30
Developing a Sales Forecast
■ Understanding the nature of the product life cycle
■ Collecting data on sales of product and comparable
products
■ Using statistical techniques to project sales
■ Work with vendors to coordinate manufacturing and
merchandise delivery with forecasted demand
(CPFR)
12-31
The Category Product Life Cycle
Knowing where a category is in its life cycle is important in developing
a sales forecast and merchandising strategy
12-32
Variations in the Category Life Cycle
12-33
Fad vs. Fashion
How do buyers tell the
difference?
Are Crocks, a Fad or a Fashion?
■ Is it compatible with changes in
consumer lifestyles?
■ Does the innovation provide real
benefits?
■ Is the innovation compatible with
other changes in the marketplace?
■ Who is adopting the trend?
12-34
Types of Merchandise
Fashion Merchandise
Unpredictable Demand
Limited Sales History
Difficult to Forecast Sales
The McGraw-Hill Companies, Inc./Lars A. Niki,
photographer
Staple Merchandise
Predictable Demand
History of Past Sales
Relatively Accurate Forecasts
The McGraw-Hill Companies Inc./Ken Cavanagh Photographer
12-35
Forecasting Staple Merchandise
Based on
extrapolating
historical sales
because sales are
constant from year to
year
12-36
Sales of 12-Inch Lodge Frying Pans
Plot of Sales by Year
Plot of Sales by Quarter
12-37
Forecasting Staple Merchandise
Forecast sales for 2009: Lodge frying pan
■ 2009 annual sales = 1.036(3.6% growth) x
118,963 (2008 annual sales) = 123,245
■ The estimated quarterly sales:




First-quarter sales
= 123,245 x .21 = 25, 881
Second-quarter sales = 123,245 x .26 = 32,044
Third-quarter sales = 123,245 x .18 = 22,184
fourth-quarter sales = 123,245 x .35 = 43,136
12-38
Factors Affecting Sales Projections
Controllable
■ Promotions
■ Store Locations
■ Merchandise Placement
■ Cannibalization
Uncontrollable
■ Seasonality
■ Weather
■ Competitive Activity
■ Product Availability
■ Economic Conditions
12-39
Forecasting Fashion Merchandise
Categories
Retailers develop fashion forecasts by relying on:
■
■
■
■
■
Previous sales data
Personal awareness
Fashion and trend services
Vendors
Traditional market research
12-40
Personal Awareness
How do fashion buyers know the trends?
■
■
■
■
■
Internet chat rooms
Look in closets
Go to the movies
Go to rock concerts
Go to nightclubs
Ryan McVay/Getty Images
SCAN
Shop the retail stores, Web sites and catalogs of competitors as a
customer would
Converse with consumers, sales clerks, and neighbors
Act like your customer
Notice
12-41
Fashion and Trend Services
Buyers subscribe to forecasting services and fashion
publications
■ Trendzine (www.FashionInformation.com)
■ Doneger Creative Services (www.Doneger.com/web )
■ Fashion Snoops (www.fashionsnoops.com)
■ Earnshaw’s
■ Women’s Wear Daily (WWD)
■ DNR
■ Home Furnishings News (HFN)
12-42
Work with Vendors:
Collaboration, Planning, Forecasting, and Replenishment
Systems (CPFR)
■ Vendors have proprietary information about their
marketing plans (e.g., new product launches, special
promotions)
■ Procedures used by retailers and vendors to work
together to insure that the right merchandise is at the
right place at the right time.


Benefits both retailers and vendors
Increases fill rate, reduces stockouts, increases inventory turns
12-43
Developing Assortment Planning
Assortment plan is a list of the SKUs that a retailer will offer
in a merchandise category and reflects the variety and
assortment that the retailer plans to offer in a merchandise
category
Variety (breadth) is the number of different merchandising
categories within a store or department
Assortment (depth) is the number of SKUs within a
category.
Product availability defines the percentage of demand for a
particular SKU that is satisfied.
12-44
Is This Store Heavy on Variety?
On Assortment?
PhotoLink/Getty Images
12-45
Determining Variety and Assortment
PhotoLink/Getty Images
Buyers consider
■ Retail strategy
 The number of SKUs to offer in a merchandise
category is a strategic decision
■ GMROI of the merchandise mix
■ Trade-off between too much versus too little assortment
 Increasing sales by offering more breadth and depth
can potentially reduce inventory turnover and GMROI
by stocking more SKUs
■ Physical characteristics of the store
■ Complementary Merchandise
12-46
Assortment Plan for Girls’ Jeans
12-47
Product Availability
■ The percentage of demand for a particular SKU
that is satisfied
■ Level of support or service level
■ The backup (buffer) stock in the model stock
plan determine product availability
■ The higher product availability, the higher the
amount of backup stock necessary to ensure that
the retailer won’t be out of stock on a particular
SKU when consumers demand it
12-48
Model Stock Plans
12-49
Importance of Backup (Buffer) Stock
Choosing an appropriate
amount of backup stock is
critical to successful
assortment planning
■ If the backup stock is too low 
loose sales and customers
■ If the backup stock is too high 
scare financial resources will be
wasted on needless inventory that
could be more profitably invested in
more variety or assortment
12-50
ABC Analysis
Rank - orders merchandise by some performance
measure determine which items:
■ should never be out of stock
■ should be allowed to be out of stock
occasionally
■ should be deleted from the stock selection
12-51
Product Availability
Factors considered to determine the appropriate
level of buffer stock and thus the product
availability for each SKU
■ ABC Classification of merchandise (inventory)



A – higher product availability
B – medium product availability
C – lower product availability is acceptable
■ Fluctuations in demand
■ Lead time for deliver from the vendor
■ Frequency of store deliveries
Trade-off among variety (breadth), assortment (depth), and product availability
12-52
Understanding the Challenges of
Assortment Planning and Allocation
Adapted with permission from ProfitLogic, now part of
Oracle Retail
12-53
Assortment Planning – A Key to Financial
Success
Right
+
Product
Right
Place
good assortment
strategy
+
Happy
Right
Right
+
= Customer
+
Time
Quantities
good assortment
execution
=
Financial success
12-54
Reality
■ Customers respond to a promotion, only to find
the store is out of stock
■ Customers find a piece of clothing in every
size…but not hers
■ Customers go to a store, only to find the
inventory ‘picked over’
12-55
55
For A Retailer,
These Situations Are Very Costly
“
• Objective of assortment planning system is to
match Inventory to demand
–By quantity
–By size
–By geography
–By store format
• Mismatch Results In Serious Consequences
–Overstocks create markdowns and lost
gross margin dollars
–Under stocks create lost sales and
unhappy customers
A retailer was stuck with $400 million in excess inventory…after
misreading consumer demand for products at the right price point.
”
--Forrester Research
12-56
56
Agenda
■ Why Is This So Hard To Do Well?
■ Symptoms Of Less-Than-Perfect Assortment
Planning
■ How Has Merchandise Optimization Helped?
12-57
Fast Changing Product Lines and Large-Scale
Expansion Have Made It a Lot More Complex
Wall Street
Retail is Detail
1,000 Stores
X
• 27 years old
50,000 SKUs
• Liberal Arts degree
X
• 4 years in
26 Weeks
merchandising X
organization
4 Measures
(Sales, Inventory, Receipts, On Order)
• Motivated by fashion
X
and trends
Plan, Actual, Last Year
X
Main
Street
4 Seasons
One Trillion Numbers
Source: Fortune 50 Retailer
12-58
What’s So Hard About
Executing On The Assortment Strategy?
Key Challenges
1. Build localized assortments
2. Create accurate Initial Allocations
3. Allocate in-season to match store demand
4. Manage Merchandise Effectively Across the Entire Lifecycle—
Be Out-of-stock at the end of the season
Problem: Execution Complexity Exceeds Capacity
Most retailers consider two of the legs: location
and product decisions. Can’t deal with TIME
Location
12-59
What is the consequence of poor execution?
• Cluttered selling floor
• Unclean seasonal
transitions
Location
• Excessive stock-outs and overstocks
• Customers leave without buying
Bottom Line Impact:
Lost sales and lowered margin and depressed
inventory turn
12-60
60
Agenda
■ Why Is This So Hard To Do Well?
■ Symptoms Of Less-Than-Perfect Assortment
Planning
■ How Has Merchandise Optimization Helped?
12-61
Approach
PreSeason
Ability to build localized assortments


InSeason
“Treat every store like it was your only store” and “Never treat
a Store as an Average Store again”
Analytics determines each store’s buy quantities, receipt flows
and initial allocation
Enables more profitable & efficient in-season inventory mgt. and
allocations


In-season updated forecasts track sales against plan—
sophisticated sell-through analysis.
Analytics lead to fewer over- and under-allocations
12-62
62
What’s the value creation opportunity?
■ Financial
9 – 16 % improvement in gross margin $$s
4 – 7 % improvement in sales $$s
■ Merchandising
Store assortments that reflect the vision of the
assortment strategy
Cleaner seasonal transitions and fresher merchandise in
the stores
Value to a $650M specialty retailer is $60M
incremental profit
12-63
How Does it Work?
1. Identify demand drivers
•
price sensitivities and seasonalities
2. Understand sales potential with Optimized History
•
optimal inventory levels and pricing schedules
3. Convert sales demand to receipt quantities by size and
pack
•
optimal size profiles, prepacks, and receipt quantities
12-64
Seasonality by Regions
•
•
Comparing regions: Capri peaks in late-June in the Midwest and in mid-July in the west
Could be due to seasonality differences and/or different sensitivities to a price change.
8
7
6
5
4
3
2
1
0
Capri - Atlantic
Pants - Atlantic
Capri – Midwest
Pants – Mid west
Capri - West
Pants - West
12-65
Price Elasticity by Divisions
Different product divisions respond differently to price cuts. With the Division 4 business being
the most impacted by price while the Division 2’s business is still affected, but not as much as
the others.
Price Elasticity Chart
700%
Sales Lift
600%
Division 1
Division 2
Division 3
Division 4
500%
400%
40% OFF
300%
200%
100%
0%
5%
10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60%
Price Discount
12-66
Underbought - Cotton T-Neck Sweater
Sales: +35% Gross Margin: +37%
12-67
Underbought - Cotton T-Neck Sweater
An increased buy quantity across all sizes would lead to a large increase in
sales units, sales dollars and gross dollars.
Sales Units
Sales Dollars
Total Buy Quantity
Gross Margin Dollars
Gross Margin $
Actual
Optimized
Results
Results
Variance
#
%
22,114
30,039
7,925
35.8%
$549,154
$746,780
$197,626
36.0%
22,176
30,039
7,863
35.5%
$309,416
$424,110
$114,695
37.1%
56.3%
56.8%
12-68
Overbought -- Merino Cardigan
Sales: +10% Gross Margin: +58%
Optimized vs. Actual Sales
250
$40.00
$35.00
200
Units
150
$25.00
$20.00
100
$15.00
Selling Price
$30.00
Unprofitable Sales
from Deep
Markdown Activity
$10.00
50
$5.00
0
/1
09
03
3/
$0.00
3
4
4
3
4
4
4
4
4
03
03
03
03
03
/
/
/0
/0
/0
/
/0
/0
/
/0
/0
/0
/
/0
2
7
5
6
7
8
7
8
0
3
4
1
1
3
/2
/2
/0
/1
/2
/2
/2
/0
/1
/1
/0
/2
/3
/1
09
10
10
11
11
12
12
01
01
01
02
02
03
03
Inventory / 5
Actual Selling Price
Actual Sales Units
Optimized Selling Price
Optimized Sales Units
12-69
Overbought -- Merino Cardigan
A reduced buy quantity coupled with an accurate flow would have led to a
much higher quality of sales, reduced markdowns and an increased gross
margin percentage more in line with other products.
Sales Units
Sales $
Total Buy
GM$
GM%
Actual
Optimized
Results
Results
1,265
1,037
$33,391
$37,022
1,277
1,037
$12,393
$19,809
37.1%
53.5%
Variance
#
%
(228)
$3,631
(240)
$7,416
(18.0%)
10.9%
(18.8%)
59.8%
12-70
How to Allocate Optimal Sizes
Example: Ribbed Black Turtleneck
40.00
Actual Size Profile
Optimized Size Profile
35.00
30.00
Percent
25.00
Shortage of
Sizes Needed
by Customers
20.00
15.00
10.00
5.00
0.00
Average of XS
Average of S
Average of M
Average of L
Average of XL
12-71
Size Profiling Analysis:
Shortages were consistent across regions
40.00
40.00
Atlantic
30.00
30.00
25.00
25.00
20.00
20.00
15.00
15.00
10.00
10.00
5.00
5.00
0.00
0.00
Average of XS
Average of S
Average of M
Average of L
Average of XL
Average of XS
Average of S
Average of M
Average of L
Average of XL
40.00
40.00
Mid West
35.00
30.00
30.00
25.00
25.00
20.00
West
35.00
Percent
Percent
South
35.00
Percent
Percent
35.00
20.00
15.00
15.00
10.00
10.00
5.00
5.00
0.00
0.00
Average of XS
Average of S
Average of M
Average of L
Average of XL
Average of XS
Average of S
Average of M
Average of L
Average of XL
12-72
Pre-pack Analysis
Example: Ribbed Black Turtleneck
Changes to Pre-Packs Leads to
Better Store Demand Matching
Existing Pre-packs
• (S, M, L) with scale (2,2,1)
• (S, M, L) with scale (2,3,2)
Optimized Two Pre-Packs
• (XS, S, M, L, XL) with scale (2,2,3,2,2)
• (XS, S, M, L, XL) with scale (1,3,3,2,1)
12-73
Buy Quantity Optimization
Apparel Retailer Drives Assortment Productivity
12-74
How to Optimize Buy Quantity by Store
Apparel Retailer Drives Assortment Productivity
The Problem: Imprecise buy quantities by item are resulting in lost sales for
some items and greater markdowns for other items
Driving the Need for Change: “Our planning process heavily relies on averages. We conduct classlevel analysis to create an average per store buy quantity for all items in the class. We know we ’re leaving
money on the table because of our customers’ brand-driven purchasing habits. Today we have
insufficient tools and systems to do it any other way. We use Excel for analysis and have another
system that serves as the place of record for the decisions.” – VP of Planning
Store
Store 1
Actual
Over /
Store
Ordered & Under
Demand
Shipped Stocked
581
450
Averages Lead to Over Stocks, Under
Stocks and Missed Opportunity
-131
Store 2
493
450
-43
Store 3
471
450
-21
Store 4
404
450
46
Store 5
391
450
59
Store 6
358
450
92
Avg.
450
450

Optimal
Actual
Missed
Opportunity
Sales
Dollars
$94,187
$90,348
4.2%
GM
Dollars
$60,872
$56,922
6.9%
Under
Stocked
Over
Stocked
12-75
Determining Buy Quantities
The Challenge of Item Planning is Understanding the Components of Sales
Example Item, Historical Sales
Unit Sales
Actual Sales
1,400
1,200
1,000
800
600
400
200
9/
5
9/
12
9/
19
9/
26
10
/3
10
/1
0
10
/1
7
10
/2
4
10
/3
1
11
/7
11
/1
4
11
/2
1
8/
29
0
Weeks
12-76
Determining Buy Quantities
The Solution: ProfitLogic Buy Quantity Optimization
Understanding the Demand Drivers
Unit Sales
Promotion
Seasonality
1,400
Natural Demand
1,200
1,000
800
600
400
200
9/
5
9/
12
9/
19
9/
26
10
/3
10
/1
0
10
/1
7
10
/2
4
10
/3
1
11
/7
11
/1
4
11
/2
1
8/
29
0
Weeks
12-77
Determining Buy Quantities
The Solution: ProfitLogic Buy Quantity Optimization
Underlying Item Demand
Unit Sales
1,400
1,200
Natural Demand
Natural Demand,
Adjusted for Trend
1,000
800
600
400
200
9/
5
9/
12
9/
19
9/
26
10
/3
10
/1
0
10
/1
7
10
/2
4
10
/3
1
11
/7
11
/1
4
11
/2
1
8/
29
0
Weeks
12-78
Determining Buy Quantities
The Solution: ProfitLogic Buy Quantity Optimization
Underlying Demand Plus Seasonality
Unit Sales
Seasonality
1,400
Natural Demand
1,200
1,000
800
600
400
200
9/
5
9/
12
9/
19
9/
26
10
/3
10
/1
0
10
/1
7
10
/2
4
10
/3
1
11
/7
11
/1
4
11
/2
1
8/
29
0
Weeks
12-79
Determining Buy Quantities
The Solution: ProfitLogic Buy Quantity Optimization
Factoring in the Promotion
Unit Sales
1,400
1,200
Promotion
Seasonality
Natural Demand
1,000
800
600
400
200
9/
5
9/
12
9/
19
9/
26
10
/3
10
/1
0
10
/1
7
10
/2
4
10
/3
1
11
/7
11
/1
4
11
/2
1
8/
29
0
Weeks
12-80
Determining Buy Quantities
Results for Example Item
Unit Sales
Actual Sales
1,400
1,200
1,000
800
600
400
200
9/
5
9/
12
9/
19
9/
26
10
/3
10
/1
0
10
/1
7
10
/2
4
10
/3
1
11
/7
11
/1
4
11
/2
1
8/
29
0
Weeks
12-81
Determining Buy Quantities
Results for Example Item
Unit Sales
Merchandise
Optimization
Actual Sales
1,400
1,200
1,000
800
600
400
200
9/
5
9/
12
9/
19
9/
26
10
/3
10
/1
0
10
/1
7
10
/2
4
10
/3
1
11
/7
11
/1
4
11
/2
1
8/
29
0
Weeks
12-82
Determining Buy Quantities
Results for Example Item
Unit Sales
1,400
Merchandise
Optimization
Actual Sales
1,200
Traditional,
Manual
Methods
1,000
800
600
400
200
9/
5
9/
12
9/
19
9/
26
10
/3
10
/1
0
10
/1
7
10
/2
4
10
/3
1
11
/7
11
/1
4
11
/2
1
8/
29
0
Weeks
12-83
Determining Buy Quantities
Merchandise Optimization vs. Traditional Methods
Merchandise Optimization
200,000
Traditional Methods
Merchandise Optimization Benefit
4.2% improvement in Sales $
6.2% improvement in Gross Margin $
150,000
100,000
Sales $$
GM $
Business Impact:
Improved Customer Satisfaction:
Improves product availability to support the customer’s needs
Increased Financial Performance:
Drives sales and gross margin through greater full price sales
Enhanced Merchandising Process: Provides buyers a greater understanding of the drivers of the business
12-84
Receipt Flow Optimization
Dept. Store Drives Inventory Turn with Demand Driven
Receipt Flow
12-85
Determining Receipt Flow
The Problem: Retailer is frontloading receipts in the beginning of the season,
driving down inventory turnover
Each Store’s Customer Demand
Operational Considerations
 How many deliveries from the supplier
can I or should I receive?
 What’s the planned date to have the
item on the selling floor?
 When is the season over?
 How do I ensure I satisfy the size needs
at the store-level (e.g., S-XL, 2-16)
 What business rules need to be met?
- Presentation quantities
- Capacity constraints
- Shipment minimums
- Planned safety stock
12-86
Determining Receipt Flow
The Challenge of flow planning is understanding how demand will unfold
over the season and creating a flow that satisfies both demand and
operational constraints
Single Shipment Inventory Flow
Units
Sales Forecast
Inventory / 5
8,000
7,000
6,000
5,000
4,000
3,000
2,000
1,000
Excess
Inventory
Front loading leads to carrying
excess inventory and a reduced
ability to react in-season
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15
Optimal
Actual
Missed
Opportunity
Inventory
Turnover
4.7
4.4
6.8%
GM
ROI
1.76
1.67
5.3%
Weeks
Multiple Delivery Inventory Flow
Units
6,000
Sales Forecast
Inventory / 5
5,000
4,000
Responsive
Inventory
3,000
2,000
1,000
0
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 Weeks
12-87
Determining Receipt Flow
The Solution: Receipt Flow Optimization
Optimal DC and Store Receipt
Flow based on:
 Pre-season Style-Color/Store
forecast
 Delivery frequencies
 Flow dates
 Safety stock
 Presentation mins and maxs
 Shipment minimums
 Item size runs (e.g., S-XL, 2-16)
Optimal Order Units based on:
 Store level size profiles
 Pre-pack configuration options
 Purchase order total quantities
 Initial and subsequent
allocations
12-88
Optimized Size Profiles
Women’s Apparel Retailer Improves Size Store Allocations
12-89
Optimized Size Profiles
Women’s Apparel Retailer Improves Size Store Allocations
The Problem: Size misallocation was leading to missed sales caused by
stocks-outs and excessive carrying costs due to over-stocking
Driving the need for change: “We’re concerned about a self-fulfilling prophecy –decisions are being
made based on selling history, which is likely to cause us to repeat past mistakes. Customer feedback
indicates that size stock outs at stores are hurting sales and customer satisfaction. We need a quick
hit solution that can address our business -- multiple store formats, thousands of styles per season, 2
seasons per year.” – SVP of Merchandising
Size Profile for Cardigan at Boston store
Client
Size Curve
ChainChain-Wide
Wide Size Profile
Boston Store
Size
ProfitLogic
Store
X Profile
Size Curve
45%
40%
M
35%
30%
S
25%
L
20%
Large cardigan was under stocked
by 55% at one store because of
chain-wide size profile
15%
10%
5%
Missed Sales Opportunity:
XS
0%
XS
S
M
L
12-90
Optimized Size Profiles
Women’s Apparel Retailer Improves Size Store Allocations
The Solution: optimized size profiles by class/store, which codifies size
demand variance. The profiles were used within the retailers’
existing planning and allocation systems
Cardigan Sweater
% Optimal
Contribution
Silk Blouse
% Contribution
Store X
Store Y
30
25
Store X
Store Y
25.00%
20.00%
20
15.00%
15
10.00%
10
5.00%
5
-
2
4
6
8
10
12
2
Size
4
6
8
10
12
14
Size
12-91