For retail businesses, inventory is simultaneously the company’s greatest asset and its largest risk. Competitor pricing, markdowns, and returns all threaten the margins that drive success, and in practice, inventory doesn’t always move according to plan. To win in this highly competitive and rapidly evolving industry, it’s essential to have a flexible toolkit that accurately produces forecasts and intelligently adapts to unplanned inventory dynamics. In this talk, I’ll outline how Nordstrom applies data science and machine learning to build a wholistic view of inventory management from assortment, through stocking with intelligent size runs, and ending with a customer experience that gets the right product to the right customer at the right time.

1. CASE STUDIES IN DATA-
DRIVEN MERCHANDISING
Erin Shellman — Nordstrom Data Science
Data Science Salon, Seattle
October 17, 2019

2. E-COMMERCE IS CHANGING THE LANDSCAPE
➤ Evolving customer expectations pose
challenges to brick-and-mortar
operations. e.g.
➤ Online shoppers expect broad
assortment and inventory availability.
➤ Online shoppers expect fast shipping and
convenience.
➤ Customers can shop directly with
brands.
➤ Customers want to try things on at
home and have frictionless returns.

3. E-COMMERCE IS CHANGING THE LANDSCAPE
➤ Evolving customer expectations pose
challenges to brick-and-mortar
operations. e.g.
➤ Online shoppers expect broad assortment and
inventory availability.
➤ Online shoppers expect fast shipping and
convenience.
➤ Customers can shop directly with
brands.
➤ Customers want to try things on at
home and have frictionless returns.
Inventory Management!!

4. MODERN INVENTORY MANAGEMENT IS MORE THAN EXCEL CAN HANDLE
➤ Many brands and retailers struggle to
adopt modern inventory management.
➤ In 2016 Ralph Lauren fell victim to the
long tail of inventory assortment that
diminished focus on core brands1.
➤ In 2018 H&M and Under Armour
collectively sat on ~$6B in unsold
inventory in part because of too many
product offerings2,3.
➤ Store in-stocks are suffering and driving
customers to shop more online.

5. “There are so many [items of clothing]
that a power plant in Vasteras, the
town where H&M founded its first
store, relies partly on burning
defective products the retailer cannot
sell to create energy.

6. Vendors
Buyers
PO
Fashion Office
Stores
Distribution center
L
M
S
I need 100
units!
Purchase
O
rder
InventoryTrends
Distribution
to size
Distribution
to location
Views, add-to-bags, purchases
NORDACE
Customers
H
istoricalsales

7. Vendors
Buyers
PO
Fashion Office
Stores
Distribution center
L
M
S
I need 100
units!
Purchase
O
rder
Inventory
Trends
Historical sales
Distribution
to size
Distribution
to location
Data Science
H
istoricalsales
Views, add-to-bags, purchases
Customers

8. ASSORTMENT PLANNING
Buying the right mix

9. ASSORTMENT PLANNING THEN
➤ Buying the right product assortment is
all about anticipating emerging trends and
estimating customer demands.
➤ Historically this is an art involving:
➤ Trend forecasting
➤ Trade fairs
➤ Wholesale showrooms and fashion
shows

10. ASSORTMENT PLANNING NOW
➤ Modern assortment planning is
a combination of fashion
knowledge and authority, and
data-driven tools for portfolio
management.
➤ Our strategic partnership with
NuOrder yields rich data that’s
transforming how we select
product.

11. Vendors
Buyers
PO
Fashion Office
Stores
Distribution center
L
M
S
I need 100
units!
Purchase
O
rder
Inventory
Trends
Historical sales
Distribution
to size
Distribution
to location
Data Science
Customers
H
istoricalsales
Views, add-to-bags, purchases
C
atalogs

12. REPURPOSING PERSONALIZATION FOR INVENTORY ASSORTMENT
➤ Cross-catalog search
➤ Identify multiple price points
for similar styles.
➤ Measure duplicative inventory.
➤ Performance estimation
➤ Forecast sales performance
prior to commitment to buy.
➤ Use segmentation to estimate
addressable audience size for
product.
< $150
$150 - 200
> $ 200

13. CASE STUDY: IDENTIFYING DUPLICATIVE INVENTORY
➤ Looks gives customers styling ideas
and inspiration on product detail
pages.
➤ Looks substitution uses a
combination of text and visual
features to swap in similar
product.
➤ We applied the algorithm on
historical inventory to support
quick adjustments made by our
merchandisers.

15. Vendors
Buyers
PO
Fashion Office
Stores
Distribution center
L
M
S
I need 100
units!
Purchase
O
rder
Inventory
Trends
Historical sales
Distribution
to size
Distribution
to location
Data Science
Customers
H
istoricalsales
Views, add-to-bags, purchases
C
atalogs
Assortment
Planning

16. UNIT QUANTIFICATION
Finding the in-stock sweet spot

17. UNIT QUANTIFICATION IS HARD TO GET RIGHT
➤ Operating both a large brick-and-
mortar and online business adds
enormous complexity to inventory
management.
➤ Getting it right requires:
➤ Knowing the intended product lifecycle
➤ Forecasting demand at many levels of
granularity e.g. channel and location
➤ Supply chain innovation helps too!
➤ “This approach allows us to provide our stores
with an initial allocation of inventory and
then to dynamically reallocate in-season to
stores needing it most, reducing markdown
risk, out-of-stocks and helping to drive
fashion and newness"

18. InventorySalesvelocity
CASE STUDY: FORECAST-DRIVEN BUY PLANNING
➤ From a labor-intensive, Excel / VBA-based workflow to one that is guided by forecasts.
➤ Our approach allows us to blend in-store and digital signals as well as product features.
➤ We’re controlling change management complexity by delivering outputs in identical
formats, allowing us to decouple the methods from the delivery path.

19. Vendors
Buyers
PO
Fashion Office
Stores
Distribution center
L
M
S
I need 100
units!
Purchase
O
rder
Inventory
Trends
Historical sales
Distribution
to size
Distribution
to location
Quantification
Assortment
Planning
Data Science
Customers
H
istoricalsales
Views, add-to-bags, purchases

20. PERSONALIZATION
Moving high risk inventory

21. PERSONALIZATION IS A LAST-MILE TOOL TO MOVE INVENTORY
➤ Products don’t always move as
planned!
➤ Broken inventory is a normal
part of the product lifecycle.
➤ Personalization usually centers
on customer experience, but
can be used to move broken
inventory before it becomes a
markdown.

22. CASE STUDY: SIZE AWARE SEARCH AND BROWSE
➤ We found that top ranked
items in search and browse
pages frequently contained
‘broken’ products.
➤ Now making adjustments to
allow product rank to decay as
they become broken.
➤ Offset markdown risk by
boosting broken products
when we’re confident in
customers’ sizes.
1 2 3 4
Brokenness
Time

23. Vendors
Buyers
PO
Fashion Office
Stores
Distribution center
L
M
S
I need 100
units!
Purchase
O
rder
Inventory
Trends
Historical sales
Distribution
to size
Distribution
to location
Quantification
Personalization
Assortment
Planning
Data Science
Customers
H
istoricalsales
Views, add-to-bags, purchases

24. CLOSING TIPS FOR DRIVING MASSIVE CHANGE
➤ It’s a big ecosystem, but focus on a small, self-contained
piece.
➤ Tackle one change at a time, i.e. you don’t need to change the
methods and the process at once.
➤ Look for opportunities for data and algorithm reuse, e.g.
purchase ordering management solution as a product data
source, or digital algorithms to characterize product.
➤ Learn how your business works by building relationships with
your stakeholders.

25. THANK YOU!

26. REFERENCES
➤ What Are The Challenges Facing Ralph Lauren? https://
www.forbes.com/sites/greatspeculations/2016/06/14/what-are-
the-challenges-facing-ralph-lauren
➤ H&M, a Fashion Giant, Has a Problem: $4.3 Billion in Unsold
Clothes. https://www.nytimes.com/2018/03/27/business/hm-
clothes-stock-sales.html
➤ A massive shift in American fashion is causing a $1.3 billion
problem for Under Armour. https://www.businessinsider.com/
under-armour-inventory-problem-2018-7