This document discusses product assortment optimization and how to determine which products a business should stock. It defines product assortment as the collection of goods and services provided to consumers. Key factors in assortment include length, breadth, depth and consistency. The document outlines reasons for assortment optimization and describes using sales data to estimate demand for attributes and forecast demand for new products. It provides a case study of a tire company and how their assortment could be optimized using this approach. The document also discusses localizing assortments for different store clusters.

1. Which Products
should you Stock?
-Presented by
Prashant Kumar Ojha
Ashish Arora
Shalender

2. Factors:
Who are our target customers?
What are their needs?
Which of their needs can we satisfy?
Store size and its characteristics
What are our competitors doing?
A different tinge

3. The Problem…
How to decide which
product to stock?

4. Solution
Deciding which product to
stock can be done by
Product Assortment
Optimization.

5. Product
Assortment???

6. Product assortment is a
collection of goods or
services that a business
provides to its
consumers.

7. Characteristics of Product
Assortment
Length or number of Products.
Its breadth or number of product lines.
Its depth or number of product varieties
within a product line
Consistency

8. Length: This refers to the number of products in a
particular product chain or line. For example, our
automobile manufacturer may have four models of
sedans, three models of SUVs, and two models of
trucks in each respective product line.
Breadth: The breadth of a company's products relates
to the number of product lines a company produces or a
retailer carries. An automobile manufacturer, for
example, may have a line of sedans, a line of SUVs,
and a line of trucks.

9. Depth: A product line's depth relates to the different
versions of the same product that may exist in each
product line. Our auto manufacturer may have a
basic, standard, and luxury version for each of its
sedans and SUVs but only a basic and standard
model for its trucks.
Consistency: Consistency is the degree to which
the product lines relate to each other. In our auto
manufacturer example, the sedans and SUVs may
be fairly consistent to each other because they are
consumer vehicles, while the trucks may not be
consistent with them because they are commercial
grade vehicles.

10. Why do we need
Assortment
Optimization?

11. Following a survey in which customers said they’d like
less cluttered stores, Walmart introduced Project Impact, in
2008, removing 15% of the SKUs it carried. Sales declined
significantly, and it was forced to roll back most of the
changes.
Super Fresh, owned by the grocery retailer A&P, stopped
carrying many of its low-selling dry grocery items to allow
for an expansion of fresh offerings. But the eliminated
products turned out to be essential to customers; when they
couldn’t find them, they took their business elsewhere, and
the retailer entered bankruptcy.

12. When the new CEO of a tire retailer shifted its
assortment from low-priced tires to more
expensive ones, he learned the hard way that
price mattered to his customers. The CEO was
replaced after two years, and his successor
restored most of the products that had been
eliminated.

13. Following these results, Its easy to
spot problem or discrepancy in
assortment by seeing the sales data
But…

14. The Difficult part…
What slow sellers should be replaced with.
Slow seller might be an important product to some
customer thus hampering customer trust.
Lack of software’s ability to forecast demand for new
products or to estimate how much demand would transfer
to other products if a slow seller were dropped.
Risks associated with product assortment decision.

15. Solution...

16. Observation shows that most of the time
customers don’t buy products; they buy a
bundle of attributes.
The approach discussed here uses sales of
existing products to estimate the demand for
their various attributes and then uses those
estimates to forecast the demand for potential
new products

17. What are attributes?

18. Case Study – Tires Company
Sales
Brand
Nationally
advertised
House
brands
Mileage
Low Mid High
Size

19. Case Study – Tires Company
With Brand and mileage – 12 combinations possible
6 feasible – National High, National Medium, House 1
High, House 2 High, House 2 Medium, House 3 Low
64 sizes for each tire
64*6 = 384 possible SKUs
But the company, on an average had 105 SKUs in stores

20. Case Study – Tires Company
Leftout SKUs - 2 possibilities arise:
1. Customers will not substitute one for another – unhappy
2. Custumors will substitute with a level up or a level down

21. Analysis
Sales data of recent period – 6 months
Forecast demand for new (potential) SKUs
Refining Forecast
Account for trading up and down
Looking for self-fulfilling prophesies

22. Sales Data

23. Forecast demand for new SKUs

24. Refining Forecast demand

25. Account for Trading Up and Down
3 substitution parameters:
1. One level up
2. One level down
3. Shift from House 3 low to House 2 medium

26. Account for Trading Up and Down
Results:
1. 35% of customers who didn’t find House 3 Low in
the assortment in their size would trade up to House
2 Medium
2. For other levels, 2% would trade up and 1% would
trade down

27. Looking for self fulfilling prophesies

28. Optimizing The Assortment
How to use this model to decide which existing and new
SKUs would constitute an optimal assortment

29. Optimizing The Assortment
Decide whether to maximize revenues or profits
Decide on pricing of potential SKUs
Decide on the final assortments

30. Optimizing The Assortment
 Graphs like this can be used to change the shelf space allotted to categories in
order to increase sales.

31. Localizing The Assortment
How demand differs between stores and creating
assortments that cater to store specific needs
But its too complicated to carry a unique assortment for
each store
To simplify the process, clusters of stores are created
which use the same assortment

32. Localizing The Assortment
How many clusters to create
On what basis cluster should be formed (income, weather
etc.)
What assortment to use in each cluster

33. THANK YOU!

34. References:
https://hbr.org/2012/11/which-products-should-you-stock