Prof. David Simchi Levi, Engineering Systems Professor at MIT and Chairman of OPS Rules spoke at Bristlecone Pulse 2017 about delivering customer value through digitization, analytics and automation.

1. Big Data & Analytics to Improve Supply
Chain and Business Performance
David Simchi-Levi
E-mail: dslevi@mit.edu

2. Three Recent Trends
2
DifficultyValue
Diagnostic
Descriptive
Predictive
Prescriptive
What happened?
Reports, Alerts, Mapping
Why did it happen?
Queries, Data mining,Statistical Analysis
What will happen?
Forecasts, Machine Learning
How to make it happen?
Optimization

3. 3
A Few Stories …
• Forecasting & Optimization
 Rue La La, B2W: Pricing
 Footwear manufacturing Company: NPI
• Conclusions

4. Online Fashion Sample Sales
Industry
• Offers extremely limited-time discounts (“flash sales”) on
designer apparel & accessories
• Emerged in mid-2000s; ~$4B industry with ~17% annual
growth in last 5 years (US)
Page 4

5. Page 5
Snapshot of Rue La La’s Website

6. “Style”
Page 6

7. “SKU”
Page 7

8. Flash Sales Operations
Merchants
purchase items
from designers
Designers
ship items to
warehouse*
Merchants decide
when to sell items
(create “event”)
During event,
customers
purchase items
Sell out
of item?
End
*Sometimes designer will hold inventory
Yes
No
Focus on first time
this happens

9. 0%
10%
20%
30%
40%
50%
60%
70%
0%‐25% 25%‐50% 50%‐75% 75%‐100% SOLD OUT
(100%)
% of Items
% Inventory Sold (Sell‐Through)
Department 1
Department 2
Department 3
Department 4
Department 5
Sell-Through Distribution
of New Products
Page 9
suggests price
may be too low
suggests price
may be too high

10. Page 10
Approach
Goal: Maximize expected revenue from new products
Demand Forecasting
Challenges:
 Predicting demand for
items that have never
been sold before
 Estimating lost sales
Techniques:
 Clustering
 Machine learning models
for regression
Price Optimization
Challenges:
 Structure of demand forecast
 Demand of each style is
dependent on price of
competing styles 
exponential # variables
Techniques:
 Novel reformulation of price
optimization problem
 Creation of efficient algorithm
to solve daily

11. Page 11
Forecasting Model:
Explanatory Variables Included
• Tested several machine learning techniques
– Regression trees performed best

12. Regression Trees:
Illustration and Intuition
Page 12
If condition is true, move left;
otherwise, move right
Demand
prediction
Why regression trees?
– Use features to partition styles sold in past, and
only use relevant styles to predict demand
– Allow for non-monotonic price/demand relationship

13. Page 13
Approach
Goal: Maximize expected revenue from new products
Demand Forecasting
Challenges:
 Predicting demand for
items that have never
been sold before
 Estimating lost sales
Techniques:
 Clustering
 Machine learning models
for regression
Price Optimization
Challenges:
 Structure of demand forecast
 Demand of each style is
dependent on price of
competing styles 
exponential # variables
Techniques:
 Novel reformulation of price
optimization problem
 Creation of efficient algorithm
to solve daily

14. Pricing Decision Support Tool
ETL
Process
Optimization
Input
Optimal Price
Recommenda‐
tions
Rue La La
Database
Reports and
Visualization
Ad hoc Reports
Query / Drill
Down Visualizer
Standard Reports
Optimizer
Database
Optimizer
Database
Rue La La Enterprise Resource Planning System
Products
Trans‐
actions
Event
Planning
Inventory‐
Constrained
Demand
Prediction
Regression
Tree
Prediction
(Rscript)
Impending
Event Data
R
Predictions
Inventory
Information
Retail Price Optimizer
LP Bound
Algorithm
LP_Solve API‐based Optimizer
Statistics
Tool ‐ R

15. Field Experiment
• Goals
– Would implementing the tool’s recommended price increases
cause a decrease in demand?
– What impact would the price increases have on revenue?
• Identified ~6,000 styles where tool recommended price increase
– Divided styles into 5 categories based on price point; for each
category…
• Raised prices on ~half of styles (treatment group)
• Did not raise prices on other ~half of styles (control group)
Page 15

16. Sell-Through Analysis
Page 16
styles with lowest
price point
styles with highest
price point

17. Revenue Impact
Page 17

18. B2W Overview
• Largest online retailer in Latin America
• Its purpose is to connect people, business,
products and services in a digital platform
• Launched in 1999 (Americanas.com) as an
extension to the physical store business
• Fastest growing Marketplace operation in the
world (+153% 2016 vs 2015)
• Competitors include Amazon, Walmart, Ponto
Frio, Extra
Page 18

19. Dynamic Pricing Approach
Page 19
Demand Curve Generation
Extract sensitivity of quantity sold
with change in price
Challenge:
Historical data has multiple
factors that influence quantity
sold
Method:
Random Forest (Bagging)
Learning via Product Clustering
productsIdentify clusters of products
having similar characteristics
Challenge:
Wide range of characteristics in
which products differ
Method:
K-Means
Optimization
Maximize revenue (margin) of a
cluster subject to a min margin
(revenue) constraint
Challenge:
Solving efficiently
Method:
Mixed Integer Linear
Programming

20. Implemented Solution Provides Real
Time Decision Support to the
Commercial Team
Page 20

21. Dynamic Pricing--Operations
• Algorithm now runs twice a day;
• Typically, the first few hours of the day generate the traffic
prediction, which is an input to the Random Forest;
• No manual intervention. All prices are pushed directly to the
web-site.
Page 21

22. Cluster 1: Low Price Products
Page 22

23. Cluster 2: Fast Selling Products
Page 23

24. Cluster 3: Premium Products
Page 24

25. Cluster 3: Premium Products-
with Margin Optimization
Page 25

26. Competitive Benefits: Dynamic
Pricing is Also a Powerful Tool
for Market Positioning
Page 26
Unique SKUs Sold Daily in Control vs. Treatment GroupsUnique SKUs Sold Daily in Control vs. Treatment Groups
Model sold 40% more unique SKUs everyday (chart below). Will help in positioning
B2W as ‘better price every time, for everything’
Model sold 40% more unique SKUs everyday (chart below). Will help in positioning
B2W as ‘better price every time, for everything’

27. 27
A Few Stories …
• Forecasting & Optimization
 Rue La La, B2W: Pricing
 Footwear manufacturing Company: NPI
• Conclusions

28. Extension to New Product Introduction
• Large footwear manufacturer
• Seasonal products: short lifecycle and low demand accuracy
• Before: Retailers ordered and returned unsold products or
require expedite shipments
• After: production and allocation is done by ML & Optimization
– Historical sales data available
– Goal: Predict demand for new products twelve months
prior to product in market
– Scope: about 2000 different SKUs
Page 28

29. Clustering
D
t
D
t
D
t
D
t
D
t
D
t
D
t
D
tD
t
D
t
D
t
D
t
D
t
D
t
D
t
D
t
Feature 1
Feature 2
Feature 1?
Feature 2?
p ; q ; m
D(t) = pm + (q‐p)Y(t) – q/m (Y(t))²
 Price
 Gender
 Material
 Color
 Cut
 Account
 Deviation from classic
 Period
New product

30. Classification
Account 1, 2, 3, 4, 8, 10, 22, 24
yes no
PRICE < 30$
yes no
YOUTH
yes no
JANUARY‐APRIL
SCORE < 2.5
yes no
yes no
Account 20
Gender: Unisex
Material: Textile
Price: 35$
First Sales: November
Score: 2
Color: Red
New product

31. Classification
Account4, 8, 9, 10, 12, 18
WOMAN
GREEN,ORANGE,
VIOLET,YELLOW
yes no
yes no
yes no
cluster 5
Account 20
Gender: Man
Material: Textile
Price: 35$
First Sales: November
Score: 2
Color: Red
New product

32. Total Sales forecast accuracy
| |
Error
F: forecasted total market
A: actual total market
i: product
prod
error

33. Implementation Results
76 Account/Product Combinations
∑ ⁺
∑
= 18%
Retailer Color Cut Gender Material Price Period
Account 1 971MID UNISEX TEXTILE 30JAN‐APR
Account 2 10MID UNISEX CANVAS 30JAN‐APR
Account 3 134OX UNISEX CANVAS 41JAN‐APR
Account 4 654OX UNISEX CANVAS 28.5MAY‐AUG
Account 5 654OX MAN CANVAS 28.5MAY‐AUG
Account 6 159OX MAN COTTON 28.5MAY‐AUG
Account 7 607HI MAN COTTON 30MAY‐AUG
Account 8 10OX MAN COTTON 28.5SEPT‐DEC
Account 9 1MID MAN COTTON 30SEPT‐DEC
Account 10 410MID WOMANCANVAS 30SEPT‐DEC
Account 11 410MID WOMANCANVAS 30JAN‐APR
Target 35000
ORDERS
1349
1349
1349
7043
1940
1940
1349
2276
3754
3754
895
35000
ACTUAL
SALES
2303
1378
557
7100
581
1724
2542
2045
1484
1048
641
(A‐O)⁺
954
29
0
57
0
0
1193
0
0
0
0
Lost sales: Lost sales benchmark: 36%

34. 34
A Few Stories …
• Forecasting & Optimization
 Rue La La, B2W: Pricing
 Footwear manufacturing Company: NPI
• Conclusions

35. Summary
• New Challenges from emerging manufacturing and
retail business
 High demand uncertainty, short product life cycle and
Multiple Channels
• Combine Machine Learning and Optimization
techniques to impact bottom line
• Our proposed methods are supported by theory,
simulation results and Practice
35