Performing fine-grained forecasts on day-store-SKU is beyond the ability of legacy, data warehousing based forecasting tools. Demand for products varies by product, store and day, and yet traditional demand forecasting solutions perform their forecasts at the aggregate market, week and promo group levels. With the introduction of the Databricks Unified Data Analytics Platform, retailers are able to see double-digit improvements in their forecast accuracy. They can perform fine-grained forecasts at the SKU, store and day as well as include hundreds of additional features to improve the accuracy of models. They can further enhance their forecasts with localization and the easy inclusion of additional data sets. And they’re running these forecasts daily, providing their planners and retail operations team with timely data for better execution. In this webinar, we reviewed: How to perform fine-grained demand forecasts on a day/store/SKU level with Databricks How to forecast time series data precisely using Facebook’s Prophet Also, how Starbucks does custom forecasting with relative ease How to train a large number of models using the defacto distributed data processing engine, Apache Spark™ Finally, we then presented this data to analysts and managers using BI tools to enable the decision making required to drive the required business outcomes

1. How Starbucks Forecasts Demand
at Scale with Facebook Prophet
and Databricks
Rob Saker, Retail Industry Leader at Databricks
Brendan O’Shaughnessy, Data Science Manager at Starbucks
Bilal Obeidat, Solutions Architect, Databricks

2. Housekeeping
• Your connection will be muted
• Submit questions via the Q&A panel
• Questions will be answered at the end of the webinar
• Any outstanding questions will be answered in the Databricks Forum
(https://forums.databricks.com)
• Webinar will be recorded and attachments will be made available via
www.databricks.com

3. Introducing Our Speakers
3
Bilal Obeidat, Lead Solutions Architect
● 15+ years of Software Development at Microsoft, Hitachi
● MS, Computer Science, Bradley University
Brendan O’Shaughnessy, Data Science Manager
● 10+ years of Analytics and Data Science at Starbucks, NRPC
● MS, Spatial Information Science and Engineering, University of Maine
Rob Saker, Retail Industry Leader
● Formerly Retail industry Chief Data Officer (CDO)
● 15+ years in Data & Analytic leadership across Retail & CPG
● MBA, University of Nebraska

4. Unified data analytics platform for accelerating innovation across
data science, data engineering, and business analytics
Original creators of popular data and machine learning open source projects
Global company with 5,000 customers and 450+ partners

5. Databricks Customers - Over Half of Top 25 Retailers by Revenue
Media & Entertainment Technology
Public Sector Retail & CPG Consumer Services Energy & IndustrialMarketing & AdTech
Data & Analytics ServicesHealthcare & Pharma Financial Services

6. Agenda
➔ A Consumer-Driven Supply Chain
◆ Why Demand Forecasting is Relevant?
◆ The Need for Granular Demand Forecasting at Scale
◆ How Starbucks does Demand Forecasting?
➔ Demo: ML based Forecasting With Facebook Prophet
➔ Conclusion

7. Customers want what they want,
where they want it, when they want it
Consumer Behavior is Changing Supply Networks
DIRECT TO CONSUMER
PERSONALIZATION
40% of Best Buy’s online purchases are picked up at the store
50% of all restaurant prepared food is consumed away from restaurant
CUSTOMER ENGAGEMENT
FROM ANYWHERE
CONVENIENCE
IS KEY

8. Your Supply Chain Needs to be
Consumer-Driven

9. A Consumer-Driven Supply Chain Starts with Demand
Forecasting
What is Demand Forecasting?
Demand forecasting is the
process where we use
historical sales, promotions,
shopper and causal data to
understand and predict
customer demand.

10. Shipments
CPG Direct-to-consumer shipments
Manufacturers
Orders Store shipments
Distribution
Centers
E-commerce shipments to consumers
Shoppers
In-store purchases
Buy-online, pickup
in store (BOPIS)
E-commerce
delivery
Subscription
Retail Stores
Sales
Retail Supply Chain

11. Orders Store shipments
Shipments
CPG Direct-to-consumer shipments
Manufacturers Distribution
Centers
Retail Stores Shoppers
E-commerce shipments to consumers
In-store purchases
Buy-online, pickup
in store (BOPIS)
E-commerce
delivery
Subscription
Sales
Retail Supply Chain Demand Forecast

12. Orders Store shipments
Shipments
CPG Direct-to-consumer shipments
Manufacturers Distribution
Centers
Retail Stores Shoppers
E-commerce shipments to consumers
In-store purchases
Buy-online, pickup
in store (BOPIS)
E-commerce
delivery
Subscription
Sales
Retail Supply Chain Demand Forecast
Intrastore
shipments
Store shipments
Inventory levels
Reorder quantity
DC shipments
Production plan
Home delivery logistics
Home delivery logistics

13. Retail Capability Model
Supply Chain Management Merchandising Store Operations
Customer
Management
Planning Freight & Logistics Planning Operations
Supplier Management
Distribution
Replenishment
Inventory
Purchasing
Sourcing
Advanced Planning
Demand Forecasting
Warehouse
Management
Reverse Logistics
Transportation
Procurement
Logistics
Allocation
Demand Planning
Revenue/Price
Management
Promotions Planning
Assortment
Category Management
Space Planning
Fresh Planning
Demand Planning
Joint planning
Trade management
Merchandising
Store Channel
Promotions
Digital
Store Auditing
Broker Management
Store Operations
Workforce
Point-of-Sale
Shelf tags/Signage
Smart Store Devices
Store Inventory
Management
Returns Management
Fresh Production
Loss Prevention
Loyalty
Digital/Ecommerce
Mobile
Customer Support
Customer
Segmentation/CRM
Voice AI/Call Center
Back Office
People Management Recruiting Finance Asset Management Real Estate
Energy & UtilitiesIT
Industry Standards &
Compliance
Legal
Master Data
Management

14. Retail Capability Model
Supply Chain Management Merchandising Store Operations
Customer
Management
Planning Freight & Logistics Planning Operations
Supplier Management
Distribution
Replenishment
Inventory
Purchasing
Sourcing
Advanced Planning
Demand Forecasting
Warehouse
Management
Reverse Logistics
Transportation
Procurement
Logistics
Allocation
Demand Planning
Revenue/Price
Management
Promotions Planning
Assortment
Category Management
Space Planning
Fresh Planning
Demand Planning
Joint planning
Trade management
Merchandising
Store Channel
Promotions
Digital
Store Auditing
Broker Management
Store Operations
Workforce
Point-of-Sale
Shelf tags/Signage
Smart Store Devices
Store Inventory
Management
Returns Management
Fresh Production
Loss Prevention
Loyalty
Digital/Ecommerce
Mobile
Customer Support
Customer
Segmentation/CRM
Voice AI/Call Center
Back Office
People Management Recruiting Finance Asset Management Real Estate
Energy & UtilitiesIT
Industry Standards &
Compliance
Legal
Master Data
Management

15. Using Data and ML Across the Supply Chain is Challenging
FORECASTING NOT
ACCURATE OR GRANULAR
LARGE VOLUMES OF
RAPIDLY CHANGING DATA
LIMITED REAL-TIME AND
CAUSAL DATA
NOT EASY TO GET TO
ACTIONABLE INSIGHTS
Managers unable to get per
day /store/SKU forecast
Data is constantly shifting and
changing. Eg. Revised data to
account for shoplifting
Omnichannel is making local
(weather), real-time (IOT),
causal (competitor pricing)
data more important
Store/Distribution managers
get BI tools with lots of data
that they have no time to
explore

16. Traditional Demand Forecasting with Allocations
Market AreaPromo Group Week
Day Day Day

17. Traditional tools can’t scale to fine-grain
DC
Promo Group
Week
105,000
1 billion
500 million
2 billion
1.5 billion
DMA
Promo Group
Week
525,000
Store
Promo Group
Week
11.922,500
Store
SKU
Week
238,450,000
Store
SKU
Day
1,669,150,000
Traditional
Forecasting
Tools
Fine grained
forecast with
Databricks
Special run/few
times a year

18. Demand Forecasting is Complicated
You Need Forecasts by Product by Store
Stores SKUs

19. You Have a Large # of Store-SKU Combinations

22. Processing each
model sequentially is
slow

23. Each model may have
local causal influences

24. Distribute Model Training with Apache SparkTM

25. Apache Spark: De-Facto Unified Analytics Engine
Runtime
Delta
Spark Core Engine
Big Data Processing
ETL + SQL + Streaming
Machine Learning
MLlib + SparkR
Uniquely combines Data & AI technologies
R Python Scala Java

26. Use a Unified Data Analytics Platform Across the Data and
ML Lifecycle
DO GRANULAR AND
ACCURATE FORECASTS
KEEP UP WITH CHANGING
DATA
USE REAL-TIME AND
CAUSAL DATA
ACTIONABLE AND EASY
INSIGHTS FOR MANAGERS
Point BI tools that store managers
use directly at ML insights stored
on Delta Lake tables
Use and track 100s of ML
models to forecast demand by
day/store/SKU using MLflow
Use Delta Lake UPSERTS to keep
data consistent
Single streamlined pipeline for
real time and streaming data with
Delta Lake and Apache SparkTM

27. Unified Data Analytics for Consumer-Driven Supply Chain
Forecast
Demand
INVENTORY
DATA
IOT DATA
COMPETITOR
DATA
PRICING
DATA
SKU
DATA
DEMOGRAPHICS
DATA
GEO-LOCATION
DATA
PO0
DATAS
VIDEO
DATA
SHIPMEN
DATAT
Optimize
Inventory
Faster
Freight and
Logistics

28. Databricks Delivers Fine-Grained Demand Forecasting
Traditional Analysis Suites Databricks
Fine grained forecasting Aggregate level Day, store & SKU
Real-time data No Streaming data
Custom causal Data Limited
Integrate weather, online &
mobile interactions
Multi-modal data for training No
Structured, unstructured,
image, video, sensor data.
Localize models for greater accuracy No Yes
Push predictions to the edge No Yes

29. Starbucks Data Science
Forecasting Framework
“Flexible forecasting at scale”
Brendan O’Shaughnessy
Data Science Manager at
Starbucks

30. Delivery
Forecasting has utility across business
functions
Promotions Market Planning
Operations
Equipment Usage
Labor

31. Challenges
▪ Many metrics and granularities
▪ Timely delivery
▪ Easy accessibility for business stakeholders
▪ Iterative nature of data science
▪ Accuracy
▪ Quick diagnostics

32. Forecasting framework
▪ Built-in diagnostics
▪ Status monitoring
▪ Leverages Delta Lake and ADLS for data ingress and
egress
▪ Executable from Databricks CLI
Robust and reliable
▪ Agnostic to metric and granularity
▪ Leverages diverse forecasting algorithms using
grouped pandas UDFs
▪ Modular structure for straightforward additions
and enhancements
▪ Apache SparkTM enables quick runtimes regardless
of the number of individual forecasts
Flexible

33. Example structure
Create custom forecasts
for any use case with
relative ease
Enterprise data
(DB Delta)
Historical
Metric
Custom data
(ADLS)
Build dataset
Compile results
To stakeholders
Model 1 Model 2 Model n
To data products
…

34. https://www.linkedin.com/company/starbucks/jobs/

35. Demand Forecasting
Demo

36. Key Takeaways Slide
ü A Consumer-driven supply chain is indispensable
ü Start with demand forecasting at scale
ü Localization of models is slow and tedious
ü Using Data and ML across supply chain is challenging
Databricks provides Unified Data Analytics to bring Data and ML
together for accurate and granular demand forecasting

37. Thank you! Questions?
38
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