Building the Data-Driven Organization

9/11/2016 September 2016, p.1Supply Chain Insights Global Summit #ImagineSC
Analytics for Supply Chain Excellence
Building the Data Driven Organization
Mani Janakiram, PhD
Director, Supply Chain Intelligence & Analytics
Intel Corporation
September 2016

p.22
Supply Chain Insights Global Summit 2016
Intel’sVision
If it is smart and connected, it is best with Intel.
2
Data Center Client Wearables/IoT

p.33
Continuing to
extend our
leadership
through
innovation
DeliveringIntel’sVisionandMission
Making
significant
investments and
have the
scale to deliver
A world-class
Supply Chain
delivering what
our customers want
Caring for the
planet
and its people
Technology
Leadership
Manufacturing
Scale
Agile and Responsive
Supply Chain
Social
Responsibility
These 4 themes continue to be integral pieces that shape our SC strategy

p.44
Our Supply Chain is Big and Complex…
Wafer Fabrication Assembly, Test
Research Develop Manufacture
Intel Foundry
Products
Customers
Construction,
Facilities Services
Planning
Sourcing &
Procurement
Systems, ICs
Memory
UX Ingredients
Outsourcing
Plan Source Make ReturnDeliver
16,000
Suppliers in
100 Countries
~1Million
Orders
$22B
Annual
Spends
101,000
Ship To
Locations
>5,500
Active SKUs
>450
Supplier
Factories
29,000
Transport
Lines
>10 Intel Factories worldwide
Analytics plays a key role in transforming Intel’s supply chain
4M Sq ft
Mfg. Space
11,000
Shipping
Lanes
>1400
Customers

p.5
Performance Parameters and Key Focus Areas
Resilient and
Responsive Supply
Chain
Supply Chain
Social
Responsibility
Touch
3D Depth Camera EICC Code
Transparency
ESG Performance
Enabling
Innovations
B
C
P
14nm
Unwire
Velocity
World’s 1st Conflict
Free Microprocessor
Customer focused supply chain performance enables Intel’s strategy

p.6
Supply Chain Analytics Scope
Finance
•Revenue projections
•Cost control
Sales & Marketing
•Evaluates and agrees on
TAM, SAM, SOM
•Respond to forecasts
•Manage customer accounts Forecasting
•Generate demand forecasts
•Request Inventory Levels
Inventory
Planning
•Map demand to capacity
•Manage inventory &
Fulfillment
Logistics
•Network Design
•Geo transportation
•Warehouse and VMI
Development/
Manufacturing
•Own product manufacturing
Materials /
Equipment
•Delivers materials &
Equipment, Substrates Engineering
•Product Design, NPI
•Time to Market
*Supply Chain Management involves coordination and synchronization of
business process, data and decisions among several functions
P
l
a
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o
n
S
t
a
t
i
s
t
i
c
s
SupplyDemand
SCM*
Physical + Information + Cash Flow

p.7
Successful SC Analytics - Talent
The Right Skills, Education & Expertise
BSIE BSCS MSIE/OR MSCS MBA PHD
Data/Information: Systems/CS
Modeling/Analytics: IE/OR/Math/Decision Science
Presentation/Visualization: MBA/Info. Systems
Supply Chain Certifications
Lean Six Sigma
Project Management
Software Certifications
Data
Scientist
Domain
Knowledge
Soft
Skills
Analytics
Skills
Software
Skills

p.8
constraints and
objective function
to match supply
and demand
master data (e.g. bill of materials, routings)
parameter data forecast (e.g. TPTs, yields)
financial data (e.g. mfg and inv costs, selling price)
capacity forecast
WIP & inventory
supply priorities
demand forecast
inventory targets
demand priorities
matched
(S ~ D)
not
matched
material release
plan
adjust and
iterate
SC Planning: Involves Timely Judgment…

p.9
Next Gen MPS* Solution
Optimize to meet customer needs
Wafer Starts, Finished Goods
Across whole network
Material availability, capacity
constraints, inventory strategies.
Use Common Tools and Processes
Across all divisions/products
Efficient & effective optimization
capabilities (Fan-in & Fan-out)
Extensive optimization based analysis
and what-if capabilities
FS1
FS2
FS3
FS4
FS5
FS6
FS7
FS8
FS9
Fab1
Fab2
Fab3
Fab4
Fab5
Fab6
Fab7
DP1
DP2
DP3
DP4 ATS1
ATS2
ATS3
ATS4
ATS5
AT1
AT2
AT3
AT4
AT5
WH1
WH2
Bottom-line Results…
* Master Production Schedule

p.10
End-to-End Supply Chain Simulation
Use Cases
 Test bed for planning and control strategies
 Analysis on financials and service levels
 scenarios to procurement strategies
 Inventory versus Service Level trade-off
 Transportation trade-offs
Poly-Formalism Approach
 Heuristics for Inventory strategy
 Optimization models for planning algorithms
 Discrete event simulation for material flows
Returns vs. Service Level vs. DOI
Fulfillment by location
Semiconductor manufacturing simulation from supplier to customer.
Enables what-if of supply chain design and scenarios

p.11
End-to-End Supply Chain Simulation
Supply-Chain
Network
Decision
Connector
LP/CPLEXOptimizer
W1
BOM
1
BOM
n
DP1
BOM
2
W2
BOM
1
BOM
n
BOM
2
DP2
DP3
DP4
DP5
DP6
…
Network Generator
CTRL_INPUT_PORT StructureInformation
InputData
Dataset
Demand/Supply Information
ModelConstruction
Knowledge
Interchange
Broker
(KIB)
Adding Flow
Information to the
graph
Invoke Solver
Flow Information
Output Data
Release Commands
Demo
Fabs
Assembly
Warehouses
Enables what-if of end to end supply chain design and scenarios

p.1212
Big Data Analytics – Supplier Intelligence
Automated data consolidation and what-if scenario analysis using
various unstructured external data sources to provide strategic decision
support and predictive analytics for multi-supplier evaluation.

p.1313
Predictive Analytics: Supplier Intelligence
What happens when a new tool technology is introduced?
How would the supplier and buyers plan?
Revenue projections
Market Size Projections
Sup #1 Sup #2 New
Technology shifts cause large
changes in revenue streams and
can potentially influence change.

p.14
Dynamic Inventory Surveillance
DP
SORTFAB
DP
SORTFAB
SORTFAB
SORTFAB
ATM
ATM
CW
VMI
VMI
VMI
VMI
VMI
Inventory monitoring and issue prediction are key capabilities for supply chains
Complexity of modern supply chains complicates inventory surveillance
Inventory surveillance aims to provide a single tool for inventory visualization and prediction

p.15
Inventory Surveillance Application
In-control Out-of-control
In control vs. out-of-control determined automatically
using a machine learning algorithm that compares the
natural variability in the historic data vs. new data
Green node & grey arc: normal
Yellow node & brown arc: warning
Red node & red arc: control limit exceeded
Heat Maps are Monitored
for any Change

p.16
Connecting & Enabling the Supply Chain with the Internet of Things…
Materials Supply
• Visibility to critical-path
parts / JIT delivery
• Improved inventory
accuracy & control
• Spares management
Manufacturing
• Synchronization of
conversion / kitting orders
• Correlate SC data for
equipment performance
analysis
Logistics &
Warehousing
• Real-time asset &
shipment tracking
• Dynamic routing
• Smart inventory
management
• Asset utilization / safety
Customer
• E2E delivery management
• Product use / performance
data
• Visibility to parts
inventories
• Optimize reverse logistics
Continuous Connectivity, Device Discovery and Provisioning
Actionable Analytics to Make Proactive Supply Chain Adjustments
Security as the Foundation to Protect Critical Information & Data - Hardware & Software
Continuous Supply Chain Visibility Through Smart, Connected Devices
Source DeliverMake Return

p.1717
IoT Enabled Supply Chain
Internet
Data DataDataData Data Data Data
Cloud Analytics
Data
Data
Product
Factories Airports WarehousesPortsSuppliers
ADAS
API Data
API Data
Data
Customer
Social Media & Environmental End to End
Visibility
Ships
Planes
Trucks

p.18
Key to building the Data Driven Organization – Talent Mgmt.
1
8
Questions….

Building the Data-Driven Organization

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