Building the Data-Driven Organization

18

Click here to load reader

Transcript of Building the Data-Driven Organization

Page 1: Building the Data-Driven Organization

9/11/2016 September 2016, p.1Supply Chain Insights Global Summit #ImagineSC

Analytics for Supply Chain ExcellenceBuilding the Data Driven Organization

Mani Janakiram, PhDDirector, Supply Chain Intelligence & Analytics

Intel Corporation

September 2016

Page 2: Building the Data-Driven Organization

p.22Supply Chain Insights Global Summit 2016

Intel’s VisionIf it is smart and connected, it is best with Intel.

2

Data Center Client Wearables/IoT

Page 3: Building the Data-Driven Organization

p.33Supply Chain Insights Global Summit 2016

Continuing toextend our leadership

through innovation

Delivering Intel’s Vision and Mission

Making significant

investments and have the

scale to deliver

A world-classSupply Chain

delivering whatour customers want

Caring for the planet

and its people

Technology

Leadership

Manufacturing

ScaleAgile and Responsive

Supply ChainSocial

Responsibility

These 4 themes continue to be integral pieces that shape our SC strategy

Page 4: Building the Data-Driven Organization

p.44Supply Chain Insights Global Summit 2016

Our Supply Chain is Big and Complex…

Wafer Fabrication Assembly, Test

Research Develop Manufacture

Intel Foundry Products

Customers

Construction, Facilities Services

PlanningSourcing &

Procurement

Systems, ICs

Memory

UX Ingredients

Outsourcing

Plan Source Make ReturnDeliver

16,000Suppliers in

100 Countries

~1MillionOrders

$22BAnnual

Spends

101,000Ship To

Locations

>5,500Active SKUs

>450Supplier

Factories

29,000Transport

Lines

>10 Intel Factories worldwide

Analytics plays a key role in transforming Intel’s supply chain

4M Sq ftMfg. Space

11,000Shipping

Lanes

>1400Customers

Page 5: Building the Data-Driven Organization

p.5Supply Chain Insights Global Summit 2016

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

BCP

14nm

Unwire

Velocity

World’s 1st Conflict Free Microprocessor

Customer focused supply chain performance enables Intel’s strategy

Page 6: Building the Data-Driven Organization

p.6Supply Chain Insights Global Summit 2016

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

n

S

o

u

r

c

e

M

a

k

e

D

e

l

i

v

e

r

R

e

t

u

r

n

O

p

t

i

m

i

z

a

t

i

o

n

S

i

m

u

l

a

t

i

o

n

S

t

a

t

i

s

t

i

c

s

SupplyDemand

SCM*

Physical + Information + Cash Flow

Page 7: Building the Data-Driven Organization

p.7Supply Chain Insights Global Summit 2016

Successful SC Analytics - TalentThe 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

Page 8: Building the Data-Driven Organization

p.8Supply Chain Insights Global Summit 2016

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…

Page 9: Building the Data-Driven Organization

p.9Supply Chain Insights Global Summit 2016

Next Gen MPS* SolutionOptimize 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

Page 10: Building the Data-Driven Organization

p.10Supply Chain Insights Global Summit 2016

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

Page 11: Building the Data-Driven Organization

p.11Supply Chain Insights Global Summit 2016

End-to-End Supply Chain Simulation

Supply-Chain

Network

Decision

Connector

LP

/CP

LE

X O

pti

miz

er

W1

BOM

1

BOM

n

DP1

BOM

2

W2

BOM

1

BOM

n

BOM

2

DP2

DP3

DP4

DP5

DP6

Network Generator

CTRL_INPUT_PORTS

tru

ctu

re In

form

ation

Inp

ut D

ata

DatasetDemand/Supply Information

Mo

de

l Co

nstr

uction

Knowledge

Interchange

Broker

(KIB)

Adding Flow

Information to the

graph

Invoke Solver

Flow Information

Output Data

Release Commands

Demo

FabsAssembly

Warehouses

Enables what-if of end to end supply chain design and scenarios

Page 12: Building the Data-Driven Organization

p.1212Supply Chain Insights Global Summit 2016

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.

Page 13: Building the Data-Driven Organization

p.1313Supply Chain Insights Global Summit 2016

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.

Page 14: Building the Data-Driven Organization

p.14Supply Chain Insights Global Summit 2016

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

Page 15: Building the Data-Driven Organization

p.15Supply Chain Insights Global Summit 2016

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

Page 16: Building the Data-Driven Organization

p.16Supply Chain Insights Global Summit 2016

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 Deliver Make Return

Page 17: Building the Data-Driven Organization

p.1717Supply Chain Insights Global Summit 2016

IoT Enabled Supply Chain

Internet

Data DataDataData Data Data Data

Cloud Analytics

Data

DataProduct

Factories Airports WarehousesPortsSuppliers

ADAS

API Data

API Data

Data

Customer

Social Media & Environmental End to End

Visibility

Ships

PlanesTrucks

Page 18: Building the Data-Driven Organization

p.18Supply Chain Insights Global Summit 2016

Key to building the Data Driven Organization – Talent Mgmt.

1

8

Questions….