Engineering Your Supply Chain for Exponential Growth

Engineering Your Supply Chain for Exponential Growth

July 25, 2012

NVIDIA Confidential SAP Supply Chain Summit July 25, 2012

2

Agenda

� NVIDIA overview

� Intrigo overview

� State of the industry

� Business problem

� CODE solution overview

� Complete

� Optimized

� Data

� Engine


3NVIDIA FactsFounded in 1993

Jen-Hsun Huang is co-founder, president, and CEO

Listed with NASDAQ in 1999, under the symbol NVDA

Invented the GPU in 1999, more than one billion shipped to date

FY12: $4 billion in revenue

7,500 employees worldwide

5,000 patents issued, allowed or filed

Headquartered in Santa Clara, CA


4From Super Phones to Super Cars


5

GPU Mobile Cloud

GeForce®

Quadro® , Tesla®Tegra® VGX ™

GeForce® GRID

Core Technologies and Brands


6About Intrigo

We are a Premier Enterprise Consultancy, focused on orchestrating Customer

Value Networks at the confluence of Demand, Supply and Innovation.

OUR SERVICES

Advisory

Re-architect the planning processes

Usability / workflow re-model

Enterprise Architecture and Software components

Implementation

Complete turnkey implementation

Assemble to deliver

Support

Offshore project/engineering delivery

Planning Analytics

Supply Respond Sense

Innovate

Source: AMR

Create

Demand


7Intrigo Systems’ Clients

“The ability of Intrigo’s leadership team to execute

in a complex, multi-party environment directly

contributed to the success of our project."

-- John Hanna,

Director - iPlan Project, Clorox

“Intrigo's deep industry domain knowledge and

their expertise in SAP made for an effective

implementation of our Business Systems. Their

commitment and focus to making us successful

make Intrigo a key partner for Aptina.”

-- Joe Passarello, CFO, Aptina Imaging


8Outsourced Manufacturing

NVIDIA selects proven subcontractors and processes

Capacity is allocated through a forecast : commit

communication cycle

NVIDIA may invest in testers and custom hardware

Vendor process stability and execution are monitored


9Capacity Alignment

Every month, NVIDIA provides a

6-month rolling forecast

Vendors respond with a commitment to

the 6-month plan

NVIDIA places purchase orders

as requiredVendors respond with acknowledgements

and delivery commitments

Weekly meetings review loading flexibility, WIP, delivery status, and action plans

Quarterly Business Reviews scrutinize goals and develop improvement plans

NVIDIA provides an annual

forecast to vendors

Vendors respond with an annual

capacity commitment


10State of Supply Chain Planningin the Semiconductor Industry

• Discrete vs. attribute-based part numbers

• Multiple postponement points

• Optimization planning models

• Long lead time materials (finished wafers)

• Capacity-constrained, high value materials

• Test equipment, allocation to multiple subcons, cost

• Heuristic planning models

• Order-based planning (customer centric models)

• Lead time assumptions for fabless firms are vague


11

Field Sales Forecast

FiscalSales Plan

Customer / Sales

Monthly Cycle

Demand

APO System

Boards Supply

Chips Supply

Supply : Demand Planning Cycle

Demand Planning (Sales & Business

Units) place a demand on Operations

Planning commits supply to

specific dates, recorded in SAP

SAP/ APO System


12

Supply Chain Planning Evolution

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Demand planning

(SAP APO)

Unconstrained supply plans

(SAP APO – PPDS heuristics)

GATP with product

allocation (SAP APO)

VMI deployment

FCD improvementsCODE

• Constrained planning• Optimized planning• Scenario planning

Inventory forecasting and

supply-demand alignment

(SAP APO with BO dashboards)

Postponement planning

12-month supply commitAggregate wafer

Planning


13

Business Problem

NVIDIA's business has grown more complex

Our ability to project, manage and optimize our wafer supply has

entered the critical path of our growth

Existing spreadsheet-driven processes cannot scale to the growth of our business (market segments, number of SKUs, product complexity)

Today’s focus is to align supply to marketing demand by SKU

Tomorrow, beyond alignment, we want to employ production plans optimized for flexibility and efficiency


14

MANUFACTURING STEP

TO BE PLANNED

Excel

Planning

single user

Excel

Planning

multi user

Complete

Planning

Scenario

Planning

Optimized

Planning

Integrated

Planning

Wafer Production

(GPU, interposer)done 1 3 10 16 -

Post Fab Wafer Processing

(bump, sort, backgrind)2 4 12 17 -

Assembly BOM Components

(substrates, interposer die)done 8 - - -

Assembly Services

(die mounted into a package)done 5 15 19 -

Post Assembly Validation

(ATE, burn-in)done 6 14 20 -

Configuration

(SLT, tape & reel)done 7 11 18 -

FG BOM Components

(kitted memory)done 13 - - -

Logistics

(factories, hubs, EOQ)9 21 - -

Excel planning on a single user systemExcel planning using a shared drive Complete planning: all data elements visible in the system, must have at least MRP II capabilities Scenario planning: extracting data from the system, manipulating data in a structured way, and saving resultsOptimized planning: system generated best answers for problems contained within the Chip Ops siloIntegrated planning: active links with Sales, BU, & Finance tools; i.e., mutual plans re-align at least daily

Planning Priorities for System Development(Summer 2011)


15Wafer Planning in Multi-User Excel

Shared Drive Folder for Virgin Data

Shared Drive Folder for Data Cleaning Workbooks

Shared Drive Folder for Calculation & Analytics Workbooks

One workbook per file is archived each week.

18 files pulled out of systems of record.Large files exceed 50,000 rows.

Supply and demand are consolidated at the wafer level and the global location level.

Supply–demand matching occurs in APO. It does not optimize and is a deterministic model.


16Functionality Comparisons- Business(Fall 2011)

Criteria SAP i2/JDA

Data input / management Satisfactory Satisfactory-excellent

Engine – LP TBD – may not be needed Excellent

Engine – heuristics Satisfactory Satisfactory

Integration with SAP ERP Excellent Satisfactory

Scenario management Satisfactory Satisfactory-excellent

Analytics Satisfactory-excellent Satisfactory

i2 is the better product in terms of core functionality required for Chips Planning

APO has seamless integration with our SAP ERP system

A custom user interface ‘skin’ will be required for APO to ensure data input accuracy

A custom report writer for analytics will be required for APO

Add-ons to the APO LP Optimizer may be required (TBD)


17Risk Factors- IT Judgment(Fall 2011)

Criteria SAP / Intrigo i2/JDA

Perceived quality of solution

(‘completeness’ of functionality)

Medium Low

Implementation experience

(semiconductor industry)

Medium Low

Financial viability of vendor Low Low

Integration challenges Low Low-Medium

Vendor ‘depth’ of bench Medium Low-Medium

Overall risk of moving forward with

vendor

Medium Low

i2 has the LP Optimization, Scenario Analyzer and Planning Data user interface

deployed in multiple semiconductor companies. But we have no i2 expertise in house

and we have to manage the seamless integration with SAP .

SAP has less semiconductor experience than i2 and a user interface ‘skin’ that needs

to be custom-developed for ease of use. TCO is significantly less than i2.


18Design Guiding Principles

• NVIDIA challenges are vanilla

• Manufacturing flows are “typical” semiconductor, including binning

• Goals: no planning in Excel & improve the data quality

• Master data must be capable of being easily maintained

• Planning rules maintained in Business language

• Transparent for other NVIDIA teams to use

• Results must be explainable to executive management

• Models must enable postponement strategies & buffers

• Scenario & bridge analysis support will be required


19Planning Solution Overview

Optimization

for wafer

planning

Wafer allocation by

product:

constrained for

backend planning

Backend planning

to determine

assembly starts &

PFG starts to

support the forecast

PFG supply:

constraint for SLT

planning

SLT planning to

determine PFG

starts for demand

signal

Projected

finished goods

for order

commitments



Optimization

for wafer

planning

Wafer allocation by

product:

constrained for

backend planning

Backend planning

to determine

assembly starts &

PFG starts to


PFG supply:

constraint for SLT

planning

SLT planning to

determine PFG

starts for demand

signal

Projected

finished goods

for order

commitments

Wafer Planning by SAP APO

Optimizer


Optimizer

Backend planning by SAP APO

Optimizer


Optimizer

Backend SLT planning by

SAP CTM


SAP CTM



Optimization

for wafer

planning

Wafer allocation by

product:

constrained for

backend planning

Backend planning

to determine

assembly starts &

PFG starts to


PFG supply:

constraint for SLT

planning

SLT planning to

determine PFG

starts for demand

signal

Projected

finished goods

for order

commitments


Optimizer


Optimizer


Optimizer


Optimizer


SAP CTM


SAP CTM

Custom UI for Data ManagementCustom UI for Data Management


22Reference Model Example

Sort

Back grind

Assy Test SLT

Tape &

Reel

FG

Bump

PFG/FGAssembly

BackgrindedDie

DieSorted WaferUnsorted WaferUn bumped Wafer

Fab

Three supply plans are generated:Fab to die (2 year horizon)

Die to FG (1 year horizon)

SLT / T&R ad hoc runs (4 week horizon)

Focus on data, priorities, rules, and system generated supply plans

Modular locations- easily added or removed as supply chain evolves


23CODE Inputs & Outputs

CODE

SAP

Platform

Product Data

Demand

Unconstrained Supply

Factory Loading Strategies

Product Priorities

Inventory Strategies

DATA OPTIMIZER

FG Supply Commit

Die/Capacity Max Supply

Scenarios – What-if’s

Execution Instructions

RESULTS

Capacity

Materials


24Business Rules for Planning

• Demand segmentation priority

• BOM priority

• Subcon loading priority

• SKU selection

• Lean rule

• BOM downbinning

• Buffer stock

• Subcon quota

• Product substitution

Rules

Strategies

Priorities

• Ease of maintenance• Excel like interface• Master data scenario management• Time phased data

• Yields• Resource availability• Resource consumption

• Time phase priorities• Product• Demand segmentation• BOM• Routing

• Max and Min volume constraints


25Planning Data Flow Overview

APO Planning Engine

Core InterfaceStandard SAP component to transfer planning input data (Master &

Transaction Data) and output data (planning results)

APO Planning Engine

Transaction Data

Data Warehouse

APO Master Data transfer

Data Repository

Input Screens Planning Data Management Tool

Publish to Output

ECC

EC

C

Ma

ste

r D

ata

tra

nsfe

r

Execution instructions to subcon

Confirmations from subcon

APO Master Data transfer

Build Rules

Build Strategies

Time Phased Data

Planning Results


26Planning Scenarios Overview

POR version

Backend material and capacity-constrained version

Wafer capacity-constrained version

Wafer capacity-unconstrained version

Backend material-constrained, capacity-unconstrained version

Backend constrained, push production

SLT material and capacity-constrained version

SLT material-constrained, capacity-unconstrained version

Scenario solves as per the requirement

Wafer Plan

Backend Plan

SLT Plan


27Problem Data SetEngine: SAP SNP Optimizer

Solve Type: linear

Horizon: 2 years

Cost Model: relative cost model engine


28

Thank You

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Engineering Your Supply Chain for Exponential Growth

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