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Building Digital Analytics Foundation for Autonomous Factories at Western Digital

Attila Lengyel, PhD

Director of Strategy, Innovation & Adoption

Digital Analytics Office

11/28/2018©2018 Western Digital Corporation or its affiliates. All rights reserved.

MDIC - Case for Quality Forum

Wednesday, November 28, 2018

Arlington, VA

This presentation contains forward-looking statements that involve risks and uncertainties, including, but not limited to, statements regarding

our products and technologies, market positioning, business strategy and growth opportunities, market trends, and data growth and its drivers.

Forward-looking statements should not be read as a guarantee of future performance or results, and will not necessarily be accurate indications

of the times at, or by, which such performance or results will be achieved, if at all. Forward-looking statements are subject to risks and

uncertainties that could cause actual performance or results to differ materially from those expressed in or suggested by the forward-looking

statements.

Key risks and uncertainties include, among others: volatility in global economic conditions; actions by competitors; unexpected advances in

competing technologies; business conditions; growth in our markets; and pricing trends and fluctuations in average selling prices. More

information about the risks and uncertainties that could affect our business are listed in our filings with the Securities and Exchange

Commission (the “SEC”) and available on the SEC’s website at www.sec.gov, including our most recently filed periodic report, to which your

attention is directed. We do not undertake any obligation to publicly update or revise any forward-looking statement, whether as a result of

new information, future developments or otherwise, except as required by law.

[This presentation contains financial measures defined as non-GAAP. The non-GAAP measures are used by the company’s management to

forecast, evaluate and review the financial results of the company. Management believes these non-GAAP financial measures are useful

because they provide meaningful comparisons to prior periods and exclude certain items that may not be indicative of the underlying

performance of the company’s business. These non-GAAP financial measures should be used in addition to, and in conjunction with, results

presented in accordance with GAAP to better understand the company’s financial performance. Non-GAAP measures are not in accordance with,

or an alternative for, measures prepared in accordance with GAAP and may be different from non-GAAP measures used by other companies.] 1

1 The last paragraph of this disclaimer and the corresponding Appendix on pages 35 and 36 of this presentation are required to be included in a presentation that includes any of the

following slides: 31-33.

Forward-Looking StatementsSafe Harbor | Disclaimers

©2018 Western Digital Corporation or its affiliates. All rights reserved.

Roadmap to Autonomous Factories


WDC Data Infrastructure - History & Future Roadmap

Introducing Western Digital Corporation

Data Analytics Organization - Lessons Learned

Breaking silos and establishing a hybrid organizational structure

Data Analytics Project Management - Business Processes

Governing the impossible without “boiling the ocean”

Success Stories & Summary - AI at Work

Ideas & projects made it to production implementation

Portfolio of Leading Brands

©2018 Western Digital Corporation or its affiliates. All rights reserved. 5

Global Presence

6

MANUFACTURING OPERATIONS

San Jose, CA, USA(Global HQ)

Sao Paulo, Brazil

Shanghai, China

Shenzhen, China

Yokkaichi, Japan

Kuala Lumpur, Malaysia

Johor, Malaysia

Penang, Malaysia

Sarawak, Malaysia

Laguna, Philippines

BangPa-In, Thailand

Navanakorn, Thailand

Prachinburi, Thailand

Fremont, CA, USA

65K+employeesworldwide


1976 | WDFirst Disk Array Sub-System Patent

1956 | IBMInvents First Hard Drive (RAMAC)

1991 | IBM Introduces MR Heads

2000 | SanDiskFirst MLC NAND

2016 | SanDiskFirst 64-layer 3D NAND Technology

2013 | HGSTFirst Helium HDD (6TB)


2006| HGSTFirst 2.5-inch HDD PMR Technology


2009 | SanDiskFirst 64GB X4 Flash memory

1995| M-SystemsFirst Flash Drive

2000| IBM & M-SystemsFirst USB Flash Drive

2008| G-TechnologyFirst 1TB Portable

External HDD

Heritage of Innovation14,000+ active patents worldwide

7

1988 | SunDiskIntroduces Flash as a Storage Medium

2009 | Fusion-ioFirst Enterprise PCIe Flash Accelerators

2013 | HGSTFirst 12Gb/s SAS SSD


IBM RAMAC(first HDD)

CAPACITY

12 TB

FORMAT

8 disks 3.5”

AREAL DENSITY

864 Gbit/sqin

DATA RATE

12 Gb/s

POWER

~10 W

COST/MB

<$0.000032

Ultrastar HE122016

2.4M xincrease

>99.9%smaller

432M xdensity

80K xfaster

99.6%less power

1956

CAPACITY

5 MB

FORMAT

50 disks 24”

AREAL DENSITY

2 Kbit/sqin

DATA RATE

150 Kb/s

POWER

2374 W

COST/MB

$10,0001x 108

cost improvement

1 source: anandtech2 source: website

6 Decades of Innovations – Phenomenal Scaling!


http://www.anandtech.com/show/10888/western-digital-announces-ultrastar-he12-12-tb-and-14-tb-hdds

http://www.statisticbrain.com/average-cost-of-hard-drive-storage/

Leading Portfolio Breadth and DepthSolutions to Capture, Preserve, Access and Transform Data

TechnologiesHeads

Media DeviceSoftware System

Software

RISC-V

Controllers

Platforms/Systems/Solutions

Devices

Client Devices Client Solutions

Data Center Devices & Solutions


Enabling Change for a Data-centric World

Generate Transform Access

Storage


The Changing Nature of Data Interaction

11

PastData Held Captive by Single Application

Current and FutureData Pooled and Shared by Multiple Applications

capturedeliver

appcreate

transform

store

access

create

storeaccess

deliver

store

store

transform

create

create

create

transform

create

©2017 Western Digital Corporation or its affiliates. All rights reserved. Confidential.

transform©2018 Western Digital Corporation or its affiliates. All rights reserved.

PastData Held Captive by Single Application

Current and FutureData Pooled and Shared by Multiple Applications

capturedeliver

appcreate

transform

store

access

create

storeaccess

deliver

store

store

transform

create

create

create

transform

create

transform

The Changing Nature of Data Interaction

12©2017 Western Digital Corporation or its affiliates. All rights reserved. Confidential.

EB data lake

TB data set


Diverse and Connected Data TypesTight coupling between Big Data and Fast Data

13

BatchAnalytics

StreamingAnalytics

ArtificialIntelligence

MachineLearning

DataAggregation

Modeling

Scale

INSIGHT

PREDICTION

PRESCRIPTION

Big Data Fast Data

Performance

SMART MACHINES

MOBILITY

REAL-TIMERESULTS


Workload Diversity Demands Diverse Technologies and Architectures

StorageHDD SSD

FastData

BigData

Performance-centric scale

Capacity-centric scale

Compute

Storage SOC GPU FPGA ASIC

MemoryNVM DRAM SRAM

Storage SemanticData Flow

Memory SemanticData Flow

Interconnect

Storage-centricarchitecture

Purpose-built data-centric architectures

Memory-centricarchitecture

14

General Purpose CPU


Symbiotics Design™Designing, tuning and optimizing across the entire portfolio technology stack

Media

Mechanical

Controller

Firmware

Firmware

Mechanical

Software

Systems

Platforms

Devices

Architecture

Higher Performance

Superior TCO

Breakthrough Value


Symbiotics Design™Creating breakthrough value and performance for devices, platforms and systems

11/28/2018 16©2018 Western Digital Corporation or its affiliates. All rights reserved.

Heads, Media, Read Channel, Controller,

Firmware, Mechanical Design

HDD Devices, SSD Devices, Electric Design, Mechanical Design, Power Supplies, Firmware

Device, Platforms, System Software

NAND, Controller, Firmware/FTL

11/28/2018 17©2018 Western Digital Corporation or its affiliates. All rights reserved.

WDC Internal Big/Fast Data Platform We built open, scalable platform to future-proof against rapid technology shifts

Compute

Memory

Interconnect

Storage

HDFS

Mesosphere

OS VMWare

Bare Metal Servers

Direct-attach Storage

WDC Tegile

WDC ActiveScale @ Site

HDFS on WDC ActiveScale

S3 on WDC ActiveScale

EdgeCore WDC Big/Fast Data Platform

Fast Data Software Stack

HDFS

S3 on AWS

EphemeralSSD & HDD

EBSSSD & HDD

WDC ActiveScale @ Switch

HDFS on WDC ActiveScale

S3 on WDC ActiveScale

Fast Data Software Stack

HDFS

EC2 @ AWSCompute & Memory

Platform supports spectrum of applications • Core supports Big Data applications• Edge supports Fast Data from

IoT/Stream devices

Conceived as layers of abstraction• Upper layers enables compute and

containerization• Lower layers enables flexible hybrid

storage choices

Leverages WD technologies• ActiveScale as on premise alternative

to AWS S3 at lower cost, reduces single cloud provider dependence

• Tegile to decouple block storage from technology choices to enable high performance workload


We have invested a lot, but where the ROI is…


We are already doing that…


Ok, this looks challenging…


Data Science is multidisciplinary…


If industrial engineers automate factories, data scientists automate engineering departments…


Need to figure a way to break silos and collaborate…


Data Analytics done right can be the “silver bullet”


Network Organization* within Corporate StructureEmployees or business groups act as independent nodes and also link across boundaries to develop new products or solutions to complex business problems

Members (Experts)

• Diverse talents• Specialized skills• Proven track-record of success• Leadership and people skills

Interactions (Sharing)

• Sharing goals• Sharing Information• Leveraging resources• Leveraging learning

Alignment (Consensus)

• Formal decision making framework

• Consensus on goals, plans and actions

Results (Collaboration)

• Act as one organization to influence decision making

• Improved quality and productivity

• Accelerated project deliveries

*Driving Results Through Organizational and Personal Networks – Rob Cross from Babson College, Boston


2016 Data Analytics Global Workshop

Playbooks

Playbook

• Component Group Imperatives

• Head/Media Imperatives

Vision Proposal

Playbook

• Reviewed strawman• Subgroup to work refine

vision throughout the workshop

DA Projects

Playbook

• Correlated all 84 data analytics projects to SWOT priorities

• Discussed opportunities

Data Systems

Playbook

• Data accessibility and quality requirements of top priority project per organization

Software Tools

Playbook

• Inventory of all software tools and their usage

• Likes/Dislikes of tools

Eng. Productivity

Playbook

• Problem statements of inefficiencies & bottlenecks of effectiveness

• Opportunities & Strategies

DA Vision

Playbook

• Voted on versions • Drafted the final• Received consensus on

Vision

SWOT

Playbook

• Data Systems (Accessibility & Quality)

• Training & Talents• Management Support

Missions

Playbook

• Operating model• Strawman problem

statements, goals, targets and scopes

Over 100 analytics leaders from world-wide organizations to kick-off the network organization


Create a data driven culture where we enable and accelerate business decisions based on sound statistical principles by fostering analytics innovation and revealing opportunities.

Strategic Anchors:

Immediate Imperatives:

Engineering Productivity

Provide Access to Business Critical Data

Make all component and HDD data fully accessible

Ensure High Quality of Data

Implement data quality metrics and an assessment framework that drives completeness, timeliness, validity, consistency and integrity

Develop Best Practices in

Data Analytics

Drive best analytics practices using a variety of tools and protocols; establish a library of methods and algorithms

Plan & Execute Projects

Identify analytics projects that have common objectives; promote, cultivate and support prioritization of these projects to achieve maximum shared value

Upskill Engineers in

Data Analyitcs

Define a data analytics roadmap for training engineers in best practices for data exploration and decision making

Accelerate Data Integration

Identify component critical to function parameters from both legacies and create a look-up table for cross-referencing

Effectiveness

Collaboration Efficiency

Optimization

Culture of High Performance & Quality

2017 Playbook


Meeting Rating

Oct 3, 2016 CAG Weekly Meeting rating unanimous vote of straight 6 from 8 Component Group organizations, where we finalized this package and proposal

7.0 – I would cancel a Hawaiian vacation for this meeting

1.0 – I would rather stay at home, the meeting was not useful

Workshop

Package proposal

Strategy formation steps

Network organization


Innovation Machine to Turn Ideas into Solutions

Software Platform

Business Data Management

Data Storage PlatformData

Accessibility

Architects of Data Storage

Data Quality

Architects of Processes

Best Practices

Architects of Solutions

Guardians of Innovation

Guardians of Business Focus

Guardians of Business Discipline

Business Value

Acceleration

Plan & ExecuteBusiness

Integration

• Subject matter of expert review

• Statistical analysis• Algorithms

• Data mapping• Data blending

Analytics Community

Initiative

Proof of Concept

CAG VA

• Business process designs

• Solution design and specs

• Scalability• Latency

Business Project20%

• Business process• Change mgmt.

• Documentation• Manuals

• Data dictionary and access control

Business Transform

Business Solution

80% 99.9%

Path to Autonomy: Factory AI LevelsTaken the self-driving car approach, we defined levels for AI solutions

Upstream Downstream FieldProcesses Test Final Test


Level 1

Defect classification

Defect Class = f(Test Data)

Level 2

Myopic sample rate

Sample Size = f(Defect Class)

Level 3

Dynamic sample rate

Sample Size = f(Sample Size, Upstream process data)

Dynamic Process

parametersLevel 4

Process Parameters = f(Sample Size, Upstream process data)

Level 5

Dynamic specs Specs= f(Process Parameters, Final test data, Field data)


Magnetic Head Fabrication. 3-Dimensional, Complex Fabrication at Nanoscale

Manufacturing process with extremely high # of unit operations; with high “device” counts/wafer

Nanoscale 3D fabrication process requiring tight tolerance controls

Functional geometries <20nm

Material layers down to 3-4 atoms thick

Perspective: Individual Magnetic Heads on a Dime

A nanometer is to

a tennis ball as a

tennis ball is to the

Earth.


Autonomous Factories – How we get there?

Think Big – Data Analytics OrganizationPromote an innovation culture by flattening the organization, embracing organizational communities and

networks, creating a platform for progressive thinkers to interlock and exchange ideas, and providing

learning opportunities for new technologies with hands-on experience and executive involvement in

surfacing Think Big ideas.

Start Small – Business Processes and Project ManagementDrive proof of concept (POC) projects with an established business process. Set focus on getting buy-in

from SMEs that proposed solution will help them do more with less -- better quality, yield, throughput,

cost, etc. There is nothing more suicidal for an initiative than trying to do everything, all over the place and

causing more work and confusion for SMEs before demonstrating clear benefits.

Scale Fast – Data InfrastructureEstablish end-to-end, standard and open digital platforms for targeted business processes so that we

don't have to redo infrastructure for every successful POC and we avoid silo platforms for business

initiatives (over 1000 shop-floor system in 11 plants). We have to implement and maintain an infrastructure that

can provide seamless scale-ability for the on-going stream of new solutions.


Autonomous Factories – Shall we be scared?

On May 11, 1997, Deep Blue defeated Gerry Kasparov, the world champion

in chess. The step Deep Blue made during the game taking Kasparov by

surprise, was a bug in the software. Kasparov saw that step as the brilliance

of super intelligence and he lost focus on the game…

"What about looking for a positive side? Now we have new intelligent

machines, and they will be taking over I would say more menial aspects

of cognition. So maybe it will help us to elevate our lives toward

curiosity, creativity, beauty, joy so there are other things that we can do

if we move to the next level of the development of our civilization.“

Gerry Kasparov

InsightsMachine Learning

PrecisePredictions

Real-TimeResults

Protection

Automation

Anywhere

Anytime

Analytics

ContextAware

Delivering the possibilities of data

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