Welcome

Data Innovation at the Speed of LITE: How Lumentum is driving a zero defect culture from engineer to executive

Stephanie Vierus

Data Scientist

Lumentum

# T C 1 8

Overview

Lumentum Lumentum (NASDAQ: LITE) is a market-leading manufacturer of innovative optical and photonic products enabling optical networking and commercial laser customers worldwide.

SpeakerStephanie has over 18 years of engineering experience and has spent the past six years dedicated to supply chain data analytics. Previously, Stephanie wrote software for mathematical algorithms in high performance computing, and firmware for flagship supercomputers.

What are we going to talk about?

AgendaOur first year of transformation – First Pass Yield

Utilizing data to drive a zero defect culture

Continuing the journey

Questions and Answers

Data Innovation @ Speed of LITEOur first year

Let’s talk about where we started

Datacom Transmission & Transport

Commercial Lasers 3D Sensing

October 2017Report Mechanisms

Manual and taking months to generate static reports Inconsistent without a standard measurement methodologyHidden in emails, share points, and desktops

Data Landscape Diverse product sets spanning multiple markets Various test platformsGlobal with 8 manufacturing locationsDisparate databases, 30 and counting

DemandsCustomers requesting real time data analytics!

Introducing a new way: First Pass Yield1. Standard definition and certified views updated at least daily

2. Consistency and linkage for single source of truth CEO to Engineer

3. Focused and appropriate details for each user role

Executive Business Unit Engineering & QualityStandard measure for all segments Multiple product lines Details to drive action

Pre-Tableau Tableau

Frequency Once a month (manual) Twice daily (automated)

Source Static report generate, outside of process Connected to test systems, embedded in process

Definition Subset of population Entire population

Data Grain Aggregated values, with limited attributes Unit serial number for a given test run, with all attributes available

Access Share Point, Emails, Meetings Tableau Online

Effort Preparing reports Acting on data

Next Steps Follow up with engineer Drill into data, alert engineers, extend VIZs

Result

Impact of Change

Engage a Company

Enable Users

• Tableau self based training

• Host Tableau Day

• Boot Up Sessions to groups

• Data Dictionaries

Tableau Site

• Architecture of flow

• Embed User Guides into VIZ

• Enable easy navigation for site

• Control certified VIZs & data sources

Monitor / Support

• Monitor Track user visits

• Ongoing expansion of content

• Partner with Kaizen Promotion Office

Data Innovation @ Speed of LITEDriving a zero defect culture

Building a data culture

Data analytics is a team sport

All three parties must be present in order to create successful insights

As a team, we strive to bring insight into

• manufacturing processes

• customer experiences

• business questions

• ad hoc analysis

• data exploration

Data Steward

•Owns & collects data

•Ensures correctness

•Provided access

•Optimizes bottlenecks

Subject Matter Expert

•Understands process

•Puts data into context

•Understands the product

• Feels pain points

Tool Expert

•Helps Visualize Data

• Standardizes metrics

•Drives Action from data

•Builds tools to enable organization

What had to be built for the data to flow?

Tableau Online

Licenses

Collaborate with data stewards

and subject matter experts

Subject matter experts identify

what they need

Data Stewards provide

access the data

sources

Data blending,

calculations, and viz

generations

Collaborate with the subject matter

experts/ gain insights

Automate dashboards

Dig deeper

A perfect pair: data analytics and Kaizen

• Just providing data to a new set of users is only half the battle

• Pairing the power of analysis and Kaizen tools to utilize the data generates results

1. Identify yield issue with new

dashboard

2. Setup Kaizen team, use PDCA

methodology

3. Monitor Results –zero defect is

possible

In one year what type of success can there be?

• 80%+ Data Coverage off revenue generating products

• Double digit yield improvement

• Data to the people!

• Reached 100% of executives & 30% of engineers

• Allowed key customers & CMs to access to their VIZs

• Removed report generation tasks

• Single source of truth

• CEO, engineers, customers all talking about data

• Quality metrics integrated into employee bonuses

• Continuous improvement with better goal setting

Business Unit Yield Improvement

Datacom 24%

Transmission & Transport 18% - 23%

Commercial Lasers 20%

3D Sensing 26%

Better Goal Setting : Bad Tester Detection

Data Innovation @ Speed of LITEContinuing the journey

Next Generation Tools

• Wafer Modeling• Bullseye

• Outer Ring

• Block

• Data Driven Alerts• Parametric

• Yield Loss

• Test escapes

• Idle equipment

• Statistical Controls• SPC

• Bad Tester

• Tool Interaction affects

What’s Next

• Scale

• Tackle the harder problems

• More advance analytics

• Empower and grow data culture across the company

Questions & Answers

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What’s Next

• Scale

• Tackle the harder problems

• More advance analytics

• Empower and grow data culture across the company

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var pd = require('pretty-data').pd;

var xml_pp = pd.xml(data);

var xml_min = pd.xmlmin(data [,true]);

var json_pp = pd.json(data);

var json_min = pd.jsonmin(data);

var css_pp = pd.css(data);

var css_min = pd.cssmin(data [, true]);

var sql_pp = pd.sql(data);

var sql_min = pd.sqlmin(data);

Sample Code

var pd = require('pretty-data').pd;

var xml_pp = pd.xml(data);

var xml_min = pd.xmlmin(data [,true]);

var json_pp = pd.json(data);

var json_min = pd.jsonmin(data);

var css_pp = pd.css(data);

var css_min = pd.cssmin(data [, true]);

var sql_pp = pd.sql(data);

var sql_min = pd.sqlmin(data);

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