Data & AnalyticsApplied to Test Strategy

Brad Waggle

Board Test Workshop

September 9th, 2014

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Michael Dell … http://www.dell.com/learn/us/en/19/power/ps2q14-20140230-michael

Here in Texas, everything’s big, so we just call it data.

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Context

Data usage behavior shift Facilitating the shift

Technology Stack

What can we do now that we couldn’t do before?

What can’t we do yet but are working on?

Agenda

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Test / Supply Chain Complexity 1000s of testers, 10’s-of-thousands of product IDs, 100’s of

thousands of component part numbers

Billions and Billions of rows of data (over years and years) Test and Repair

6.1B Measurement

45.5B Component

65.8B

And GROWING FAST!

Context

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In the past Small product sample Short time frame Solve point problems Lack of learning

Going Forward All products (n = all) All time frames Solve strategic problems Learn and prevent problems

Data usage behavior shift

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Migrating from: Excel based aggregation / manipulation Limited custom web pages IT driven support model Traditional database architecture

To: Best in class front-end analytics (Spotfire) User driven support model / self-service Big data architecture

Facilitating the shiftTest

Repair

Field

Component

Cost

Product

Qualification

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Migrating from: Excel based aggregation / manipulation Limited custom web pages IT driven support model Traditional database architecture

To: Best in class front-end analytics (Spotfire) User driven support model / self-service Big data architecture

Facilitating the shiftTest

Repair

Field

Component

Cost

Product

Qualification

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Big Data Cloudera Hadoop Hive / Impala

Traditional database MSSQL

Spotfire Analytics R Statistical Programming (TERR)

Technology Stack

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What can we do that we couldn’t do before?

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Detect technology & quality trends

Consistently apply strategies to products in the NPI Phase

Apply statistical models at scale

Use big data and analytics to:

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Detect Technology & Quality Trends Drove a major shift in test strategy

Multi-year view of a design / component margin Trend for 1 electrical test parameter (65 products, 6 years, 100M measurements)

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Consistently apply test strategies to a new product Drives the most cost effective test solution up front in the NPI cycle Launch Lean vs. launch and optimize

Test strategy dashboard that facilitates choosing the best test optimization strategy for a new product

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Apply statistical models / algorithms at scale Determine component variability vs. design margin with higher

confidence Reduce non-value-add tests

Cpk analysis to determine critical measurement parameters

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What can’t we do yet but are working on?

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Leverage unstructured / semi-structured data

Perform predictive modeling

Use big data and analytics to:

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Leverage Unstructured, Semi-Structured Data

ICT test log

Enhance

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Predictive Modeling

Mock up of linear regression attempting to correlate component types to overall product failure rates

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Spatial Analytics

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Questions?