The Brilliant Factory:

Optimize, Predict, and Prevent David Sweenor | Global Product Marketing Manager | Advanced Analytics

@DavidSweenor

Embed Analytics Everywhere

Optimize, Predict, and Prevent

Data

Supplier

Sensor

Factory

Design

Data

Ble

nd

ing

Machine Learning

& Data Mining

Machine learning crunches data to build a

predictive model

Pre

dic

tive

Mo

de

l De

plo

ye

d

Predictive Model P

red

ictiv

e S

co

re G

en

era

ted

The predictive model acts on unseen”

data

Become scrap

Be out of

compliance

Trigger an

alarm

Become and

outlier

Exhibit

abnormal

behavior

Likelihood to…

Breakdown or

become

defective

The output is a score

An

aly

tics

Em

be

dd

ed

into

Bu

sin

es

s

Dashboards

Mobile

Web

Process improvement

Direct Mail Campaign

Data

Mailing List

1M prospects

Assumptions

• $2 to mail each prospect

• 1 out of 100 will buy

• $220 profit for each response

Catalogue

Mail Cost $2

Results

• Profit = Revenue - Cost

• =($220*10,000) – (1M * $2)

• =$200,000

Assumptions

Analytical Model output:

• 25% of the entire list are 3x more likely to respond

The Value of a Prediction

Mailing List

250K prospects

Catalogue

Mail Cost $2

Results

• Profit = Revenue - Cost

• =($220*7,500) – (250K * $2)

• =$1,150,000

• 5.75x improvement by mailing fewer people

Adapted from Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die by Eric Siegel

Manufacturing Analytics

Discover defects, improve yields, monitor suppliers, optimize processes and reduce costs

• Manufacturing Optimization

• Predictive Failure Analysis

• Root Cause Analysis

• Process Optimization

• Statistical Process Control

• R&D

• Predictive Maintenance

• Design of Experiment

• Product Traceability

• Six Sigma

• Production Process

The Internet of Things Impacts All of Us

Why are Analytics essential to IoT?

"Data is inherently dumb, it doesn't

actually do anything unless you know

how to use it and how to act on it,

because algorithms are where the real

value lies; algorithms define action,”

Source: Gartner Symposium Nov 2015 in Barcelona, Peter Sondergaard, senior vice president and head of

research at the analyst house http://www.v3.co.uk/v3-uk/news/2433966/algorithms-key-for-turning-dumb-data-into-real-business-benefits

Business need • Differentiate a commoditized business and

product to enhance margins and react to an

offshore competitor seizing market share.

Data required for analysis

• Historical Equipment data - performance

• Sensor data – temperature, maintenance

• Real time social data - Sentiment data

analysis

• Geospatial data – Lat/Long position data

Solution and results

• Aligning social sentiment with equipment

performance for higher quality

• Differentiated value proposition

• Higher margins

• Predictive performance and service

Creating Value with a Social French Fryer

Industrial Automation and Manufacturing

.

Building Automation,

Energy, Utilities

Healthcare Life Sciences

Transport Logistics Retail

Cost Savings via

Automation

43%

Opportunity for

Innovation

48%

Process

Improvements

50%

Process

Improvements

38%

Opportunity for

Innovation

40%

Cost Savings via

Automation

52%

Cost Savings via

Automation

35%

Opportunity for

Innovation

38%

Demand From

End Users

40%

Need Competitive

Advantage

33%

Need for Faster

Decision Making

41%

Opportunity for

Innovation

43%

Process

Improvements

42%

Cost Savings via

Automation

46%

Opportunity for

Innovation

57%

The impact of analytics on IoT

The ability to collect data will

always outstrip the ability to

transmit and store it

Pushing Analytics to the Edge

Big Data Streams from Connected Cars

• Cars – connected car data, network, contextual

• OEMs & Dealerships – vehicle diagnostics, in-car service consumption

• Insurance companies – aggregated/anonymized driving data, incident data

• Fleet customers – fleet performance, compare against competition

Big Data Streams from Connected Cars – con’t

• Federal / State DoT – breakdown data, accident data, environmental data

• Smart Cities – real-time traffic flow, incident alert, parking

• Advertisers – customer/passenger demographics

• Other B2B – content usage, frequency, length, etc.

Are you moving data

to and fro?

There is a better way!

Data Flow

Gateway

Edge Analytics Core Analytics

Cloud

Cloud

Data center

Device/Sensor Analytics

Internet of Things – Edge Analytics

Eliminate Unnecessary Data Movement

Analytic Transport

Date/Time

Trans type

Velocity Trigger

Public or Private Cloud

Statistica

Analytic Workflow Atom Export Models as:

Java, PMML, C, C++, SQL

Oracle Hadoop Hive on Spark Teradata In-Database Analytics

How does work get done in your organization?

Image Source: IBM/Vermont Historical Society

Image Source: Google Maps

How many people keep reinventing the wheel?

Distribute analytic output to LOB

Network (Entity) Analytics Airport Predictive Maintenance Dashboard

Real-time streaming Process Flow Visualization

Democratizes Analytics to the Entire Organization

Data scientists

Use the global community for analytic modules

Build advanced analytic flows once; reuse and share

Empowered with in-database processing

Engineers

Automated data preparation

Wizards and templates with reusable configurations

No knowledge of SQL or databases required

Operators

Embed analytics in LOB apps

Recipes & Quick Starts

CI driven by shortage of expertise, thus a greater need for democratization and decentralization

Promote and Distribute Best Practices

Distribute & share analytics across the world

Take your math to where the data lives

Avoid duplicate infrastructure

Site 1 Tulsa, OK

Site 3 Sao Paolo, Brazil

Site 4 California

Site 2 Taiwan

Analytics Platform

Regional USA energy company turns to predictive analytics in pursuit of cleaner air and regulatory compliance.

Power utility plant optimizes coal-fired

cyclones without infrastructure retrofits

Business challenge The company wanted to use complex streaming data and existing

control technology to address competing goal functions and achieve

significantly better operations without the need for expensive

infrastructure projects.

Solution Statistica monitors and analyzes complex power plant operations in

real time and identifies specific settings for multiple parameters that will

reliably produce desired performance of high-dimensional, continuous

processes.

Results • Significantly improved & stabilized low NOx operations for cyclone

furnaces

• Optimized robust performance of 340 Mega Watt Cyclone with OFA

ports

• Optimized simultaneously for competing goal functions: minimum

emissions, maximum efficiency, and greatest reliability

• Fully documented by Electronic Power Research Institute (EPRI)

Published: June 2016 | Expires: June 2018

Read the EPRI case study report >

“We quickly identified the right claims to investigate and saved

$500K in warranty chargebacks.”

Automotive tech manufacturer increases efficiency

of warranty scoring and defense against claims

Business challenge In the warranty of mechatronic systems and electric motors, manual claims

classification required over 50% of engineers’ and analysts' time on data

retrieval, alignment, and preparation. Also, the company was unable to identify

quality issues early enough to pursue proactive process improvement.

Solution Dell Statistica’s auto-classification solution uses text mining and concept-

extraction; builds prediction models for each failure classification; builds a

workflow with rules to classify narratives to highest-probability failure mode;

and deploys for automatic scoring of new warranty narratives.

Results • Enhanced accuracy due to automatic text classification

• Enables proactive and preventive measures instead of reactionary

• Provides competitive advantage and drives down warranty costs

“Defending against a warranty claim, we

needed to analyze several years of data

in a short period of time, impossible

without Statistica. We quickly identified

the right claims to investigate and saved

$500K in warranty chargebacks.”

National Warranty Manager

Published: March 2015 | Expires: March 2017

When your reputation is built on the highest standards of quality,

performance and durability, Statistica shines.

Solar tech producer drives quality with predictive

analytics

Business challenge Over 10,000 streaming, automated parameters required real-time monitoring and

analysis to meet ever-higher demands of product quality—and to anticipate

manufacturing issues—in this extremely competitive industry.

Solution Statistica Enterprise integrated easily with the company’s existing MRP system

and offered practical algorithmic capabilities in a scalable, web-enabled

platform that maintains performance in the face of increasing complexity.

Results • Optimizes manufacturing efficiency by enabling hundreds of end-users

and engineers to monitor and respond to mission-critical data

• Maintains company’s competitive edge through application of predictive

process monitoring for potential quality issues

• Supports real-time processes 24/7

“This technology has enabled [us] to stay

in business in the face of very strong

headwinds and competitive pressures.”

Director of IT

Major solar tech producer

Published: June 2016 | Expires: June 2018

“Statistica offers an empirical line of sight between what we do in assembly and its effect on finished product.”

Lower manufacturing costs and higher quality

through predictive analytics v. “tribal knowledge”

Business challenge Even with sophisticated data-collection, our customer sought to improve

quality and reduce product failures by replacing "tribal knowledge" with

additional empirical data analysis that would more accurately relate

equipment parameters to product performance.

Solution Using Data Miner to identify correlation of complex parameters to product quality

outcomes, we built models that enabled engineers to test “what-if” scenarios and

optimize multiple, competing outcomes (e.g., power v. fuel efficiency).

Results • Streamlined multiple processes, e.g., reduced trim balance problems 45%

• Replaced metrology equipment costs and reduced product cycle time

• Reduced time & personnel costs needed for product adjustments

• Increased throughput with reduced scrap and rework

Published: August 2016 | Expires: August 2018

Read the Quality Digest article >

By 2018 more than half of large organizations around the globe will compete using Advanced Analytics and proprietary algorithms, causing

disruption on a grand scale.

Source: Gartner, Inc., Magic Quadrant for Advanced Analytics Platforms, Lisa Kart, Gareth Herschel, Alexander Linden, Jim Hare, 9 February 2016.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

The Analytics of Things

• Reduce scrap and waste at the Edge

• Root cause at the edge – power, torque, pressure with constraints vibration and temperature with one metric in near real time

• Multivariate alarms with tens of thousands of parameters – send state changes back

• Edge filtering of outliers, alarms, and relevant history

• Pattern recognition on critical machinery – e.g. wind turbines and sound signatures

• Quality control thorugh edge based analytics

The Industrial IoT will transform and disrupt entire

industries while creating opportunities for new business

models

The ability to collect data will always outstrip our ability to transmit and store it pushing analytics to the edge

Statistica addresses some of the broadest set of analytic use cases including IoT Edge Analytics.

.

Key Takeaways

Embed Analytics Everywhere

Questions?

John K Thompson | GM of Advanced Analytics | @johnkthompson60

dell.com/statistica Questions?

David Sweenor | Global Product Marketing Manager | Advanced Analytics

@DavidSweenor

dell.com/statistica