Download The Brilliant Factory: Optimize, Predict, and Prevent

The Brilliant Factory:
Optimize, Predict, and Prevent
David Sweenor | Global Product Marketing Manager | Advanced Analytics
@DavidSweenor
Embed Analytics Everywhere
Optimize, Predict, and Prevent
Likelihood to…
Become scrap
Design
Machine Learning
& Data Mining
Machine
learning
crunches
data to
build a
predictive
model
Predictive Model
The
predictive
model acts
on unseen”
data
Predictive Score Generated
Sensor
Data Blending
Supplier
Predictive Model Deployed
Factory
Trigger an
alarm
Become and
outlier
Exhibit
abnormal
behavior
Breakdown or
become
defective
Analytics Embedded into Business
Be out of
compliance
Data
Process
improvement
Dashboards
Mobile
Web
The output
is a score
Direct Mail Campaign
Results
Data
Assumptions
• $2 to mail each prospect
• Profit = Revenue - Cost
Mail Cost
$2
• 1 out of 100 will buy
Mailing List
• $220 profit for each response
• =($220*10,000) – (1M * $2)
Catalogue • =$200,000
1M prospects
The Value of a Prediction
Results
Assumptions
• Profit = Revenue - Cost
Mail Cost
$2
Analytical Model output:
• 25% of the entire list are 3x
more likely to respond
Mailing List
250K prospects
Adapted from Predictive Analytics: The Power to Predict Who Will Click, Buy,
Lie or Die by Eric Siegel
• =($220*7,500) – (250K * $2)
Catalogue • =$1,150,000
• 5.75x improvement by mailing
fewer people
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
Creating Value with a Social French Fryer
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
The impact of analytics on IoT
Industrial
Automation and
Manufacturing
Transport
Logistics
Healthcare
Life Sciences
Retail
Process
Improvements
Opportunity for
Innovation
Demand From
End Users
Opportunity for
Innovation
52%
50%
43%
40%
57%
Opportunity for
Innovation
Opportunity for
Innovation
Need for Faster
Decision Making
Opportunity for
Innovation
Cost Savings via
Automation
40%
48%
41%
38%
46%
Process
Improvements
Cost Savings via
Automation
Need Competitive
Advantage
Cost Savings via
Automation
Process
Improvements
38%
43%
33%
35%
42%
Building
Automation,
Energy, Utilities
Cost Savings via
Automation
.
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, incar 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!
Internet of Things – Edge Analytics
Device/Sensor
Analytics
Edge Analytics
Core Analytics
Cloud
Data Flow
Cloud
Gateway
Data center
Eliminate Unnecessary Data Movement
Analytic
Transport
Statistica
Date/Time
Trans type
Velocity
Trigger
Analytic Workflow Atom
Export Models as:
Java, PMML, C, C++, SQL
Public or
Private
Cloud
Oracle
Hadoop Hive on Spark Teradata
In-Database Analytics




How does work get done in your organization?
How many people keep
reinventing the wheel?
Image Source: IBM/Vermont Historical Society
Image Source: Google Maps
Distribute analytic output to LOB
Network (Entity) Analytics
Real-time streaming
Airport Predictive Maintenance Dashboard
Process Flow Visualization
Democratizes Analytics to the Entire Organization
Data scientists
Engineers
Operators








Use the global community for
analytic modules
Build advanced analytic flows
once; reuse and share
Empowered with in-database
processing

Automated data preparation
Wizards and templates with
reusable configurations
No knowledge of SQL or
databases required
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
Site 1
Site 2
Tulsa, OK
Taiwan
 Take your math
Analytics
Platform
to where the
data lives
 Avoid duplicate
infrastructure
Site 3
Site 4
Sao Paolo, Brazil
California
Power utility plant optimizes coal-fired
cyclones without infrastructure retrofits
Regional USA energy company turns to predictive analytics in pursuit of cleaner
air and regulatory compliance.
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
•
•
•
Read the EPRI case study report >
Published: June 2016 | Expires: June 2018
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)
Automotive tech manufacturer increases efficiency
of warranty scoring and defense against claims
“We quickly identified the right claims to investigate and saved
$500K in warranty chargebacks.”
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
“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
Dell Statistica’s auto-classification solution uses text mining and conceptextraction; 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
Solar tech producer drives quality with predictive
analytics
When your reputation is built on the highest standards of quality,
performance and durability, Statistica shines.
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
•
“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
•
•
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
Lower manufacturing costs and higher quality
through predictive analytics v. “tribal knowledge”
“Statistica offers an empirical line of sight between what we do in assembly
and its effect on finished product.”
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
Read the Quality Digest article >
Published: August 2016 | Expires: August 2018
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
Key Takeaways
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.
.
Embed Analytics Everywhere
Questions?
John K Thompson | GM of Advanced Analytics | @johnkthompson60
Questions?
dell.com/statistica
David Sweenor | Global Product Marketing Manager | Advanced Analytics
@DavidSweenor
dell.com/statistica