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THE INTERNET OF THINGS: ENABLING PREDICTIVE ANALYTICS IN
MANUFACTURING
Bill Jacobs
VP, Product Marketing
Revolution Analytics
Bill Jacobs
VP Product Marketing & Field CTO
Revolution Analytics
Brandon Koh
Business Development Manager, Internet of Things
Intel Corporation
Define Just Which IoT We’re Talking About?
Why Does IoT Matter?
Intel’s Experience
Revolution Analytics’ Contribution
Your Potential Experience
For more information…
In your “Day Job” – are you:
A data scientist or data modeler?
A software developer?
An IT architect or administrator?
A manufacturing or quality engineer?
An operations professional or executive?
A business or financial analyst?
Other?
The Gartner Hype Cycle for the Internet of ThingsAvailable from here: http://www.microsoft.com/en-us/server-
cloud/internet-of-things.aspx
Or directly from Gartner:
http://www.gartner.com/technology/reprints.do?id=1-
27LJLAK&ct=150119&st=sb
The Internet of Consumer Things
Revolutionary
Vast New Opportunities
Evolving in Pockets
The Internet of Industrial Things…
Not New
Builds Upon Rich Legacy of Automation
… Yet Exploding.
Kaizen• Kaizen – “Change for Good” – Toyota 1936
Deming• W. Edward Deming Prize – First Awarded 1951
SQC• Statistical Quality Control – 1970s
SPC• Statistical Process Control - 198
APC• Advanced Process Control (Semiconductor) 1990s
6ơ• Six Sigma – 1990s
IoT• IoT - Big Data – Predictive Analytics
"PDCA Cycle" by Karn-b - Karn G. Bulsuk (http://www.bulsuk.com). Originally published at http://www.bulsuk.com/2009/02/taking-first-step-with-pdca.html - Own work.
Originally developed for Taking the First Step with PDCA. Licensed under CC BY 3.0 via Wikimedia Commons -
http://commons.wikimedia.org/wiki/File:PDCA_Cycle.svg#/media/File:PDCA_Cycle.svg
Where Do You See Advanced or Predictive Analytics and IoT Delivering Value?
Process Understanding and Optimization?
Failure Reduction, Quality Improvement?
Supply or Demand Chain Optimization?
Productivity or Resource Optimization?
Increased Predictability of Supply Chains and Customer Demand?
Automated Maintenance?
Other?
Check out Kuka’s Automated Manufacturing at:
https://www.microsoft.com/en-us/server-cloud/customer-stories/KUKA-robotics.aspx
* Source: http://newsroom.intel.com/community/intel_newsroom/blog/2014/09/29/intel-and-mitsubishi-
electric-collaborate-to-create-next-generation-factory-automation-systems; Sept 29’2014
Intel’s Assembly / Test –sensors and analytics
help maintain productivity*.
Measured
Benefits:
$9M/ year
* Source: https://www-ssl.intel.com/content/www/us/en/internet-of-things/blueprints/iot-business-value-manufacturing-blueprint.html
Small Big Data Cluster
Localized Industrial
Data Center
Other IDCs at other factories
Enterprise DATA CENTER
B2B CloudManufacturing Network
Infrastructure
On-premise Server Platform;
Edge Server Gateway of a manufacturers’ Private Cloud
Enable Operational Technology on the manufacturing shop floor
High availability, Redundancy
Examples of OT Technologies:-
Manufacturing Data Store
Manufacturing Data Analytics
Secure end to end closed loop control operations
Sensors
Actuators
Meters
Legacy
Systems
Smart
Machines
Human/
Machine Interfaces
Communication
Infrastructure
Gateway
Bolt on: Connect existing systems
Aggregate sensor data; Actuates physical world
Analyze data locally: filtering, real-time response
Provide security from edge to cloud to deliver trust, reliability
IOT Pilot Implementation
SERVICES CREATION (API-Enabled)
Root Cause
Analyses
Classification /
Regression
Time Series
Forecasting
Analytics Deployment Layer
RDBMS/row-store database for
OLTP workloads
Virtualization
Existing
Factory
Applications
consolidation
into VMs
ExternalExternal
CustomersEnterprise
Users
API API
Manufacturing Network
Enterprise Network
Equipment
Security
SQL
Yield Data
Relay sensor counts
Overview
Parallel CPU Tester,
execute test programs
into test signals.
Problem
Some yield loss are non-
genuine caused by hardware
failure (relays) during
test process
Solution
Unit test results multi
variables and real time relay
counts are correlated to
predict “time to failure” of
the component (relays).
Results
Non-genuine yield los
reduced by 25%
20% spare spending
reduction
50% reduction in
maintenance time
Machine Data
(Motor Missteps, Missing ball error)
New sensors data
Overview
BA is an assembly process
that attaches ball
interconnect to silicon
packages.
Problem
Missing Ball assembly causes
faulty material. Equipment is
a legacy tool, limited visibility
Solution
Three types of analytics:
Monitoring. Time series
predictions of motor missteps
and pressure sensors data ;
Regression to estimate
relationship between
variables and predict missing
ball occurrences.
Results
50% reduction in yield loss
related to missing solder balls
Increase Process stability
after conversion
22
Ensemble of machine learning models used
Features
Pass
Or
Fail
ImagePre-processing
Overview
A machine vision equipment
is a module that screens units
into good and marginal units.
Problem
Often high quality marginal
rejects exist. Need highly
automated method to look
within marginal units in a
much shorter timeframe.
Solution
Image classification
are performed with for faster
determination of true
passes/ rejects.
Results
10 times faster results.
Headcount efficiency
improvement of 10%
Who We AreOnly provider of commercial big data big analytics platform based on
open source R statistical computing language
Our Software DeliversScalable Performance: Distributed & parallelized analyticsCross Platform: Write once, deploy anywhereProductivity: Easily build & deploy with latest modern analytics
Our Services DeliverKnowledge: Our experts enable you to be expertsTime-to-Value: Our Quickstart program gives you a jumpstartGuidance: Our customer support team is here to help you
Global Industries Served
Financial Services
Digital Media
Government
Health & Life Sciences
High Tech
Manufacturing
Retail
Telco
Customers
300+ Global 2000
Global Presence
North America / EMEA / APAC
Our Vision:
R becomes the de-facto
standard for enterprise
predictive analytics
Our Mission:
Drive enterprise adoption of R
by providing enhanced R
products tailored to meet
enterprise challenges
Support & Services
Commercial Support Programs
Training Programs
Professional Services
Community Programs
Academic Support Programs
Contributions to Open Source R
Open Source Extensions
Sponsorship of R User Groups
Software Products
Stable Distributions
Broad Platform Support
Big Data Analytics in R
Application Integration
Deployment Platforms
Agile Development Tooling
Future Platform Support
is….the only big data big analytics platform
based on open source R
the defacto statistical computing language for
modern analytics
Training
• On-Site or Remote
Classes
• Classroom or Self
Paced
• Standard or Tailored
• Certification Testing
Project Services
• Analytics Strategy
• Analytics
Architecture
• Custom
Development
Projects
• Application
Migration
• Package
Certification
Quick Start Services
• Pre-production
• Jumpstart value
• Combines software,
training, and
services
• Proof-of-Concept
Post Go-Live Support
• Technical Account
Management
• On-going Training
• Staff Augmentation
Finance Insurance
Healthcare & Pharma Digital Economy Analytics Service Providers
Manufacturing & High Tech
Deployment / Consumption
Data / Infrastructure
Advanced Analytics
ETL
SI / Service MSP / DSP
Server Farm, EDW,
Data Lake or Cloud
34
Business Analysts(Alteryx, Tableau, Qlik, Cognos,
Microstrategy, Datameer etc.)
Power Analysts(R Studio, DevelopR, etc.)
Line of Business users(Analytic Apps, Rules Engines, etc.)
Analytics Consumers
Scores
Math Servers and
Clusters
Data
Models
Execution
DataModelsExecution
VisualizationIngest
Scored Data
Structured Data
Big Data
• Transformation
• Aggregation
• Exploration
• Modeling
• Model Evaluation
• Data Scoring
Sensors
Machines
Data Suppliers
Legacy Sources
Data Sources
EDW ERP/MRP
Sensors
Machines
Data Suppliers
Legacy Sources
Data Sources
EDW ERP/MRP
Server Farm, EDW,
Data Lake or Cloud
35
Business Analysts(Alteryx, Tableau, Qlik, Cognos,
Microstrategy, Datameer etc.)
Power Analysts(R Studio, DevelopR, etc.)
Line of Business users(Analytic Apps, Rules Engines, etc.)
Analytics Consumers
Math Servers and
Clusters
Data
Models
Execution
DataModelsExecution
Ingest
Scored Data
Structured Data
Events Stream
Processing
ModelsEdge
Computing
Scores
VisualizationBig Data
• Transformation
• Aggregation
• Exploration
• Modeling
• Model Evaluation
• Data Scoring
Enhanced Open Source Delivers: Revolution R Enterprise Delivers:
• Simplicity
• Speed
• Capability
• Speed
• Scale
• Stability
• Time-to-Results
• Compatibility
• R:• Broadly-used, scalable language
• Large, collaborative community
• Vast repository of tools and algorithms
• Broadens career opportunities
• Revolution R Enterprise:• Big Data capability
• Scales from workstations to Hadoop
• Transparent parallelism
• Cross platform compatibility
• Multi-platform architectures
• Supports Heterogeneous Architectures
• Eliminates Model Recoding
• Integrates With Major BI & Application Tools
• Run Analytics within the “Data Lake”
• “Good Citizen” in Shared Platforms
• Commercial Support Reduces Project Risks
• Quick Start Programs to Speed Results
• Future-Proof Platform Continuity
• Predictable Time To Results
• Viable Alternative to Legacy
• Future-Safe Platform
• Simplified Licensing
• Lower Staffing Costs
Smart Supply Chains:
Risk Analysis
Delivery Optimization
Supplier Assessment
Smart Manufacturing
Predictive Maintenance
Quality Improvement
Anomaly Detection
Asset Optimization
Root Cause Analysis
Machine Learning
Smart Demand Chains
Demand Planning
Customer Experience
Social Sentiment
Warranty Analytics
•
http://www.revolutionanalytics.com/products
http://www.revolutionanalytics.com/revolution-r-enterprise
http://www.revolutionanalytics.com/big-analytics-hadoop-and-edws
•
http://www.revolutionanalytics.com/big-analytics-hadoop-and-edws
http://www.revolutionanalytics.com/sites/default/files/teradata_revolution_analytics_white_paper.pdf
•
• Microsoft’s Internet of YOUR Things: http://www.microsoft.com/en-us/server-cloud/internet-of-things.aspx
• Microsoft Azure Machine Learning: http://azure.microsoft.com/en-us/services/machine-learning/
•
https://www.datacamp.com/courses/big-data-revolution-r-enterprise-tutorial
https://www.coursera.org/course/rprog
•
http:/www.revolutionanalytics.com/whitepaper/delivering-value-big-data-revolution-r-enterprise-and-hadoop/
http://www.slideshare.net/RevolutionAnalytics/indatabase-analytics-deep-dive-with-teradata-and-revolution
•http://mran.revolutionanalytics.com/download/
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