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

BRANDON KOHBUSINESS DEVELOPMENT MANAGER, INTEL

* 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%

ABOUT REVOLUTION ANALYTICS

•

•

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

Delivering Value

Big Data Predictive 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/