WEBINAR SERIES 2016

THE INTERNET OF THINGS

Presented by: WESLEY JOHNSTON, CBIM Roundtable Professor of Marketing and Institute Distinguished Research Senior FellowED CROWLEY, Executive-in-Residence, CBIM

YOUR PRESENTERS AND HOST

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WESLEY JOHNSTONCBIM Marketing Professor,

Georgia State University

WEBINAR SERIES 2016

HELENE MATHERNInstitute Director

ED CROWLEYCBIM Executive-in-

Residence

HANDOUTS & RECORDING

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• A link to the handouts and recording will be sent to all attendees following the webinar

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WEBINAR SERIES 2016

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WEBINAR SERIES 2016

The Internet of Things (IoT) -Big Data and Predictive Analytics

Wesley J. Johnston, PhDISBM Fellow

Director, Center for Business and Industrial MarketingGeorgia State University

Atlanta GA, USA

Ed CrowleyExecutive in Residence, CBIM

CEO PhotizoLexington Kentucky

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IoT

Poll Question #1

• What impact is the IoT having on your business?– None – I’m not seeing any impact– Some – It’s having an impact but not really a

significant one– Moderate – It is having an impact, it’s changing our

behavior– Significant – We are actively reacting because the IoT

is having a significant impact– Disruptive – It’s changing our fundamental business

model and forcing radical change in our business

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IoT

Gartner there will be nearly 26 billion devices on the Internet of Things by 2020. According to ABI Research more than 30 billion devices will be wirelessly connected to the Internet of Things (Internet of Everything) by 2020

ImplicationAmount of information managed by enterprise data centers will grow by 50 times this decade

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Enablers

• Tagging– RFID – Near field communication

• Barcodes

• Direct – Microprocessors– Sensors

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Examples

• Pratt and Whitney– Jet engines

• Numerous sensors• 500 gigabytes per engine per Atlantic crossing

– Predict 97% of engine maintenance events– Predict 100% of incidents requiring turn off

• SKF– Intelligent ball bearings

• Sense temperature• Oil viscosity• Use to manage manufacturing ecosystem

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The big problem “Big Data”• Big Data is used in the singular and refers to a

collection of data sets so large and complex, it’s impossible to process them with the usual

databases and tools. Because of its size and associated numbers, Big Data is hard to

capture, store, search, share, analyze and visualize.

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(Shorter) Definition

– Big Data has 3 characteristics• Volume – how much data

• Velocity – how fast that data is processed

• Variety – the various types of data

– Big Data is not a single technology but a combination of old and new technologies that helps companies gain actionable insight especially for predictive analytics

– The missing ingredient in many application attempts so far is Value

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Smarter analysis –precise analysisPredictive analytics

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Traditional analytics versus predictive analyticsWhat happened versus what will happen

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Company Example – Wal-Mart• Walmart has 8,500 stores in 15

countries, under 55 different names. The company operates under the Walmart name in the United States, including the 50 states and Puerto Rico. It operates in Mexico as Walmex, in the United Kingdom as Asda, in Japan as Seiyu, and in India as Best Price. It has wholly owned operations in Argentina, Brazil, and Canada. Walmart's investments outside North America have had mixed results: its operations in the United Kingdom, South America, and China are highly successful, whereas ventures in Germany and South Korea were unsuccessful.

• A Wal-Mart “supercenter has 120,000 – 140,000 SKUs and takes inventory manually 3 to 4 times daily

• Information is sent by satellite to Wal-Mart headquarters and then forwarded to regional distribution center for restocking of stores in 24 to 48 hours

• 85% of merchandise is automatic replenishment

• Wal-Mart also has a walmart.com Internet distribution and sales arm

• RFID, and mobile technologies will be important to Wal-Mart’s future strategy

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Converging Evolution is Driving Significant Business Model Impact

Business Model Impact

PredictiveAnalytics

Big DataIoT

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EnablersRFID Tags

NFCDigitized Devices

Ubiquitous Connectivity

Poll Question #2

• How are you using predictive analytics?– We are not planning on using predictive analytics

in the next 12 months.– We are actively exploring how we can use

predictive analytics and will be using it in the next 12 months.

– We have a pilot project or are developing a predictive analytic application.

– We already have a predictive analytics application which we are using in our business.

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Customer Engagement Predictive Analytics

Providing a complete, easy-to-shop, assortment of products the consumer wants

Maintaining high in-stock levels of the required assortment

Communicating product benefits and value through advertising and price incentive

Developing and introducing customized products to meetspecific consumer needs

Efficient StoreAssortments

EfficientReplenishment

EfficientPromotion

Efficient Product Introductions

Better:ProductsQualityAssortmentsIn Stock ServiceConvenienceValue

GreaterConsumerSatisfaction

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Operations Optimization Predictive Analytics

Improve customer satisfaction

Reduce failures, maximize performance

Optimize asset availability and life

Lower risk exposure

Decrease loss of service

Optimize labor and operations costs

Decrease planned and unplanned maintenance

Optimize workforce productivity

Recover lost revenue

Optimize Asset Actions

Accurately ReplenishConsumption ItemsPredict Individual

FailuresIncrease Production

Yield

Optimize service

Reduce cost

Improve quality

Improve Customer Satisfaction and Reduce OperatingCosts 19

From Reactive to Predictive

•Use a predefined lifetime for replacement

•Frequent unexpected failures leading to customers’ frustration

•Adaptively raise alert based on the actual condition of the product and environment

•Focused on critical event prediction

PREDICTIVE MAINTENANCE1.Anomaly detection: How to classify the present condition into good and bad

2.Change-point detection: How to recognize change-points of the system

For example Predictive Maintenance and Quality enable the transition from staticmaintenance models to dynamic, condition-based maintenance models.

Time-Based Maintenance

Condition-Based Maintenance

Source: IBM 20

Predictive Analytics Process

Source: IBM

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Integration Bus

End User Reports, Dashboards, Drill Downs

High volume streaming data

Telematics, Manufacturing Execution Systems,

Legacy Databases, Distributed Control Systems

Enterprise Asset Management Systems

(Maximo)

Analytic Datastore(Pre-built data schema for storing quality, select machine and prod data, configuration)

PredictiveAnalytics

DecisionManagement

BusinessIntelligence

An Example Predictive Analytics Architecture

This is one very simplified example of a Predictive Analytics Architecture – IBM’s PMQ (Predictive Maintenance and Quality).

Source: IBM

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Just in Time Toner Use Case

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Situation Today:• Printer generates low toner alert• Toner cartridge is shipped automatically• Toner is replaced before cartridge is empty wasting

up to 25%

Fuel Analogy:• Car alerts to quarter tank• You can only buy a full tank of gas• The rest of the gas is wasted

Solution:• Utilize predictive modeling to determine optimal ship

date• Reduce the amount of toner waste• Improve margin for fleet service providers

Impact:

• $30+ cost savings per device (avg).

• Typical OEM has 1M devices under management

• $25M+ per year annual cost savings

• Tool cost - $3M per year

How the System Works

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Fleet Management Software

Vendor Monitors and Manages Fleets

of Devices for Customers Ships Supplies to Customer

Installed Base of Devices

Can be 100,000’s or even millions of devices

ModelIBM PMQ Platform

A device says, “I am running low on a supply item (but not empty)” - supply

item still has 30-40% of capacity

Photizo model uses Predictive Analytics and Machine Learning to say… when will THIS supply item be empty based on how THIS device is being used and

its environment?

Provides a “ship date” for the supply

item

Alerts vendorwhen it’s time to ship supply item

Vendor ships supply item, it arrives just before the supply

item is empty!

Software provides key metrics on each device and each supply item.

Foundations for Building A Predictive Analytics Capability or Solution

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Technology• Predictive analytics

tools

• Data integration tools

• Platform for running the tools

Industry Expertise• Comprehensive

ecosystem understanding

• Knowledge of industry processes, pain points and use cases

Experienced Analytics Team• Data Architect

• Data Scientist

• Business Analyst

• Solution Architect

Defined Process• Use case analysis

• Data suitability analysis

• Solution development & deployment

• Data assessment methodology

Wesley J. Johnston, Ph.D.Director, CBIMwesleyj@gsu.edu

Edward A. CrowleyExecutive in Residence, CBIMeacrowley@photizogroup.comCEO Photizo Group, Inc.

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THANK YOU

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WEBINAR SERIES 2016