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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
30 day access
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Institute member access
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WEBINAR SERIES 2016
B2B PULSE
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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
www.isbm.org | 814.863.2782
WEBINAR SERIES 2016
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