THE INTERNET OF THINGS

Transcription

1 THE INTERNET OF THINGS Presented by: WESLEY JOHNSTON, CBIM Roundtable Professor of Marketing and Institute Distinguished Research Senior Fellow ED CROWLEY, Executive-in-Residence, CBIM WEBINAR SERIES 2016

2 YOUR PRESENTERS AND HOST WESLEY JOHNSTON CBIM Marketing Professor, Georgia State University ED CROWLEY CBIM Executive-in- Residence HELENE MATHERN Institute Director WEBINAR SERIES

3 HANDOUTS & RECORDING A link to the handouts and recording will be sent to all attendees following the webinar Institute member access B2bpulse.isbm.org 30 day access isbm.org/education/webinars WEBINAR SERIES

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5 The Internet of Things (IoT) - Big Data and Predictive Analytics Wesley J. Johnston, PhD ISBM Fellow Director, Center for Business and Industrial Marketing Georgia State University Atlanta GA, USA Ed Crowley Executive in Residence, CBIM CEO Photizo Lexington Kentucky Page 5

6 IoT

7 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 7

8 IoT Implication Amount of information managed by enterprise data centers will grow by 50 times this decade Gartner there will be nearly 26 billion devices on the Internet of Things by According to ABI Research more than 30 billion devices will be wirelessly connected to the Internet of Things (Internet of Everything) by

9 Enablers Tagging RFID Near field communication Barcodes Direct Microprocessors Sensors 9

10 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 10

11 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. 11

12 (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 12

13 Smarter analysis precise analysis Predictive analytics 13

14 Traditional analytics versus predictive analytics What happened versus what will happen 14

15 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 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 15

16 Converging Evolution is Driving Significant Business Model Impact Predictive Analytics Business Model Impact IoT Big Data Enablers RFID Tags NFC Digitized Devices Ubiquitous Connectivity 16

17 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. 17

18 Customer Engagement Predictive Analytics Providing a complete, easy-toshop, 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 meet specific consumer needs Efficient Store Assortments Efficient Replenishment Efficient Promotion Efficient Product Introductions Better: Products Quality Assortments In Stock Service Convenience Value Greater Consumer Satisfaction 18

19 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 Replenish Consumption Items Predict Individual Failures Increase Production Yield Optimize service Reduce cost Improve quality Improve Customer Satisfaction and Reduce Operating 19 Costs

20 From Reactive to Predictive For example Predictive Maintenance and Quality enable the transition from static maintenance models to dynamic, condition-based maintenance models. Time-Based Maintenance Condition-Based Maintenance 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 MAINTENANCE 1.Anomaly detection: How to classify the present condition into good and bad 2.Change-point detection: How to recognize change-points of the system Source: IBM 20

21 Predictive Analytics Process Source: IBM 21

22 An Example Predictive Analytics Architecture End User Reports, Dashboards, Drill Downs Predictive Analytics Decision Management Business Intelligence Analytic Datastore (Pre-built data schema for storing quality, select machine and prod data, configuration) Integration Bus Source: IBM Telematics, Manufacturing Execution Systems, Legacy Databases, Distributed Control Systems High volume streaming data Enterprise Asset Management Systems (Maximo) This is one very simplified example of a Predictive Analytics Architecture IBM s PMQ (Predictive Maintenance and Quality). 22

23 Just in Time Toner Use Case 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 23

24 How the System Works Software provides key metrics on each device and each supply item. Model IBM PMQ Platform 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? Fleet Management Software Provides a ship date for the supply item Alerts vendor when it s time to ship supply item A device says, I am running low on a supply item (but not empty) - supply item still has 30-40% of capacity 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 Vendor ships supply item, it arrives just before the supply item is empty! 24

25 Foundations for Building A Predictive Analytics Capability or Solution 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 25

26 Wesley J. Johnston, Ph.D. Director, CBIM Edward A. Crowley Executive in Residence, CBIM CEO Photizo Group, Inc. 26

27 THANK YOU WEBINAR SERIES 2016