Bringing the power of IBM

Watson IoT to the Edge with

Cisco

Dave Locke Senior Inventor IBM IoT Ecosystem Manager Connecting Things that Matter @DaveJLocke

Agenda

The Power of Data

IBM Watson IoT Platform

Edge processing

Use Cases

Summary

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IoT is Driving Digital Disruption Into the Physical World

Advanced Analytics

Product Lifecycle Mgmt

Cloud Computing

Pervasive Connectivity

Embedded Sensors

Creating New Products and Business Models

Smarter, safer cars

Health and fitness

Home and building

automation

Improving Operations and Lowering Costs

Predictive maintenance

Analyze and reduce risk

Factory automation

Driving Engagement and Customer Experience

Smarter, more profitable retail

Engaged events and venues

Apps that link the digital and

physical world around a brand

Accelerating advancements in technology… Are transforming every part of business…

Leveraging the data generated by digital technology provides intelligence to help us do things better, improving our responsiveness and ability to predict and optimize for future events

INTELLIGENT

Digital technologies (sensors and other monitoring devices) are being embedded into many objects, systems and processes

INSTRUMENTED

INTERCONNECTED

In the globalized, networked world, people, systems, objects and processes are connected, and they are communicating with one another in entirely new ways

Smarter Planet and the Internet of Things

Little Data Big Data

IoT Driving Forces…

Price Power conservation,

Energy Generation

Form Factor, Miniaturization

Connectivity,

Network

Drive Innovation Edge

Most IoT data are not used

currently. For example, only

1 percent of data from an oil

rig with 30,000 sensors is

examined. The data that

are used today are mostly

for anomaly detection and

control, not optimization and

prediction, which provide

the greatest value.

Analytics

Data • Cloud • Big Data • Analytics • Applications

IoT Device

Data

Traditional Data Processing Model

Traditional: Deliver Data to the Analytics

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Anatomy of an IoT Solution

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Watson IoT Platform

Sensors, Devices,

Gateways & Networks

Other

Data Sources

Weather

Map

01 0110 0010 001001

Devices Platform Applications

Other IoT platforms BMS

Asset

IBM Watson IoT Platform

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Third Party Apps Offerings

IBM Watson IoT Platform Connect Attach, Collect & Organize, Device Management, Secure

Connectivity, Visualization

IBM Watson IoT Platform Information

Management Storage & Archive, Metadata Management, Reporting, Streaming

data, Parsing and Transformation, Manage unstructured data

IBM Watson IoT Platform Analytics Predictive, Cognitive, Real-time, and Edge

IBM Watson IoT Platform Risk Management Security Analytics, Data Protection, Auditing/Logging,

Firmware Updates, Key/Cert Mgmt, Org Specific Security

Third Party Apps

The IBM Watson IoT Platform Everything you need to Innovate with IoT

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IoT requires the right capabilities applied to the right data for the right

results

Cognitive technology

enables deeper customer

engagement through

enhanced interactions and

automated discovery and

insights using machine

learning techniques

Predictive models are

created from historical data

to generate insight and

recommend actions before

situations cause business

disruptions

Real-Time analytics enables

monitoring and processing of

streaming data to enable

“perishable insights” and

automated decisions in near

real-time

Real-Time

Most machine data is

worthless about 1

second after it is

generated

Cognitive

IoT will rapidly

change our ability to

interact with

machines and

engage customers

Predictive

70% of the most

profitable

companies will

leverage predictive

analytics in 2016

In the cloud & at the edge

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Real-Time Watson IoT Platform Analytics Real-Time Insights

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Real-time

dashboard

Recommendations drive response in Maximo

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Sensors provide information about the device

1

Device

SCADA, historians

Data may be collected by a gateway device for connectivity or protocol translation

IoT Connect

IoT Analytics

Real-time data

Rules trigger an action, such as an alert, email, text message or a work order in Maximo

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Data drives real-time analytics and business rules

3 Data comes in through Watson IoT Platform Connect

2

Maximo

1

2

• Contextualizes device data

• Monitors streaming data to detect situations

• Acts on insights from the data

Data is enriched with external data such as Weather or asset master data

1 3a

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ingest

analyze report & recommend act

profile SaaS offering

Prebuilt analytics

Faster time to value

Designed for line of business

Reduces need for data scientists

Insight at point of engagement

Predictive IBM Predictive Maintenance on Cloud

IBM Predictive Quality on Cloud

IBM Predictive Warranty on Cloud

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Watson IoT API families allow easy integration of

cognitive analytics into IoT apps

Natural Language Processing

Enables interaction through natural human

language and dialog

Machine Learning

Automates data processing and continuously

monitors new data to learn and improve results

Textual Analytics

Enables mining of textual sources to find

correlations and patterns in these vast amounts

of untapped data

Video/Image Analytics

Enables monitoring of unstructured data from

video feeds and image snapshots to identify

scenes and patterns

Analytics at the Edge

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Why This is So Unique

Traditional: Deliver Data to the Analytics

Analytics

Data

Edge Node

Fog Node

IoT Device

Analytics Analytics Analytics

Analyze Data in the 'Right' Place by Distributing Analytics from Cloud to Edge

Data Data Data

• Cloud • Big Data • Analytics • Applications

This is a Differentiated Route from the Industry Direction

IoT Device

Data

© 2016 Cisco and/or its affiliates. All rights reserved. Cisco Public

Combined Architecture

Enable Cognitive Computing Enable edge analytics; route to the cloud

&

Cloud Edge Node Fog Node IoT Device

Processing Processing Processing

Data Data Data

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Analytics at the Edge

Define and manage analytics in the cloud, run them where it makes sense

Analyze & act on data close to source

Reduce burden on constrained networks and reduce transmission costs

Enable continuous operations even if the network is down

Deliver high value data to the cloud for richer cross site / cross fleet analytics

IBM and CISCO announce analytics from Cloud to Edge!

Edge

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Expanding Analytics Further Into the IoT Environment

IBM Watson IoT Platform

(Cloud)

Capabilities:

• Complex analytics

• Analytic definition and distribution to

edge

• Longer term trends

• Pattern detection and machine

learning

Edge Gateway

WIoTP Edge

Analytics Agent

Operations:

• Filter and reduce data sent to cloud

• Pre-process and transform raw

data

• Identification of critical conditions to

send to cloud for additional

analytics

• Drive actions as the result of

analytics

IBM IoT Platform Analytics: An integrated

cloud-and-edge analytics programming model

that allows control and optimization over the

data flowing between edge and cloud.

IoT Devices

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Where Does the EAA Run?

IBM Watson IoT

Platform

IoT Devices IoT Gateway

• EAA will run on IoT Gateway devices made by companies we

partner with

• IBM is partnering with Cisco today and will be partnering with

other gateway providers in the future

Edge ….

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Combine with Cloud Analytics For Added Value

IoT

Gateway

with EAA

IBM Watson

IoT Platform

Edge

Analytic

Results

Service

Request

Device

Commands

• Analytics on the edge can send

data resulting from analytics to

the cloud for additional analytics

• Can be combined additional IoT

data for additional and analytics

and drive cloud based actions

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How it works

CLOUD ON-PREM

IoT Device IoT Device

IBM Cognitive Analytics Agent

Broker; Cisco Edge,

Fog Computing

& Edge Analytics

Watson IoT Platform

IBM Real-time Insights

Gateway Deploy to

IBM Edge

Engine (EAA)

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3

Device Data

Flows into

the Edge

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IBM EAA Filters &

Aggregates Device

Data, Rules Trigger,

Drive Alerts & Actions

Local Actions

Go Back Out

to IoT Devices

4b

Data, Alerts, &

Cloud Actions Flow

Back to Cloud

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Enrich with Context

(Weather) & Apply

Deeper Cognitive,

Predictive Analytics

4a

1 Configure Rules &

Actions in the Cloud

Actions

Analytics

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A progression of analytics & capabilities at the edge…

Real-Time High speed, ‘perishable’ data require

scalable contextualization and processing

to gain insight and react in near real time

Complex systems need more natural

interaction patterns via voice and chat

that operate independently of the cloud

Natural Language

Processing

Data is filled with trends, such as rising

temperature or cyclical patterns in a

motor’s RPMs, we need to automatically

understand norms and forecast issues

Machine Learning

Edge Workflows & Transactions Increasingly, we’ll need to handle more complex

logic and transactions at the edge, extending

insights to more complex orchestrations of

actions with enhanced security via blockchain

Unstructured data is also proliferating in the form of

video, image &audio data. This data needs to be

correlated with other sources of machine data and

processed for insights

Unstructured data

Predictive Mission critical equipment and

processes need to run smoothly, and

you need advance warning of issues in

order to avoid down time, business

disruption, and safety issues

Use Cases

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Targeting three operational patterns

Autonomous Operations

Remote Operations

Large Scale Operations &

Fleets

Industries: Automotive, Oil & Gas, Manufacturing, Heavy Equipment

Examples: Discrete manufacturing & continuous operations

Potential: Economic impact of $1.2 trillion to $3.7 trillion per year in 2025 (McKinsey)

Benefits

• 10 – 20% reduction in health & safety costs

• 5 – 10% increase in worksite productivity

• 5 – 10% reduce costs of equipment

Industries: Commercial Real Estate, Travel & Transportation, A&D, Heavy Equipment, Electronics

Examples: Elevators, motors, aircraft & engines, buildings & systems, commercial equipment

Potential: Economic impact of $560 billion to $850 billion per year by 2025 (McKinsey)

Benefits

• 10 – 20% increase in personnel productivity

• 5 – 12.5% decrease in logistics & scheduling costs

• 10 – 40% cost savings for equipment & maintenance

Industries: Transportation, Oil & Gas, Utilities, Mining, Construction

Examples: Shipping, Drilling, pipelines, oil platforms, wind/solar farms

Potential: Direct economic impact of $160 billion to $930 billion per year in 2025 (McKinsey)

Benefits

• 10 – 20% increase in productivity

• 5 – 12.5% decrease in operation costs

• 10 – 40% cost savings for equipment & maintenance

Global Auto Manufacturer benefits from edge-based Condition

Monitoring & Predictive Maintenance

Challenges

• Ensure high quality welds made by robots during manufacturing, improve detection speed to reduce impact of down process activities

• Monitor robot health through predictive modeling to detect early signs of deteriorating performance and risk of failure

Solution

• Edge analytics for real-time monitoring of welding robots based on properties such as vibration, rotation speed, velocity and weld temperature

• Cloud-based cognitive analytics for forecasting asset health and predicting component failures

• Components: IBM Predictive Maintenance & Quality, IBM Watson IoT Platform & Edge Analytics, Cisco Edge Analytics Fabric

Outcomes

• Higher quality welds with reduced rework, overtime, and scrap improving output and decreasing overall costs

• Predictability of robot issues allowing for pro-active maintenance during operational down time

Port of Cartagena leverages Condition Based Maintenance

Challenges

• Fleet of hundreds of vehicles, cranes and boats operating 24x7x365. Struggling to maintain equipment efficiently.

• Can’t afford to rely exclusive on cloud analytics due to potential connectivity problems.

Solution

• Consists of: Cisco UCS240 Server, Cisco Edge Analytics Fabric,

Watson IoT Platform with Edge Analytics.

• Optimizing maintenance by triggering automatic alerts based on

conditions at the edge (fuel levels, battery voltage, engine conditions

and other advanced measures).

Outcomes

• Now conducting condition-based maintenance, informed by actual

condition of assets operating at the edge.

• Critical data analyzed immediately at the edge; high-value data sent

data for deeper analysis in the cloud.

Utility improves outage detection and notification

Challenges

• Equipment failures and storms resulting in outages in the electrical grid

• Gaining real-time understanding of emerging situations to respond quickly and appropriately

• Notifying customers of the issue, current status and estimated restoration

Solution • Consists of: Cisco router (pole-top mounted), Cisco Edge Analytics Fabric, Watson IoT

Platform with Edge Analytics

• Help utility identify power outages faster by bringing analytics to the edge of grid to monitor smart meter telemetry and pinpoint outages as they occur

• Forward analytic results back to the cloud for more powerful analytics, wide-area intelligence & cognitive learning

Outcomes

• Improved notification time for the utility

• Lower operating expenses, Increased customer satisfaction

• Improved awareness and faster, proactive decisions through improved analytics

Silverhook Powerboats

Challenges

• Operating engines that are costly and dangerous to damage; rely on engine governors which adversely impact performance.

• Need to monitor real-time engine conditions and get feedback to operator with low latency.

Solution

• Consists of: Cisco IR829 Ruggedized Network Router, Cisco UCS240 Rack Server, IBM Watson IoT Edge Analytics

• Created rules at the edge, triggering alarm based on engine condition. New dashboard shows real-time race data, including status, engine condition, speed, RPMs and more.

Outcomes

• Better real-time monitoring with low latency.

• Helps Silverhook push for maximum performance with confidence,

enabling them to avoid engine shutdowns and win more races.

Silverhook Powerboats

Summary

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Summary

• Analytics are a key to gaining insights from IoT data

• Scalable solutions require a variety of analytics performed on the right data…and at the right location—including real-time, predictive, and cognitive & performed from edge to cloud

• Cognitive analytics will enable us to deliver transformative solutions that interact with users naturally and can learn from and automatically process the flood of data

• IBM has the portfolio of analytics to help customers succeed with IoT solutions and a network of partners to help deliver

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Learn more about IBM’s point of view on the Internet of Things ibm.com/IoT

Try out Internet of Things on Bluemix ibm.biz/try_iot

Try out Real-Time Insights

ibm.biz/try_rti Try out Edge Analytics

https://ibm.biz/Bdsdzs Getting Started video for Real-Time Insights

youtu.be/_Q4GlqAf2m4 Join us in our IoT conversations

@IBMIoT

IBM IoT – Get started today

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Edge Applications

Condition Based Maintenance

Condition Based Maintenance (CBM) uses sensor data from equipment and applies a monitoring strategy that uses the actual condition of the asset to decide when and what maintenance should be done. CMB can augment a time-based maintenance strategy and helps reduce failures while reducing the cost of maintenance overall by right sizing maintenance intervals. Operations benefits from greater asset availability and better predictability of performance.

Predictive Maintenance

Predictive Maintenance applies a deeper analysis of historical data to build predictive models for asset health and failures. Predictive models are then used to give forewarning of failures giving operations and maintenance the time to address impending issues with decreased risk of failure. Predictive models can be developed as an extension of CBM and used to understand potential failures of equipment in real-time.

Predictive Quality

Predictive Quality works holistically across equipment and work cells to understand the predictors of poor quality across a process. Predictive Quality applies statistical modeling to historical data from across equipment to generate predictive quality models for the entire process. Predictive Quality uses data such as environment or weather conditions and asset properties.