FIMART - Oct 2016

Cognitive + Edge Initiative Cisco/IBM

Exponential Data Created in IoT and Distributed Environments Needs to be Connected

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.

Precision Agriculture

Examples: GNSS guided planting, maintaining and

harvesting of crops

Water management

weather conditions and actions

Soil management and plant growth data

Tractors/machine maintenance

Farming management based on intensive use of collected data to maximize yield and minimize resource usage

Next industrial revolution in Agriculture – Data Driven with Analytics

Agriculture is IoT ready

Agriculture land (% of land area)

Precision Farming Market Opportunity

Global market for precision farming is between $20-30B for 2019

Precision Agriculture is happening, it is a global problem, but the growers will define where the put their data in the next 3 years

Creating Value for Distributed/IoT Environments Solving Critical IT/OT Challenges

Data Capture

Infrastructure

Data Management

People & Processes

Deployment

Analytics

Stream only what is necessary; eliminate the unessential

Capture all data where it lives, evaluate, sort high from low value

Monitor product quality during processes & remotely manage at the point of collection

Put intelligent analytics at the edge of the network

Rely on IBM & Cisco as trusted technology leaders

Link analytics to response teams to align intervention

Result Use ALL the Data Important to you!

Sensors

Gathering Data

Background

IBM has transformed Cognitive Computing and Cloud Platforms:

• Watson IoT

• Cloud

• Mobility

• Security

Data Processing

Background

Cisco has prepared for Hyper Distribution/IoT and developed:

• Edge and Fog Computing

• Edge Performance Analytics

• Streaming Data Processing

• Distributed Data Storage/Access

Cloud Edge Node Fog Node IoT Device

Data Data Data

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

Better Together

Enable Cognitive IoT at the Edge Augment our networking with a

holistic route to the cloud

&

Cloud Edge Node Fog Node IoT Device

Processing Processing Processing

Data Data Data

Use Case: Remote Management

Impact on

People 10 – 20% increase in

productivity

Impact on

Equipment 10 – 40% cost savings

• Monitor and evaluate remote locations for hazards; trigger action based on safe operating directives

• Monitor affects of weather; trigger mitigation planning based on conditions

• Dangerous inspections can use Drone and video technology to uncover risks by detecting abnormal conditions

• Monitor worker health in hazardous environment against personal health history & known tolerances

• Evaluate asset performance at the point of monitoring, drive corrective action, reduce premature degradation

• Recover as much as ½ your maintenance budget by aligning maintenance investment to asset condition

• Support disruptive sales models – “Power by the Hour” selling service equipment provides instead of equipment itself

• Increased condition –based maintenance reduces need for capital investment

• Link weather & environmental conditions that affect equipment performance

• Monitor performance across connected assets for insight on dependencies and correlations that cause failure

• Course correct asset performance before it impacts your business

• Sensors, rather than human judgment (and human error), drive adjustment of work instruction

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)

Impact on

Operations 5 – 12.5% decrease in

operation costs

Use Case: Autonomous Operations

Impact on

People 10 – 20% decrease in

Health & Safety costs

Impact on

Process 5 – 10% increase in

worksite productivity

Impact on

Equipment 5 – 10% reduce costs

of equipment

• Video monitor people movement in construction site or hazardous area

• Monitor security; reduce human observation 30 – 50% @ $6 Billion per yr

• Halt equipment when human proximity sensed; 10-20% decrease in safety costs

• Monitor time in hazardous environment against tolerances

• Monitor fatigue: position of body during lift, is it safe

• Recover as much as ½ your maintenance budget by aligning maintenance investment to asset condition

• Video analysis of conditions fed by drones automate inspection in areas difficult to access

• Augmented reality goggles for workers provide real-time guidance on work practice enhancing knowledge

• Auditory monitoring of running equipment indication of change

• Course correct asset performance before it impacts your business

• Sensors, rather than human judgment (and human error), to adjust the performance of machinery

• Downstream quality detection drives notification for service of upstream component

• Analysis of parts moving through process micro-adjust for variances in raw material quality

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

Examples: Discrete manufacturing & continuous operations

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

Use Case: Large Scale Operations / Fleets

Impact on

People 10 – 20% increase in

productivity

Impact on

Equipment 10 – 40% cost savings

• Automate climate control based on impact of weather

• Wearable technology monitor and alerts on driver performance impacting safe operations and occupant comfort and safety

• Customer centric schedule updates driven from sensed conditions to mobile devices

• Automate monitoring of restricted areas; analysis at point of sensor evaluates permission rules and can trigger security of breaches

• Evaluate asset performance at the point of monitoring, drive corrective action, reduce premature degradation

• Recover as much as ½ your maintenance budget by aligning maintenance investment to asset condition

• Support disruptive sales models – “Power by the Hour” selling service equipment provides instead of equipment itself

• Increased condition –based maintenance reduces need for capital investment

• Link weather & environmental conditions that affect schedules, drive actions based on operational business rules

• Automate course correction through connected navigation systems

• Linking speed and proximity of vehicles allows for speed increases by maximizing available capacity of transit lanes

• Integrate service intervals to people flows: trigger service events based on predicted travel patterns

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

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

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

Impact on

Logistics & Scheduling 5 – 12.5% decrease in

operation costs

The Solution Being Announced The right partnership

IBM and Cisco are teaming

up to create a joint approach

to IoT data and analytics –

The solution is available together and will deliver distinct insight for customers to address their most

complex IT/OT challenges in distributed environments, including autonomous operations, remote

operations and large scale operations and fleets.

• Cognitive and Cloud

• Business Analytics

• Watson IOT Platform

• Cloud Data Storage Platform

• Device Interactions

• Edge Analytics

• Intelligent Distribution

• Graphical Development and Management Tools

& • Enable immediate analytics

at the edge

• Collect information for longer

term analysis in the cloud

Joint Solution • First of its kind joint approach

to IoT data analytics

• Enable immediate IoT analytics

at the edge of the network

• Collect data for longer term

analysis in the cloud

• Architectural approach to

address critical challenges

for distributed environments

Edge (Cisco Router / UCS / Server)

IoT Device IoT Device

IBM Watson IoT Platform, Business

Analytics & Cognition

Cisco Edge,

Fog Computing

& Edge Analytics

Cognitive & Edge Technology Collaboration Benefits

&

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)

2

3

Device Data

Flows into

the Edge

4

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

5

Enrich with Context

(Weather) & Apply

Deeper Cognitive,

Predictive Analytics

4a

1 Configure Rules &

Actions in the Cloud

Actions

Analytics

Components CLOUD ON-PREM

IoT Device IoT Device

IBM Cognitive Analytics Agent

Watson IoT Platform

IBM Real-time Insights

Gateway

Metered on

MB Analyzed

1a

2

Cisco Router / UCS

(Per Instance)

Cisco DDF

(Per Instance)

5 Additional Services

for Analytics, Context

(Weather), Storage, etc.

4

1 Metered on

MB Transmitted

Actions

Analytics

1a Metered on

MB Stored

Metered on

MB Analyzed 3

Broker; Cisco Edge,

Fog Computing

& Edge Analytics

IBM Portfolio

Applications

• Maximo Asset Management

• TRIRIGA

• PMQ

Jasper's Control Center

service management is

integrated with IBM’s cloud

based real-time information

management and analytics

Cloud

An Open System

Data Center Edge Node Fog Node Device or Controller

Generating Data Leveraging Data Analyzing Data Aggregating Data Capturing Data

M

Time-Series Historian Database

(ParStream)

M

Correlation

M

Aggregation

M

Filtering

Microservices (Develop or Buy)

M

Machine Learning

3rd Party

M M M M M M M M M M M

Analytics (Data Prep)

M

Access & Integration

(CIS)

M

Event Stream Processing

(CSA)

M

IBM Watson IOT Platform Offer Details

The IBM Part Consists of 4 Main Components:

Edge Analytics Agent (Microservice)

• Runs on Cisco DCN to host analytics at the edge

• Interacts through brokers and links

Watson IoT Platform

• Bluemix service

• Define & distribute the analytic rules

to a gateway

IoT Real-Time Insights

• Analytic rules will process device data and

drive actions at both the Gateway and Cloud

Cloud Data Storage

• Store the data that needs to be kept