Infosys Overview for Industrial IoTomg.or.jp/document/forum/Infosys Overview for Industrial... · 2016-04-22 · Infosys confidential, not to be reproduced without permission Asset

Infosys confidential, not to be reproduced without

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Infosys Overview for Industrial IoT

Apr 2016


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3 of Top 4 Oilfield Service

Majors

6 of Top 10 Global Telcos

5 of Top 5Auto OEMs

4 of Top 5Global Aerospace

and Defense

Successful Global Experience with Leading Companies

Established in 1981, Headquarters: Bangalore

Pioneered and expanding the integrated Global Delivery Model (GDM)

194,000+ Employees of 115 different Nationalities

1092 Clients (Over 150 Fortune 500 clients)

97 % Repeat Business (as of March 31,2016)

Revenue of 10,750 億円 (as of March 31,2016)

98.3% of our projects delivered on time

5 of Top 10 Heavy

Engineering

Enterprise Applications and Infrastructure

Big Data and Analytics

Infosys Products & Platforms

Engineering Services

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Continuous Learning is the foundation for Infosys

42万坪Campus to train 14000 Engineers given day

“Design Thinking” training for all employees

Mandatory “Artificial Intelligence” training

217 億円 Infra investment for learning and 3% of our profit for training

Infosys is the Gold Standard in Training ~Forrester,2011

We make Employees better With investments in training In tomorrow’s leadership

Reason for world’s largest corporate university with over 600 teachers

Most of our 194,000+ staff receive six months training before

they start work

The Infosys Leadership Institute ensures Infosys has the leadership

to stay relevant to our clients

147 training rooms, 650+ faculty rooms & 42 conference rooms

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Infosys Japan at a glance

Photography by Infoscions: Nitin Dangwal, Ajit Chouhan and Rajeev Rajagopalan

Combining the best of Global Delivery and Japanese Service

Infosys Japan Intellect FocusPeople Clients

Started in 1997

Part of global strategy

Tokyo, Nagoya

Over 1000 people

300 + in Japan

35% Japanese

Immersion training

Interns from best universities

Local partnershipsKeidanren Member

Solutions for global enterprise

Engineering

Business Platforms

Manufacturing and High Technology

Insurance and Banking

Telecommunication and Retail

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IoT world – Key issues

Asset Sensor NetworkData

AggregationData Analysis

Enterprise Systems

Security

Device / service management

Key Points

• No one company can do all the things

• What is the use case?

• How to make money? Infosys Area of Specialization

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Infosys-Aachen University Survey Outline

Key dimensions of asset efficiency

Energy efficiency aims to reduce consumption of energy, resources (raw

material, water and fuels) and waste generation. It saves costs and improves

the overall sustainability of an enterprise

Information efficiency can address efficient data management with focus on

data standards and security for high data quality and interoperability

Operational efficiency has an integrated view by monitoring all levels beginning

from the asset to entire supply chain. The vision of the future manufacturing

processes is real-time visibility with a closed control loop at all levels

Planning and execution of maintenance tasks using real-time conditional data of

production systems coupled with strong predictive analytics will help in improving

the overall maintenance efficiency of assets by reducing unexpected breakdowns

Maintenance

efficiency

Energy

efficiency

Operational

efficiency

Information

efficiency

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Asset Efficiency Report: Research Methodology

The first major study in the field of Industrial Manufacturing to shed light on the field of

asset efficiency as a major driver for competiveness and to identify company

specific readiness and maturity for Industry 4.0 enabled asset efficiency

This comprehensive global research project studied producing companies, mainly in the field of manufacturing, and the assets on shop floor level the company uses for production

1

The study polled 433 industrial manufacturing executives in companies’ plant or production management (manufacturing managers, plant technical managers, COOs, asset efficiency consultants and heads of R&D/manufacturing) in China, France, Germany, UK, and US

2

Prestigious FIR Institute at the University of Aachen, together with Infosys, designed and analyzed the study over all five countries; FIR surveyed in Germany between January and March 2015 while independent research firm Vanson Bourne conducted the study in China, France, UK, and US between February and March 2015

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The survey was conducted online as well through telephone and personal interviews.4

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Asset Efficiency Report: Survey Demographics

A fairly equal split of the participants per region and per industry production type

36%

11%

12%12%

35%

USA

UKFrance

Germany

19%

China

23%

n = 433 7%

16%

19%

12%

Others

Process

industry

Electronics

Aerospace

Automotive

17%

Machinery

29%

Configure

to order

30%

Make to

stock24%

Engineer to

order

Make to order

Regions Industries

Production type Revenue (in USD)

x > 3 bn

17%

0.5 bn < x < 3 bn12%

x < 0.5 bn

71%

n = 433

n = 433 n = 433

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Infosys Asset Efficiency Survey - FindingsMaturity level of

implementation

4

3

2

1

15 7

31

13

39

32

15

48

2020Today

Potential recognized

Systematically implemented

Partly implemented

No awareness

ChinaGermany FranceUKUSA

FollowerEarly adopters

43

57

86

14

79

21

74

26

68

32%

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Focus Areas1. Enterprise: Asset

Efficiency, Smart Factory, Track & Trace

2. Consumer IoT: Connected Car, Connected Care

3. Social IoT: Smart City, Smart Retail

Consulting

Big Data

Solution Definition

Integration

Platforms

Analytics

Infosys IOT

Practice

• Infosys Information Platform• IoT Mediation Platform

• IOT Application development• Platform development• Industrial automation, control, instrumentation• Embedded software, Electronic product design• Advanced Engineering, Industry 4.0

PLC: RSlogix PLC 5, SIEMENS, GE Fanuc, Soft PLC, Honeywell etc.SCADA/HMI: Rockwell RSView, GE Fanuc Cimplicity, Citect,WINCC, RSView 32, WonderwareMES/Historian/MII: CAMSTAR, Citect-Ampla, SIMATIC IT, SAP MII/ME, OSI PI, Proficy, Aspentech

DCS: Invensys, Honeywell, ABB & others

Platforms: GE Predix, PTC Thingworx, Bosch M2M, Microsoft Azure, IBM IOTF, Oracle, SAP, Cumulocity, AWSAnalytics: R, Python, Matlab, JavaGateways: Intel, HP, Cisco, Schneider, National Instruments

Sensors, Devices: Murata, National Instruments, Schneider

Multi-disciplinary Team Platforms

Breadth and Depth of Industry Domain

• Metals• Semicon / Electronics• Power and Utility

• Food and Beverage• Automotive and Aerospace• Oil and Gas

20+ Global Customers in different Industry Domains

IoT Ecosystem Worldwide Customers

Infosys IoT10

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Infosys IoT Focus Areas

EnterpriseAsset Efficiency

Condition & Health Monitoring

Serviceability - Diagnostics

Track and Trace

Preventive Maintenance -Prognostics

Factory Visibility, Augmented Reality

ConsumerSafety & Quality of Life

Connected Car

Connected Stadium

Connected Homes

Connected Care

Connected Insurance

Enterprise –

Fixed & Moving Assets

Consumer –

InnovationGeneral –

Social & Sustainability

Social & SustainableSmarter Environments

Smart Cities

Smart Energy

Smart Farming

Smart Retail

Smart Buildings

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Infosys developed key base components

• Analytics Platform based on open source

– Infosys Information Platform

– Infosys Mediation Layer

• Use case specific Algorithms

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Big Data Management - Infosys Information Platform

Infosys

Informati

on

Platform

(IIP)

Mo

db

us

TC

P/I

P

OP

C-U

A

BA

Cn

et

TC

P/I

P

for

Pro

fin

et

TC

P

Str

eam

Cu

sto

m

Zig

Bee

TC

P/I

P

MQ

TT

/

AM

QP

Abstracted Data Model ( Profile, Asset Properties )

Dat

a A

dap

ters

Data Ingestion

Visualization

Data Explorer

Data Modelling

Data Science

IoT

Mediation

Layer

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Mediation Layer

OPC UA Clients

OPC UA SDK Client Toolkit

OPC UA SDK Server Toolkit

OPC UA Server

Mo

db

us

TCP/

IP

OP

C-U

A

BA

Cn

et

TCP/

IP

for

Pro

fin

et

TCP

St

ream

Cu

sto

m

ZigB

ee

TCP/

IP

MQ

TT/

A

MQ

P

Abstracted Data Model ( Profile, Asset Properties )D

ata

Ad

apte

rs

IoT

Mediation

Layer

User Interface Layer

Business Component Layer

.NET Framework

.NET Framework

OPC Wrappers

OPC DA/AE Server

Historian Server

WCF Service

OP

C b

as

ed

Un

ifie

d A

rch

ite

ctu

re

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Industrial Internet Consortium• Open Consortium founded by AT&T, IBM, Cisco, GE and Intel, has Japanese members as well

• Goal is to promote open standards for the emerging world

• Has 19 working groups in

• 7 broad areas:

• Business Strategy and Solution Lifecycle

• Legal

• Marketing

• Membership

• Security

• Technology

• Testbeds

• https://youtu.be/waSI-Ot43o8

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IIC Testbeds

Infosys Lead Member

Infosys and GE Lead Member

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Partner Ecosystem for IIC Test beds

IIP

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Name of the Test Bed Partner Companies

Asset Efficiency Test Bed Bosch, GE, IBM, Intel, National Instruments, PTC

Industrial Digital Thread GE

Connected Care Massachusetts General Hospital MD PnP Lab, PTC, and RTI

Smart Energy PTC and Schneider Electric

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Asset Efficiency Solution

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Members:Infosys, Bosch, GE, Intel, National Instruments, PTC

To collect asset information efficiently and accurately in real-time and run analytics to make the right decisions regarding the operation, maintenance, overhaul and replacement of the asset.

Condition-based Maintenance (CBM) which is possible only with an effective health monitoring system. This aircraft landing gear use case, which is based on the Asset Efficiency Testbed, enables automatic detection, diagnosis, prognosis, and mitigation of adverse events arising from component failures; ensures flight safety and reduction in the overall operational and maintenance costs.

• Mapping and Modelling based on Information collection in real time about the asset

• Failure mode analysis and prediction using engineering knowledge • Processing of Holistic data by integrating the asset with overall

system• Large Data processing and informed decision making

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Asset Efficiency - Solution Overview

Sensor Data

Geographic Data

Categorical Data

Event Data

Time Data

Other System Data

Anomaly

Detection

Fault

Detection

Energy

Efficiency

Asset

Utilization

System

Health

Performance

Analytics

Prognostics

Holistic

Analytics

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Digital Twin: Aircraft Landing Gear

Data Model

Data to Information

Asset Model

Physical to Digital

Service

Model

Reactive to Proactive

Cognitive

Model

Diagnostics to Prognostics

Digital

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Asset Efficiency solution for landing gear

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Reference Architecture

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Demonstration

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Industrial Digital Thread

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Engineering Data*

Manufacturing Data*

Test Data

Maintenance Data

Operations Data

Industrial Digital Thread

Input for future models & new

designs

Input for subsequent production

Design

TESTBED

Defects/ Concessions/ Deviations Resolution

Categorize/Reuse knowledge / Reduce cycle time in subsequent

production*

Value Analysis/ Value Engineering

Studies to reduce maintenance cost, down time

Studies to reduce operational cost & improve operational

efficiency

Input Data

Output Data

Feedback

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IDT Testbed Platform Solution Overview

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Connected Care

• Provide seamless connectivity between healthcare devices, providers, care

givers, patients

• Key driver is to reduce cost of healthcare in an aging society

• Has to be safe, secure and adhere to multiple regulations

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Phase 1: Connected Care Test bed28

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Architecture Overview

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Smart Energy

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• Smart Energy is one of focus areas for Energy consumption analysis, Energy

optimization and demand forecast

• Key driver is to,

• Optimize energy consumption

• Increase Renewable sources share

• Reduce carbon footprint

• Infosys Mysore campus itself is a smart city and smart energy concepts are

implemented

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Energy Management Framework

Energy Manage

ment

Data Collection

Integration

Visualization

Analytics

Decision Guidance

Modeling and

Optimization

• Metered data collection at Plant,

Area, Line, key Equipment levels

• Data standards and communication

interfaces (OPC, Modbus TCP etc.)

• Production Systems

• Production Scheduling

• Energy Efficiency Controls

• Contractual & Billing Systems

(Energy buying and Consumption)

• ERP

• Contextual Displays

• Dashboard with drill down

• Trends and Reports

• Notifications

• Key Performance Indicators at Plant,

Process, Equipment levels

• Mapping energy usage profiles

• Peak load and surcharge

• Workflows for Energy Management

• Knowledgebase, benchmark data

• Peak runs and Production schedules

• Comprehensive Energy Audits

• Alternative Energy management

strategies

• Abstract Models

• What-if Scenarios

• Cost optimization

• Energy demand Optimization

LegendExisting at high level,

lacking details (Typical)

Not Existing, Manual, Large

Scope for Improvement

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Realizing a Smart Energy Management System

Smart Energy Management System

Wired and Wireless Metering

Steam AirWater &

SewerElectricity

Energy Consumption vs target

Process View

Production State View

Equipment View

Plant View

Energy Analytics

Day, month, Hour, year Trends &

Reports

Statistical (Peak, Average, Base

load)

Alarms and Notifications

Equipment and Process Efficiencies

Demand Forecasting –

Plant and Process

Consumption and Loss Analysis

Decision Support

Load Management

Energy Planning

Energy Control

MES

Production Schedule

Machine Status and Utilization

Production Mode

Product View

ERP

Billing Information

Tariff Plan

Energy Model and

Estimation

What-If Scenarios

Optimization Engine

Asset Information

Energy Data Model

Ener

gy D

ata

Mo

del

Ener

gy D

ata

Mo

del

Energy Dashboard

Decision Support• Real-time dashboards• Comparative benchmarks, thresholds, peak usage, cut-off• Supply-demand view, cutover to co-generative power etc.,

Energy Analysis• Historical time-series view, Aggregated reports• Comparative analysis at production line level, process level• Equipment analysis - design vs actual, consumption vs

production usage

Demand Forecasting & Energy estimator• Estimator takes into account average equipment consumption

during peak/avg/base load, avg production run/day• Estimator looks at process-equipment mapping, production

schedule, average cost to calculate Energy estimates per production line per day

• Engine has ability to extrapolate based on production planning and historical consumption data the Energy demand

Optimization engine• Uses design vs actual, comparative benchmarks for equipment's,

process levels to highlight areas of improvement• Highlight efficiency, leakage, idle time utilization etc.,• Identify process deviations, equipment retrofits/rewiring,

adjustments to production schedules, etc.,

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Demonstration: Infosys Mysuru campus energymanagement

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Infosys IoT / Industry 4.0 Booth at Hannover Fair 2016

Exhibition standHall 7, Digital Factory, E34

• Speaker Sessions• IoT / Industry 4.0 Demos

• Infosys Speaker Session on Industry 4.0• 25 Apr 2016, 15:00 to 16:00• Hall 8, StandD19

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Thank You

[email protected]

[email protected]

03 5545 3251

Infosys Overview for Industrial IoTomg.or.jp/document/forum/Infosys Overview for Industrial... · 2016-04-22 · Infosys confidential, not to be reproduced without permission Asset

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