Microservices: The Future-Proof Framework for IoT

1Copyright © 2016 Capgemini and Sogeti – Internal use only. All Rights Reserved.

Presentation Title | Date

Microservices:The Future-Proof Framework for IoTDr Michael CaponePrinciple Analyst - Capgemini

3Copyright © 2016 Capgemini and Sogeti. All Rights Reserved.

CWIN16 IoT Portfolio | 28th September 2016

There are many IoT definitions and buzzwords.

Tele

mat

ics

Intelligent Devices

IoTM2MConnected

Dig

italIndustrie 4.0 M

obile Predictive

AnalyticsSmart

Big DataDatability

Data Science

PaaS

Prescriptive Analytics

Cloud

SensorsTelematics AMP 2.0



Internet

Mir geht´s gut.

Ich habe hunger.

Ich liebe Katzen.

Ich mag Pizza.

7.000.000.000 Nutzer

40 zetabytes



Internet-of-Things

Mir geht´s gut.

Ich habe hunger.

Der Wind ist stark.

Meine Batterie ist fast leer.

Ich fahre 140 kmh!

Ich brauche Schmierfett

.

Deine Torte ist fertig.

Dein Puls ist

schwach.

Ich liebe Katzen.

Ich mag Pizza.

14.000.000.000 Dinge in 2016

4 Zetabytes200.000.000.000 Dinge

in 2020

7.000.000.000 Nutzer



Part 1: Our IoT Competencies

1.Capgemini is the only SI that can deliver a complete IoT solution from sensor to app

2.We are the only SI that focuses on „Data Actionability“

3.We are the only SI that has delivery capability with all 8 leading IoT platforms



Capgemini is the Leading IoT SI

150 million connected devices

23 million devices managed as a service

20.000+ IoT experts worldwide available using our RightShore model.

202 IoT projects since 2004

A leading SI in 3 Experton categories: Big Data Analytics, Beratung & Systemintegration Machinen- und Anlagenbau, Beratung & Integration Automobil.



IoT is always xDDCXHamburg

ChromeKalkutta

SogetiToulouse

SESOntario

iGateMumbai

SHTUtrecht

DCAAmsterdam

iGate (Sensors, Embedded Sogftware)Shamik Dasgupta

SES (Energy)Perry StonemanWilliam NicholsonMike Malloni

Sogeti (M2M, Embedded Software)Michiel Boreel Philippe RavixMenno van Doorn

SHT (Sensors)Carlos Rebeiro

DCA (Infrastrutkur)Ab FrohweinEdwin Leinse

CC (Strategy)Johann Williamson

DCX (CSX)Michael CaponeJan Gudat

Chrome (Manufacturing)Mangirish HervadkarSaktipada Maity

iGate (Sensors, Embedded Software)Madhusudhan Reddy Nukala Shreyas BhargaveBipin Patwardhan

GES (Sensors)Uday GokhaleAditya Gondhalekar

Insights & Data (Analytics)Jean Gillot

CCStockholm

GESBangalore

iGateAtlanta

I&DParis



Gartner Leader in “Completeness of IoT Vision”

Performance and usage data is acquired and transmitted every x seconds.

Data from many connected devices is aggregated and stored

When the data indicates an issue that can cause a problem or opportunity, a case is created and a resolution or „Next Best Action“ is selected

An actor takes action: 1. Customer Care

provides the operator tips and instructions,

2. Sales & Marketing proactively sends the customer offers and orders

3. Technical Service is dispatched to perform maintenance

4. System sends messages to machine

We make IoT data

ACTIONABLE

1

2

4

5

Data is analyzed in real-time to identify issues or opportunities.

3

Control Tower

Connected Service Excellence (CSX)

„2 Steps Ahead“



5-A Model for Maturity Assessment, Archtecture and Project Plan

Functional Layer Description Technology

Action (A5)A task is automatically created for sales, contact center, service, or system.

ERP Integration, Contact Center, Field Service Management, Mobile Apps

Assignment (A4)A recommendation or „Next Best Action“ is proposed.

Knowledge Base, Expert System, Product Information System

Analysis (A3)Data is viualized, tabulated, and analyzed. Insights are produced.

Dashboards, Reporting, Descriptive, Predictive Analytic

Aggregation (A2)Data from multiple, different sources are combined and stored.

Big Data, Data Lake, In-Memory Database

Acquisition (A1) Data is created and transmitted.

Sensors, Gateway, Connectivity

IoT-CloudState Engine

2. Machine Data

5. Alert & Task

Technician

ServiceCloud

Manager

Predictive Model

DB

RBDLaaS

4. Case

1. Input Stream

PLC DataMachine Data

ApamaComplex Event Processor

3. Descriptive Stream

EBS

WebMethodsInput Orchestrations

2. Complex Stream

Streaming

Manual

Case Manageme

nt

Q: How do I identify bad quality during or before the process?A: PQM


2. Machine Data

9. Alert & Task

Knowledge

Technician

7. Model + Error Code

8. ArticleServiceCloud

Manager

Assets

6. Model

5. Location + Error Code

Predictive Model

DB

RBDLaaS

4. Case

1. Input Stream




EBS


2. Complex Stream

Streaming

Manual

Case Manageme

nt

Q: How do I prevent unplanned downtime?A: PMM


2. Machine Data

9. Alert & Task

Knowledge

Technician



Assets

6. Model


Predictive Model

DB

RBDLaaS

4. Case

1. Input Stream




EBS


2. Complex Stream

Streaming

Manual

Case Manageme

nt

Q: How do I ensure service is performed correctly?A: PMM w/ VR

Customer: Industry: Offering: Departments:

Solution

Situation/Challenges Benefits/Results

Team size:

Project effort:

Project duration:

Q: How do I predict and optimize milk production? A: Prescriptive AnalyticsLely Agriculture Milking Embedded Software

Development of regression test tool enabling two types of verification.

Verification of the algorithm output of the ported code against the original mathlab results.

Verification of the algorithm output of a baseline algorithm against the results of a new algorithm.

New multistage vision algorithms are developed in mathlab for new camera’s

Code must be ported to an ARM based platform running a Linux distribution.

Fast on-target results of animal health supporting early indication of performance.

Shorter iterations for evaluation of new algorithm stages

Objective evaluation of algorithm improvements achieved over time

Q: How do I know if my patient is doing well on the new treatment plan?A: eHealth

Copyright © Capgemini 2013. All Rights Reserved

15

CWIN München IoT Capone.pptx

• Blood Pressure• Heart Rhythm• Pulse• Blood Sugar• EKG• BMI• Weight• Oxygen Saturation• Movement• Glucose• Temperature• Prescription

Adherence

FeaturesBest-of-SpeedPlatform as a ServiceStreaming AnalyticsMicro-Services Any Input

Motivation Operational Efficiency Customer Satisfaction New Revenue ModelBenefitsLower CostsShorter Treatment PeriodLower Side EffectsHigher Patient Satisfaction

Q: How do I satisfy the info needs of partners, customers and users?A: Micro-Services

Copyright © Capgemini 2013. All Rights Reserved16

CWIN München IoT Capone.pptx

Infrastructure ServicesDeliverables1. Database Hosting2. Predictive Modelling3. Streaming Anayltics4. Dashboards & Reporting

Customer

Sales Marketing

R&DService

Engineering ServicesCompetencies1. Sensor Design & Prototyping2. Embedded Software3. Connectivity4. Gateways, Hubs, Bridges5. Testing

Application ServicesDeliverables1. App Factory (Development)2. Rules Engine3. CRM & Contact Center4. FieldService

There are many users of IoT data. These users have different requirements, they use different hardware, have different logic, and want to trigger departmental specific processes.

An enterprise-ready data lake consolidates the data from all „smart appliances“ making it available to multiple users and enabling correlative analytics, i.e. an intelligent home. Any device can be made „smart“ by retrofitting or embedding sensors and software. Generating data is only the first step. The data has to be validated and transmitted reliably and securely.

FeaturesAny InputAny Output DeviceDIYMany Benefits

Motivation Operational Efficiency Customer Satisfaction New Revenue ModelBenefitsCustomer LoyaltyUser AcceptanceNew Revenue ModelsSingle Point-of-TruthLower Operating Costs

Web Frontend

Service Cloud(Cases, Opportunities,

Quote, Contacts, Accounts, Assets)

• Experten• Trainer• etc.-------------------------------• CPQ Steelbrick- Configuration: Usage

Administration, and Product & Pricing Configuration

- Presentation: admin (config master) Portal user (Nutzer)

MarketingCloud

……

……

……

……

……

……

……

……

……

……

……

……

……

……

..

Gateway

CORE

MicroService

MicroService

MicroService

Leads, Accounts

ESB

IoT Cloud• Regelwer

k für Trigger von Event-basierten Cases

Event Raw

DatalakeAnalysisResults

Raw Event Data

Assets

Event veredelt

Bereitstellung veredelter Daten

“Stammdaten”Anfragen an Microservice

Neu zu erstellenCore-Bauteine

Cont

ent

Man

agem

ent

Syst

em•Provider Invoice Data•Customer Invoice Data

Named Login / Counted LoginCustomer

Community / WebShop

(Steelbrick)• Händler• Flotte• Werkstatt----------------------• Fahrer

Kampagnen

Trigger

Micro-Services



IoT Partner Eco-System

Platform Partners

Solution Partners(not all shown)

German Delivery Lead



Focus on Use Cases with Measurable Impact

Quelle: Computerwoche Ausgabe 20. Juli 2015

QuickWins Sectors Crude retrofit sensorsConnectivity certainNo opt-ins



IoT Use Cases by “Things”

MachinesIndustrie 4.0

Assets

Shipments

Products

INTE

RNAL

EXTE

RNAL

People

OPT

IMIZ

ETR

ANFO

RMAT

ION

AL

Decrease Energy CostsDecrease Loss & TheftDecrease Misuse & AccidentsImprove Efficiency & ProductivityDecrease AssetsDecrease Unplanned DowntimeDecrease Inventory CostsDecrease Labor CostsDecrease Cost of WasteDecrease Costs for Parts & SuppliesDecrease Early RetirementDecrease Shipping CostsExtend Machine LifeImprove ProductivityDecrease AccidentsDecrease Energy CostsDecrease Mishandling & LossImprove On-time DeliveryGrow After-Market SalesDecrease Misuse & Warranty CostsDecrease AccidentsOffer Value-Added ServicesImprove First-Call Resolution RateDecrease Rearly RetirementImprove Product DevelopmentReduce Undesired Side EffectsReduce AccidentsReduce Trial and Treatment PeriodsImprove EfficacyImprove Health

QuickWins

Simple retrofit sensors No approvals or opt-ins Connectivity certain




2. Machine Data

9. Alert & Task

Knowledge

Technician



Assets

6. Model


Predictive Model

DB

RBDLaaS

4. Case

1. Input StreamsApamaComplex Event Processor



2. Complex Stream

Streaming

Manual

Case Manageme

nt

Q: How do I identify poor quality during or even before it happens?A: Digital Manufacturing PaaS for PQM and PMM

FeaturesBest-of-BreedBest-of-SpeedPlatform as a ServiceStreaming AnalyticsMicro-Services Compatible

Motivation Operational Efficiency Customer Satisfaction New Revenue Model

BenefitsLower WasteHigher QualityHigher ProductivityLower Rework Costs

Phase 1WIP

Phase 2Q1 2017

Live im AIE München Nov 2016



Sensors1. Temperature2. Humidity3. Vibration4. Movement5. Geo-location6. Battery Status7. Signal

Strength

Configured and delivered in just 3 weeks• 25 compact sensor modules w/ 7 sensors

each• 12 months flat data plan for EU US • 12 months internet gateway services• 12 months data storage• 12 months access to device management

console • 9 users + 1 administrator• 5 days training or custom configuration• 12 months support

Q: How do I test IoT ideas before investing?A: IOTINABOX

€55.000 plus applicable sales tax

Q4 2016 DHL Full Trailer Tracking

Live im AIE München



Q: How do I know if my treatment is effective?A: eHealth

• Blood Pressure• Heart Rhythm• Pulse• Blood Sugar• EKG• BMI• Weight• Oxygen Saturation• Movement• Glucose• Temperature• Prescription

Adherence

FeaturesBest-of-SpeedPlatform as a ServiceStreaming AnalyticsMicro-Services Any Input

Motivation Operational Efficiency Customer Satisfaction New Revenue ModelBenefitsLower CostsShorter Treatment PeriodLower Side EffectsHigher Patient SatisfactionForschungsprojekt mit der Uni

Hamburg, UK-Freiburg und Merck, 2015PoC Merck 2015 Phase III TrialsStudie TU-Freiburg PoC Q3 2016 SepsisPilot BG-Bau Q1 2017 Reha OptimierungLive im AIE München



Q: How do I satisfy the plethora of needs of partners and users?A: Micro-Services

Infrastructure ServicesDeliverables1. Database Hosting2. Predictive Modelling3. Streaming Anayltics4. Dashboards & Reporting

Customer

Sales Marketing

R&DService

Engineering ServicesCompetencies1. Sensor Design & Prototyping2. Embedded Software3. Connectivity4. Gateways, Hubs, Bridges5. Testing

Application ServicesDeliverables1. App Factory (Development)2. Rules Engine3. CRM & Contact Center4. FieldService

There are many users of IoT data. These users have different requirements, they use different hardware, have different logic, and want to trigger departmental specific processes.

An enterprise-ready data lake consolidates the data from all „smart appliances“ making it available to multiple users and enabling correlative analytics, i.e. an intelligent home. Any device can be made „smart“ by retrofitting or embedding sensors and software. Generating data is only the first step. The data has to be validated and transmitted reliably and securely.

FeaturesBest-of-BreedBest-of-SpeedPlatform as a ServiceStreaming AnalyticsAny InputAny Output Device

Motivation Operational Efficiency Customer Satisfaction New Revenue ModelBenefitsCustomer LoyaltyUser AcceptanceNew Revenue ModelsSingle Point-of-TruthLower Operating Costs

Live 21.09. auf der IAA



Q: How can I track anything worldwide?A: Pico Sierra

Challenges1. Energy Harvesting und

Generation2. Multi-Protocol Connectivity

Sensormodul has GSM, Radio and Satellite transmitters

Forschungsprojekt mit der Uni HamburgSuchen Kunden für PoC



Challenges1. Energy Harvesting und

Generation2. Multi-Protocol Connectivity

Q: How can I track anything worldwide?A: Pico Sierra

Sensormodul has GSM, Radio and Satellite transmitters



Part II. Top IoT Challenges (Opportunities)

9. Personal Safety

Market Challenge OpportunityStandards - too many > consortia building > limited flexibility

Integrations, Connectors, Membership

Inconsistent Taxonomie - Platform, Industrie 4.0, IoT, IIoT, SmartFactory, Dig Manu

Publications, Seminars, Community

Hoarding - internal data, data hoarding E2E Encryption, Micro-ServicesSensor Selection - retrofitting cost-prohibitive > long tail projects A1-A2

Testing, Progammable Sensors, Robust Sensor Design

Feuderalism - different departments, different budgets Strategic ConsultingFashionistas - too many uncoordinated pilots and no plan

Strategic Consulting, Micro-Services

Electronics - burn out faster than machanics, IoS IoS, TestingSecurity - premise-based, hacking threats E2E Testing & CertificationUser Acceptance - Peak of Hype, early adopter dissatisfaction

Micro-Services

Personal Safety



Madness Factor 1. Poor Interoperability

Hub BridgeHub HubBridge Bridge Gatew

ayHub

Many standards and silo´d proprietary systems make it difficult to connect smart things.



Madness Factor 2. App Avalanche

The average European household today has 1 smart thing – 1.5 smart apps

and by 2025, the number of

smart things per household

will grow to 100.

Remember this?



Madness Factor 3. Too Complex

Instructions

1. Remove old light bulb by turning it counter-clockwise with your hand

2. Install new light bulb by turning it clock-wise

3. Turn lamp on to test

Instructions

1. Remove old light bulb by turning it counter-clockwise with your hand

2. Install new light bulb by turning it clock-wise

3. Turn lamp on4. Plug-in smart bridge5. Connect bridge to router with provided

cable6. Wait for 3 lights on bridge to turn blue7. Download the app from the eShop

www.smartlight.com to your smartphone or tablet

8. Create an account9. Start the app and wait for your light to

display10.Select light to configure 11.Download additional third-party apps

to learn new ways to play with light

Dumb Bulb Smart Bulb

http://www.smartlight.com/



Madness Factor 4. New Behavior

1. Tap around in dark until you find a switch

2. Flip switch

1. Tap around the dark to find your smart phone

2. Tap around in the dark until you find your reading glasses

3. Unlock your smart phone4. Scroll until you find the app5. Select light from menu6. Turn light on

1. Wait until commercial2. Stand up and run to

refrigerator3. Open refrigerator4. Retrieve snack

1. Find smart phone 2. Find reading glasses3. Unlock smart phone4. Locate refrigrator app5. Activate frig cam6. Browse frig with cam7. Stand up 8. Run to sofa9. Retrieve snack



Madness Factor 5. Expensive

Smart things cost as much as 10 times more than dumb things.

Quellen: BI Intelligence, TÜV Trust IT



Madness Factor 6. Short Relevance

Smart things have long functional lifecycles but get “old” fast.

1. Programmed obsolescence – built to fail2. Style obsolescence – frequent facelifts 3. Systematic obsolescence – unexpected

compatibility

Quellen: BI Intelligence, TÜV Trust IT

1. „Smart“ things are just IoT silos.

Hub

Things can be connected, but that does not mean they are smart.

Hopper

2. Many disconnected silos.

Hub Bridge

Manufacturers want to protect their data and their customer relationship.

The more smart things we acquire, the more silos we have to subscribe to. Hopper Grinder

3. Frustation.

Hub Bridge EBS

The insights from one machine cannot be used to control another machine.

Hopper Grinder Mixer Chiller

4. Chaos.

Hub Bridge EBS Gateway

The insights from one machine cannot be used to control another machine.

IF vacuum cleaner on, THEN television louder.

This is not really smart and certainly not sustainable.

Hopper Grinder Mixer Chiller Grinder

5. Shadow silos serve other users.


There are many users for device data.

To give additional users access to the device data, we typically copy a subset of the data to another system and build a new application.


TechManagement

6. Valley of Disillusionment


When many users want access to the same data, but for different purposes, we create a shadow farm.

This is not sustainable.


TechManagerController Operator HR

7a. Aggregation at gateway


To perform correlative analytics and add external data, a shadow system and a parallel stream are created,

External dataHopper Grinder Mixer Chiller Grinder

7b. Replace OEM gateways

Industrial Gateway

or the OEM gateway is replaced by one of the dozen available multi-protocol gateways (for industry, business and consumer applications),External data


7c. Connect multiple databases.


or the databases are integrated.

These options enable intelligence.

This is not actionable intelligence.


8. Making data actionable.


Intelligence and actionability are enabled, but users have to use multiple applications. This is not user friendly.

External data


TechTech Operations Complianc

e

9. Micro-Services Platform

EBS

Goals• Any Device• Any Input Source• Collaborative Intelligence• Single Point-of-Truth• Actionability• User Friendliness• Any User• Any Device


Technologies1 Enterprise Bus1 Data Lake1 Streaming Analytics Engine1 Enterprise Knowledgebase1 Process Builder

1o. App Factory

EBS

Applications for every user on any device.

Each application can have ist own

• Logic• Process• Dashboard• UX

defined by the user group. Hopper Grinder Mixer Chiller Grinder

Presentations & Public Speaking

Microservices: The Future-Proof Framework for IoT