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 IoT
Dr Michael Capone
Principle Analyst - Capgemini

3Copyright © 2016 Capgemini and Sogeti. All Rights Reserved.
CWIN16 IoT Portfolio | 28th September 2016
There are many IoT definitions and buzzwords.
Telematics
Intelligent Devices
IoTM2M
Connected
Digital
Industrie 4.0
Mobile
Predictive Analytics
Smart
Big Data
Datability
Data Science
PaaS
Prescriptive Analytics
Cloud
Sensors
Telematics 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 Zetabytes
200.000.000.000 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 xD
DCX
Hamburg
Chrome
Kalkutta
Sogeti
Toulouse
SES
Ontario
iGate
Mumbai
SHT
Utrecht
DCA
Amsterdam
iGate (Sensors, Embedded Sogftware)
Shamik Dasgupta
SES (Energy)
Perry Stoneman
William Nicholson
Mike Malloni
Sogeti (M2M, Embedded Software)
Michiel Boreel
Philippe Ravix
Menno van Doorn
SHT (Sensors)
Carlos Rebeiro
DCA (Infrastrutkur)
Ab Frohwein
Edwin Leinse
CC (Strategy)
Johann Williamson
DCX (CSX)
Michael Capone
Jan Gudat
Chrome (Manufacturing)
Mangirish Hervadkar
Saktipada Maity
iGate (Sensors, Embedded Software)
Madhusudhan Reddy Nukala Shreyas
Bhargave
Bipin Patwardhan
GES (Sensors)
Uday Gokhale
Aditya Gondhalekar
Insights & Data (Analytics)
Jean Gillot
CC
Stockholm
GES
Bangalore
iGate
Atlanta
I&D
Paris

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-Cloud
State Engine
2. Machine Data
5. Alert & Task
Technician
ServiceCloud
Manager
Predictive Model
DB
R
BDLaaS
4. Case
1. Input Stream
PLC Data
Machine Data
Apama
Complex Event Processor
3. Descriptive Stream
EBS
WebMethods
Input Orchestrations
2. Complex Stream
Streaming
Manual
Case
Management
Q: How do I identify bad quality during or before the process?
A: PQM

IoT-Cloud
State Engine
2. Machine Data
9. Alert & Task
Knowledge
Technician
7. Model + Error Code
8. Article
ServiceCloud
Manager
Assets
6. Model
5. Location + Error Code
Predictive Model
DB
R
BDLaaS
4. Case
1. Input Stream
PLC Data
Machine Data
Apama
EBS
WebMethods
2. Complex Stream
Streaming
Manual
Case
Management
Q: How do I prevent unplanned downtime?
A: PMM

IoT-Cloud
State Engine
2. Machine Data
9. Alert & Task
Knowledge
Technician
8. Article
ServiceCloud
Assets
6. Model
Predictive Model
DB
R
BDLaaS
4. Case
1. Input Stream
PLC Data
Machine Data
Apama
EBS
WebMethods
2. Complex Stream
Streaming
Manual
Case
Management
Q: How do I ensure service is performed correctly?
A: PMM w/ VR

Customer: Industry: Offering:Departments:
SolutionSituation/Challenges Benefits/Results
Team size:
Project effort:
Project duration:
Q: How do I predict and optimize milk production?
A: Prescriptive Analytics
Lely 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
15CWIN München IoT Capone.pptx
• Blood Pressure
• Heart Rhythm
• Pulse
• Blood Sugar
• EKG
• BMI
• Weight
• Oxygen Saturation
• Movement
• Glucose
• Temperature
• Prescription Adherence
Features
Best-of-Speed
Platform as a Service
Streaming Analytics
Micro-Services
Any Input
Motivation
 Operational Efficiency
 Customer Satisfaction
 New Revenue Model
Benefits
Lower Costs
Shorter Treatment Period
Lower Side Effects
Higher Patient Satisfaction

Q: How do I satisfy the info needs of partners, customers and users?
A: Micro-Services
Copyright © Capgemini 2013. All Rights Reserved
16CWIN München IoT Capone.pptx
Infrastructure Services
Deliverables
1. Database Hosting
2. Predictive Modelling
3. Streaming Anayltics
4. Dashboards & Reporting
Customer Sales Marketing R&DService
Engineering Services
Competencies
1. Sensor Design & Prototyping
2. Embedded Software
3. Connectivity
4. Gateways, Hubs, Bridges
5. Testing
Application Services
Deliverables
1. App Factory (Development)
2. Rules Engine
3. CRM & Contact Center
4. 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.
Features
Any Input
Any Output Device
DIY
Many Benefits
Motivation
 New Revenue Model
Benefits
Customer Loyalty
User Acceptance
New Revenue Models
Single Point-of-Truth
Lower 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
…………………………………………………………………………..
Gate
way
CORE
MicroService
MicroService
MicroService
Leads, Accounts
ESB
IoT Cloud
• Regelwerk
für Trigger
von Event-
basierten
Cases
Event Raw
Datalake
Analysis
Results
Raw Event
Data Assets
Event veredelt
Bereitstellung
veredelter Daten
“Stammdaten”
Anfragen an Microservice
Neu zu erstellen
Core-Bauteine
ContentManagementSystem
•Provider Invoice Data
•Customer Invoice Data
Named Login /
Counted Login
Customer
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 sensors
Connectivity certain
No opt-ins

IoT Use Cases by “Things”
Machines
Industrie 4.0
Assets
Shipments
Products
INTERNALEXTERNAL
People
OPTIMIZETRANFORMATIONAL
Decrease Energy Costs
Decrease Loss & Theft
Decrease Misuse & Accidents
Improve Efficiency & Productivity
Decrease Assets
Decrease Unplanned Downtime
Decrease Inventory Costs
Decrease Labor Costs
Decrease Cost of Waste
Decrease Costs for Parts & Supplies
Decrease Early Retirement
Decrease Shipping Costs
Extend Machine Life
Improve Productivity
Decrease Accidents
Decrease Energy Costs
Decrease Mishandling & Loss
Improve On-time Delivery
Grow After-Market Sales
Decrease Misuse & Warranty Costs
Decrease Accidents
Offer Value-Added Services
Improve First-Call Resolution Rate
Decrease Rearly Retirement
Improve Product Development
Reduce Undesired Side Effects
Reduce Accidents
Reduce Trial and Treatment Periods
Improve Efficacy
Improve Health
QuickWins
 Simple retrofit sensors
 No approvals or opt-ins
 Connectivity certain

IoT-Cloud
State Engine
2. Machine Data
9. Alert & Task
Knowledge
Technician
8. Article
ServiceCloud
Assets
6. Model
Predictive Model
DB
R
BDLaaS
4. Case
1. Input Streams
Apama
WebMethods
2. Complex Stream
Streaming
Manual
Case
Management
Q: How do I identify poor quality during or even before it happens?
A: Digital Manufacturing PaaS for PQM and PMM
Features
Best-of-Breed
Best-of-Speed
Streaming Analytics
Micro-Services Compatible
Motivation
 Customer Satisfaction
Benefits
Lower Waste
Higher Quality
Higher Productivity
Lower Rework Costs
Phase 1
WIP
Phase 2
Q1 2017
Live im AIE München Nov 2016

Sensors
1. Temperature
2. Humidity
3. Vibration
4. Movement
5. Geo-location
6. Battery Status
7. 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
Features
Best-of-Speed
Streaming Analytics
Micro-Services
Any Input
Motivation
Benefits
Lower Costs
Shorter Treatment Period
Lower Side Effects
Higher Patient Satisfaction
Forschungsprojekt mit der Uni Hamburg,
UK-Freiburg und Merck, 2015
PoC Merck 2015 Phase III Trials
Studie TU-Freiburg PoC Q3 2016 Sepsis
Pilot BG-Bau Q1 2017 Reha Optimierung
Live im AIE München

Q: How do I satisfy the plethora of needs of partners and users?
A: Micro-Services
Infrastructure Services
Deliverables
1. Database Hosting
2. Predictive Modelling
3. Streaming Anayltics
4. Dashboards & Reporting
Customer Sales Marketing R&DService
Engineering Services
Competencies
1. Sensor Design & Prototyping
2. Embedded Software
3. Connectivity
4. Gateways, Hubs, Bridges
5. Testing
Application Services
Deliverables
1. App Factory (Development)
2. Rules Engine
3. CRM & Contact Center
4. 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.
Features
Best-of-Breed
Best-of-Speed
Streaming Analytics
Any Input
Any Output Device
Motivation
 New Revenue Model
Benefits
Customer Loyalty
User Acceptance
New Revenue Models
Single Point-of-Truth
Lower Operating Costs
Live 21.09. auf der IAA

Q: How can I track anything worldwide?
A: Pico Sierra
Challenges
1. Energy Harvesting und Generation
2. Multi-Protocol Connectivity
Sensormodul has GSM, Radio and Satellite transmitters
Forschungsprojekt mit der Uni Hamburg
Suchen Kunden für PoC

Challenges
1. Energy Harvesting und Generation
2. 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 Opportunity
Standards - 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-Services
Sensor Selection - retrofitting cost-prohibitive > long tail projects A1-
A2
Testing, Progammable Sensors, Robust Sensor
Design
Feuderalism - different departments, different budgets Strategic Consulting
Fashionistas - too many uncoordinated pilots and no plan Strategic Consulting, Micro-Services
Electronics - burn out faster than machanics, IoS IoS, Testing
Security - premise-based, hacking threats E2E Testing & Certification
User Acceptance - Peak of Hype, early adopter dissatisfaction Micro-Services
Personal Safety

Madness Factor 1. Poor Interoperability
Hub BridgeHub HubBridge Bridge GatewayHub
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 on
4. Plug-in smart bridge
5. Connect bridge to router with provided cable
6. Wait for 3 lights on bridge to turn blue
7. Download the app from the eShop
www.smartlight.com to your smartphone or
tablet
8. Create an account
9. Start the app and wait for your light to display
10. Select light to configure
11. Download additional third-party apps to learn
new ways to play with light
Dumb Bulb Smart Bulb

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 phone
4. Scroll until you find the app
5. Select light from menu
6. Turn light on
1. Wait until commercial
2. Stand up and run to
refrigerator
3. Open refrigerator
4. Retrieve snack
1. Find smart phone
2. Find reading glasses
3. Unlock smart phone
4. Locate refrigrator app
5. Activate frig cam
6. Browse frig with cam
7. Stand up
8. Run to sofa
9. 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 fail
2. 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.
Hopper Grinder Mixer Chiller Grinder
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.
Tech
ManagerController Operator HR

7a. Aggregation at gateway
To perform
correlative
analytics and add
external data, a
shadow system
and a parallel
stream are
created,
External data

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. Hopper Grinder Mixer Chiller Grinder

8. Making data actionable.
Intelligence and
actionability are
enabled, but users
have to use multiple
applications. This is
not user friendly.
External data
Tech
Tech Operations Compliance

9. Micro-Services Platform
EBS
Goals
• Any Device
• Any Input Source
• Collaborative Intelligence
• Single Point-of-Truth
• Actionability
• User Friendliness
• Any User
• Any Device
Technologies
1 Enterprise Bus
1 Data Lake
1 Streaming Analytics Engine
1 Enterprise Knowledgebase
1 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.

Microservices: The Future-Proof Framework for IoT

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Editor's Notes