microservices: the future-proof framework for iot
TRANSCRIPT
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
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CWIN16 IoT Portfolio | 28th September 2016
Internet
Mir geht´s gut.
Ich habe hunger.
Ich liebe Katzen.
Ich mag Pizza.
7.000.000.000 Nutzer
40 zetabytes
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CWIN16 IoT Portfolio | 28th September 2016
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
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CWIN16 IoT Portfolio | 28th September 2016
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
7Copyright © 2016 Capgemini and Sogeti. All Rights Reserved.
CWIN16 IoT Portfolio | 28th September 2016
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.
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CWIN16 IoT Portfolio | 28th September 2016
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
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CWIN16 IoT Portfolio | 28th September 2016
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“
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CWIN16 IoT Portfolio | 28th September 2016
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
IoT-CloudState Engine
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
PLC DataMachine Data
ApamaComplex Event Processor
3. Descriptive Stream
EBS
WebMethodsInput Orchestrations
2. Complex Stream
Streaming
Manual
Case Manageme
nt
Q: How do I prevent unplanned downtime?A: PMM
IoT-CloudState Engine
2. Machine Data
9. Alert & Task
Knowledge
Technician
7. Model + Error Code
8. ArticleServiceCloud
Assets
6. Model
5. Location + Error Code
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 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
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CWIN16 IoT Portfolio | 28th September 2016
IoT Partner Eco-System
Platform Partners
Solution Partners(not all shown)
German Delivery Lead
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CWIN16 IoT Portfolio | 28th September 2016
Focus on Use Cases with Measurable Impact
Quelle: Computerwoche Ausgabe 20. Juli 2015
QuickWins Sectors Crude retrofit sensorsConnectivity certainNo opt-ins
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CWIN16 IoT Portfolio | 28th September 2016
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
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CWIN16 IoT Portfolio | 28th September 2016
IoT-CloudState Engine
2. Machine Data
9. Alert & Task
Knowledge
Technician
7. Model + Error Code
8. ArticleServiceCloud
Assets
6. Model
5. Location + Error Code
Predictive Model
DB
RBDLaaS
4. Case
1. Input StreamsApamaComplex Event Processor
3. Descriptive Stream
WebMethodsInput Orchestrations
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
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CWIN16 IoT Portfolio | 28th September 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
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CWIN16 IoT Portfolio | 28th September 2016
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
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CWIN16 IoT Portfolio | 28th September 2016
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
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CWIN16 IoT Portfolio | 28th September 2016
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
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CWIN16 IoT Portfolio | 28th September 2016
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
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CWIN16 IoT Portfolio | 28th September 2016
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
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CWIN16 IoT Portfolio | 28th September 2016
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.
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CWIN16 IoT Portfolio | 28th September 2016
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?
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CWIN16 IoT Portfolio | 28th September 2016
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
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CWIN16 IoT Portfolio | 28th September 2016
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
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CWIN16 IoT Portfolio | 28th September 2016
Madness Factor 5. Expensive
Smart things cost as much as 10 times more than dumb things.
Quellen: BI Intelligence, TÜV Trust IT
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CWIN16 IoT Portfolio | 28th September 2016
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.
Hub Bridge EBS Gateway
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
Hub Bridge EBS Gateway
When many users want access to the same data, but for different purposes, we create a shadow farm.
This is not sustainable.
Hopper Grinder Mixer Chiller Grinder
TechManagerController Operator HR
7a. Aggregation at gateway
Hub Bridge EBS 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
Hopper Grinder Mixer Chiller Grinder
7c. Connect multiple databases.
Hub Bridge EBS Gateway
or the databases are integrated.
These options enable intelligence.
This is not actionable intelligence.
Hopper Grinder Mixer Chiller Grinder
8. Making data actionable.
Hub Bridge EBS Gateway
Intelligence and actionability are enabled, but users have to use multiple applications. This is not user friendly.
External data
Hopper Grinder Mixer Chiller Grinder
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
Hopper Grinder Mixer Chiller Grinder
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