My presentation from IoT Tech Expo 2017 - Connected Industry Track. Berlin, Germany. June 2017

1. Current state of Industrial IoT / Industrie 4.0 markets
Knud Lasse Lueth, IoT TechExpo Europe, Berlin, June 2017

2. Today’s “Connected Industry” lineup
Managing Director, IoT Analytics –
Market Insights for the Internet of
Things
My focus: In-depth market research
on IoT and Industry 4.0
Chair
Knud Lasse Lueth
Speakers / Moderators / Panelists
Today’s focus areas: Industrie 4.0, Edge, Platform, Smart Factories, Automation, M2M, Analytics, PdM
@KnudLueth
@AnalyticsIoT

3. 3
2
1
4
5
6
7
8
9
10
Americas Europe APAC Trend2
43% 30% 20%
31% 47% 15%
49% 24% 25%
43% 33% 17%
46% 33% 13%
48% 31% 17%
48% 33% 12%
61% 30% 6%
52% 30% 13%
57% 35% 4%
1. Based on 640+ publicly known enterprise IoT projects.(Not including consumer IoT projects e.g., Wearables, Smart Home) 2. Trend based on IoT Analytics’s Q2/2016 IoT Employment Statistics Tracker 3. Not including
Consumer Smart Home Solutions Source: IoT Analytics 2016 Global overview of 640 enterprise IoT use cases (August 2016)
Smart City
Connected Car
Smart Energy
Connected Industry
Other
Smart Supply Chain
Connected Health
Smart Retail
Connected Building
Smart Agriculture
APAC MEA N/AAmericas Europe
IoT Segment Global share of IoT projects1 Details
6%
8%
20%
22%
13%
13%
5%
5%
4%
4%
N = 640 global, publicly
announced IoT projects
3
Q3/2016
Starting point: We see “Connected Industry” at the forefront of IoT today
Analysis based on 640 real enterprise IoT projects (excl. Consumer IoT)

4. However, we still have ways to go…
Industrie 4.0
Today, we are HERE
• Software platforms just being created  Immature
• Many pilot projects  No real success story
• No “one architecture standard”  Nothing works plug&play
• Right skills not on board  No data scientists & more
• Mindset and org structure not changed  Not ready for
software and data-driven service business models
Source: IoT Analytics

5. Within “Connected Industry”, PdM will be the #1 use case in 2025
Based on IoT Analytics Industry 4.0 Market Model
5
0% 30%10% 20% 50%40% 60%
Annual growth until 2025
Market Size
in 2025
Remote Asset Testing&Inspection
Data-driven
Inventory Optimization
Predictive Maintenance
Data-driven Asset/
Plant Performance Optimization(1)
Movable Asset Tracking /
Fleet Management
Data-driven
Quality Control
Additive Parts
Manufacturing Human Robot
Collaboration
Advanced Digital
Product Development
Remote Service
Augmented Operations
Bubble Size indicates the market opportunity in 2025
1. Excluding Predictive Maintenance
Source: IoT Analytics

6. Deep-dive Predictive Maintenance (1/2):
Efficiency gains of 20-43% reported
Copyright©2015bywww.iot-analytics.comAllrightsreserved
6
Item Value Comment
Number of switches worldwide 2,300,000 In all countries
Current avg. downtime per switch 4,000 minutes p.a. German railway example
PdM solution effect 43% delay minute reduction Pilot project with German railway
Annual savings per switch $160k per switch Calculated by Konux
Potential global savings $368B for all switches p.a. Extrapolated, feasibility not checked
Business Case provided by Konux
“Our first pilots with real predictive maintenance solutions show efficiency gains of approx. 25%”
Head of Technology Research at Major Engine Manufacturer
Sample business case
($368B TPM1 for railway switches)
1
Real results
(20% maintenance reduction
for printing machinery)
PdM use case for Häuser KG (Customer of
Heidelberger Druck printing machinery):
• Overall maintenance time for entire production
line reduced by 20%
• Unplanned maintenance redcued by almost 50%
Maintenancehours
2
3
Source: IoT Analytics Predictive Maintenance Market Report
IoT Predictive Maintenance Market Report
Source:

7. Deep-dive Predictive Maintenance (1/2):
Roll-out example at thyssenkrupp
7
Name
Description
Solution Brief
Project description
Further resources
Technology partners
https://www.microsoft.com/en-us/cloud-platform/customer-stories-thyssenkrupp-elevator
https://www.thyssenkrupp.com/en/newsroom/press-releases/press-release-61632.html
Latest information
April 2016: thyssenkrupp launches MAX elevator service in Germany
September 2016: Microsoft equips thyssenkrupp‘s technicians with HoloLens
Elevators monitor various parameters e.g.,
• Motor temperature
• Shaft alignment
• Cabine speed
• Door functioning
Data from elevators is transmitted into the cloud and combined into a single dashboard
with two additional functionalities:
• Alarms that indicate an immediate issue
• Events, which are stored and used for management
The events are fed into dynamic predictive models, which continually update datasets
via seamless integration with the Intelligent Systems Service. In the case of failure, the
Elevator sends out an error code and informs technicians on the 3-4 most probable
causes for its defect.
Technicians are provided with instant diagnostic capabilities and rich, real-time data
visualization via mobile devices.
Project initiation: CEO-driven. TKE CEO Andreas Schierenbek met with Microsoft
CEO Steve Ballmer at a Silicon Valley trip, kicked off collaboration.
Organization: Joint effort of a TKE-led project team with Microsoft software
engineers and data scientists (Total of 50 people involved – 2 years development
time)
Technology
Elavator manufacturer thyssenKrupp has launched the “MAX” predictive
maintenance service for its elevators, by connecting sensors and systems to the
cloud.
Company: thyssenkrupp Elevator Technology
Location: Essen, Germany
Company size: ~ 50,000 employees
Project Start Date: 10/2015 (Market intro)
Status: In deployment in US, Spain, Germany
No. of connected devices: Unknown (Goal: 180k by 12/2017)
“MAX” Predictive Maintenance
Solution for Elevators
Cloud services
Azure Machine Learning
Source: thyssenkrupp Elevator Technology, Microsoft, IoT Analytics Research
Predictive Maintenance – Market Report 2017-2022
M2M Connectivity
Integration

8. Why so slow?: Missing Interoperability & Data Science Skills
See the “Industrial Analytics 2016/2017” report for further details (Free download at IoT Analytics website)
Technical challenges: #1 Interoperability Skill gaps: #1 Data Science
Source: IoT Analytics

9. Key questions we get from people implementing IoT…
o What are examples of projects “killing it” in the industry with IoT?
o Which standards should we work with?
o How should we tackle the “security” topic?
o Should we rather work with a horizontal cloud player or a focused niche IoT Platform?
o How should we think about data governance? Who gets to see the data?
o How to price our new “connected product” service?
Today’s goal: Provide answers to some of these questions…

10. IoT Analytics at a glance
Who we are: IoT boutique, Germany-based
Who we serve: 100+ customers to date…
What we do: Data-driven IoT market insights
Who we reach: 40k+ people in IoT per month
1
Latest (free)
IoT Insights
Average monthly website visitors (Q4/2016)
Cumulated amount of social media followers (Q4/16)
~40,000
~10,000
Founded: 2014
Headquarters: Hamburg, Germany
Team: 10 people - Background in
Consulting, Data Science,
Telco/IT architecture
Key focus areas: IoT Platforms, IoT security, Smart
Home, Industrial IoT
2 3
Detailed
Market Reports
Go-2-Market
Services
IoT decision-makers subscribed to email market updates (Q4/16)
~6,000
@KnudLueth @AnalyticsIoTTo stay up to date, sign-up to our Newsletter: https://iot-analytics.com/newsletter-sign-up
FREE reports (selection)
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• IoT Security Market
Report
• Industrial IoT /
Industry 4.0 Market
Report
• IoT Platforms
Company List
IoT Solution Deve-
lopment Guide
Industrial Analytics
Report 2016/2017

11. IoT Technology
5 layers of an
end2end IoT
solution
,2G/3G/4G
Find out more in our
free white paper:
https://iot-
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