Data driven value generation is possible through a structured approach: 1) Define goals and use cases to address, assess current IoT/data analytics maturity. 2) Collect, integrate and analyze relevant data sources using the "data value chain" methodology. 3) Generate insights and answers to business questions through visualization and exploitation of integrated data. 4) Realize value such as increased productivity, reduced costs or improved quality by applying insights.

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Data driven value generation. Is it possible?
M2M Summit 2017
11.10.2017, Köln
Tiemo von Hinckeldey

2. mm1 – Die Beratung für Connected Business
Content
A ▪ mm1 – a quick introduction
B
▪ The challenge
▪ Use cases (projects)
▪ The structured approach
2

3. mm1 – Die Beratung für Connected Business 3
mm1 – a quick introduction

4. mm1 – The Consultancy for Connected Business
mm1 is the Consultancy for Connected Business
4
Founded by experienced
McKinsey consultants
▪ 20 years of experience in Connected
Business.
▪ 300+ consulting projects focused on
digital product development.
▪ 60 consultants and a network of external
experts support high-profile clients in
telecommunications, insurance,
automotive, manufacturing and banking.
▪ Unique combination of strategy, product
and technology competence.
Specialized in technology
and business
transformation in the
telecommunications
industry
The Consultancy for
Connected Business
Expanding our scope:
automotive industry,
manufacturing industry,
and banking
Founding of mm1
Technology GmbH
(developing connected
business HW/SW
solutions)
1997
2000+
2007+
2016

5. mm1 – Die Beratung für Connected Business
One page about us: Introducing mm1 and it‘s focus
on„Industrial IoT“
5
1
2
3
4
Longstanding experience as
consultancy for
Connected Business with
widespread service offer for
Digital Transformation
Ability to deliver for industry
focussed projects, due to our
Industrial IoT practice and
extensive network
Successful projects for
development and implementation
of Connected Business- and
Industrial IoT Solutions
Applied procedure to build IIoT-
structures and business models
(‚Manage the IoT‘)
▪ Since 1997 in consultancy business; currently 60 consultants and approx. 250
experts as free lancer; awarded ‚Beste Beratung‘ 2016 and 2017
▪ Covers all aspects of Connected Business: From business model to technical
realization, from use case to data- and system architecture
▪ Outstanding combination of consulting and implementation performance for
Industrial IoT-Systems based on consultants and partners with longtime industry
experience
▪ ‚mm1 Industrial IoT Practice‘: 7 mm1-Consultants permanently acquire and
enlarge industial IoT knowledge
▪ >300 Digitalization projects in different industries (Telco, IoT, Connected
Mobility, Connected Finance); e.g. Design of intralogistics platform, Set-up of
M2M platform
▪ Numerous projects focussing on ‚Industrial IoT‘: Connecting subject expertise,
technology know-how and future trend analytics
▪ mm1-procedure for managing the (Industrial) Internet of Things describes main
areas for players in IIoT on 7 layers
▪ Different options for positioning in the IIoT-field are outlined along the value
chain

6. mm1 – The Consultancy for Connected Business
New in 2016: mm1 Technology – consulting and solution
development in combination
6
consulting technologyConsultancy for
Connected Business
The specialist for IoT wireless
connectivity solutions“We make
Connected
Business
champions!”
mm1 Consulting
& Management
PartG
mm1
Technology
GmbH
Experienced consultants
▪ develop connected business
strategies and business
models
▪ develop connected business
(product) propositions
▪ manage implementation and
market entry across functions
▪ drive transformations
(IT, organization, processes)
Experienced delevopers and
architects
▪ evaluate und create connected
business technologies (radio,
cloud, data)
▪ develop and implement IoT
connectivity solutions (software,
hardware)

7. mm1 – Die Beratung für Connected Business 7
Challenge I Use Cases I Methods

8. mm1 – Die Beratung für Connected Business 8
Realising added value from production data requires new
competences and commitment to invest and taking risk
Data collection &
visualization:
▪ Sensors and bus interfaces
widespread in production
▪ Visualization often available
at machine or production
cockpits
▪ Basic technical skills needed
Data analysis:
▪ New technology developing
with high speed
▪ Complex, long lasting
projects
▪ Special skills in data analytics
required
Moderate,
but proven
value
High value
potential, but
not obvious
Invest
and
taking
risk
Value creation
Hurdle:
Skill gap, Invest
and
risk taking

9. mm1 – Die Beratung für Connected Business
„Big Data Analytics“ become an enabling function for
higher machine availability. Predictive maintenance.
9
▪ To improve the market position a producer of
machines wants to deploy added services such as
predictive maintenance
▪ This shall be done via an integration in service
processes – visualization in a CRM-System
▪ Therefore indicators for machine failures need to be
identified and analyzed
▪ Analysis of 3.1 mn. data points of defected and
faultless machines for comparing Machine-KPIs and
patterns
▪ Visualization of six main defect-events and the
corresponding patterns of the specific machine
components as well as the preliminary time
▪ Development of three data-based services
▪ Transparency on available sensor-data and the
connections between defect events in
machines and the prediction value
▪ Structured approach for further analysis of
optimization potentials
▪ Further development and introduction of
business models based on predictive
maintenance
Approach & Solution
Situation & Problem
Result

10. mm1 – Die Beratung für Connected Business
A highly IoT capable assembly line delivers miserable
productivity
10
Situation
▪ Higher downtime than production time
▪ Stations failure with random alternation
▪ Inline testing delivered different results
▪ Overlaying error effects / hysteresis
▪ #200 out of #1.000 parts „OK“
S
S
S
S
Out
In
S
Testing
Central control unit
Mobile terminal
The assembly line
▪ 18 stations (handling robots, one welding laser,
assembly stations, inline testing)
▪ Fully automated with real-time dashboard,
mobile control terminals, data logfiles, etc.
Challenge
▪ Meet demand quantities (productivity)
▪ Regain supply backorder (few weeks)

11. mm1 – Die Beratung für Connected Business
Three different approaches for increasing output. Data
Analysis Team added 500 OK parts with lowest time and
project cost
11
A - Standard procedure
B - Set-up new test bed
C - Data analysis
Team B
+ 50 OK parts
Team C
+ 500 OK parts
Time (month)
Project cost
(FTE)
Team A
+ 150 OK parts
1
2 3 4 5
2
3
4
5
1
Applied process
optimization methods
such as Kaizen
Built the existing test bed
a second time with
improvements
▪ Harmonization and
creation of data basis
▪ Data consolidation and
analysis

12. mm1 – Die Beratung für Connected Business 12
To create value, data must be taken to the analysis level
and processed via a structured approach
Up to 20%
increase of asset
productivity
Potential Analytics-
ROI in manufacturing*
Up to 10%
reduction of
maintenance costs
Up to 30%
reduction in yield
detraction
Up to 90%
increased defect
detection rates
* McKinsey (2017): Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector?
Leveraging it starts with 3 questions…
Which goal should
be achieved?
Which use case shall
be addressed?
What is my IoT/
data analytics maturity?
„We have to reduce our
plant operation costs“
„The number of maintenance
cycles shall be reduced“
„Our plant is fully digitized but
we have no data analytics skills“
…and is facilitated by a structured approach
?
How can
these
potentials
be realized?

13. mm1 – Die Beratung für Connected Business
To overcome the challenges, the Data Value Chain is a
straightforward goal-oriented approach for leveraging the
data value potential
13
Collection Integration Exploitation
Activities
a) Definition and annotation:
▪ Formulate questions
▪ Structure and describe internal and
external data sources
b) Organization:
▪ Define the structure of data sources
and make them available
c) Gathering:
▪ Gather data for each question
Integration:
▪ Establish a common data
representation for all data and
integrate the raw data
a) Analysis:
▪ Analyze the integrated data and
generate an answer to each question
b) Visualization:
▪ Visualize the results as text or figures
c) Usage:
▪ Derive meaning based on the
answers from the system
1 2 3
Examples
Questions:
▪ „Which environmentmal conditions
trigger machine failure?“
▪ „Which products are most effected
by machine failure?“
Data Sources:
▪ e.g. machine sensors, environmental
data, product outputs
Integration:
▪ e.g. combination of historic
environmental data and machine
downtime data for each machine
Answers:
▪ „Humidity >x% increases probability
of machine failure significantly“
▪ „Product xyz produces particularly
high machine failure rates“
Usage:
▪ „Install humidity sensors and
dehumidifiers“
▪ „Produce xyz on a newer machine“
Insights and value generation

14. mm1 – Die Beratung für Connected Business
Everyone, who starts to implement IoT-solutions for
leveraging data will improve the value creating process
14
Visualization &
Analysis
 Eventbased visualization
 Manual process capabilites
 UIs device related
Parts logistics
 Occasional part tracking
 manual recording
 Applying state-of-the–art UI-
development tools (web
components, reactive,
responsive)
 End-to-end part tracking from
order to delivery
 Integration of sensors
 Real-time Dashboard
 Autonomous analysis – Big Data
becomes Smart Data
 Tracking of all process steps /
Operations
 Automization of sensor –
platform communication
IoT Value
Chain
Smart
Modules
Applications
KPI
Smart Objects
Digital Starter Transformator IoT-Champion
1 2 3
▪ Transparency (vs. Data mix)
▪ Visualized connection of
failures and data
Enabled for quick decision
making eg. prevent
contractual penalties
Advanced competences for
realizing efficiency potentials
e.g. avoiding high
maintenance costs –
„Predictive Maintenance“
Value on each level
(examples)
Connectivity
Platform
SW Custom.
Extraction: Maturity Level of Connected Business Readiness

15. mm1 – Die Beratung für Connected Business
Data driven value creation. Is it possible?
▪ It‘s possible!
▪ It‘s reality
▪ It‘s valuable, wherever you start from
▪ But, you need…
– a clear target
– a structured approach
– data analytics competence
– …and a little invest
15