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The 3 Ts The Making of a Successful DigiTal TransformaTion | April 11 Jon Melnick, Ph.D. Research Director, Lux Research
The 3 TsThe Making of a Successful DigiTal TransformaTion | April 11
Jon Melnick, Ph.D.
Research Director, Lux Research
Agenda
2
1 Digital transformation is hard
Understanding the 3 Ts2
Putting the Ts to work3
3
DATA
What is Digital Transformation?
MONEY
80-90% of
digital
transformation
projects fail
And it’s only getting harder, with more complexity
5
DIGITAL TOOLBOX
And it’s only getting harder, with more complexity
6
Machine
vision
Predictive
maintenance
Self-driving
cars
Blockchain
Augmented
reality
Cloud
computing
Digital Transformation failures fall into three categories
7
Team
Lack of executive buy-in
Lack of cross team buy-in
Executive/champion
turnover
User not interested or
not capable
Timing
Technology readiness
Timing mismatch with
business goals
Competitors’ maturity
Technology
Not secure data
Not adaptable
Too expensive
Lack of interoperability
8
Your competitors
are doing it
Rapid growth
opportunityFailure comes
with risk
So, why not just skip the whole thing?
Agenda
9
1 Digital transformation is hard
Understanding the 3 Ts2
Putting the Ts to work3
Overcoming Digital Transformation failures
10
Technology
TimingTeam
11
Technology
TimingTeam
Overcoming Digital Transformation failures
There are
so many
stakeholders
who need to
buy-in
Demonstrating goal metrics and calculating ROI helps to get
broad executive buy-in and mitigate turnover
13
Save $20K-$50K per year
per employee
Ease of use is key to entry-level user adoption
14
Overcoming Digital Transformation failures
15
Technology
TimingTeam
16
Technology
TimingTeam
Overcoming Digital Transformation failures
Technology
failures are a
fast way to bring
digital
transformation
to a grinding
halt.
Machine Vision started in
manufacturing and has been
finding new applications
19
Established applications
Machine Vision started in
manufacturing and has been
finding new applications
20
Established applications
Current Developments
Machine Vision started in
manufacturing and has been
finding new applications
21
Established applications
Current Developments Future Deployments
Machine Vision started in
manufacturing and has been
finding new applications
22
Current Developments
Machine Vision started in
manufacturing and has been
finding new applications
23
Current Developments
Source: CNET
Source: The Drive
Training data is the critical component for accurate
machine vision
24
Image sensor
Lens
Signal processing
electronics
Pre-processing algorithms
Computer vision algorithms
Training data
IMAGE ANALYSIS
Training data is the critical component for accurate
machine vision
25
Image sensor
Lens
Signal processing
electronics
Pre-processing algorithms
Computer vision algorithms
Large data set needed
Can introduce bias or mistakes
IMAGE ANALYSIS
Training data
Strategies for developing a training data set for machine vision,
each with pros and cons
26
STRATEGY:
1
2
3
Transfer Learning
CON: Less mature approach
PRO: De-facto large dataset with data exclusivity
Computer Vision as a Service
CON: Lose data exclusivity
PRO: Rapid access to large dataset
Large Proprietary Dataset
CON: Difficult to get / expensive, time-consuming
PRO: High-value differentiator
EXAMPLES
Overcoming Digital Transformation failures
27
Technology
TimingTeam
28
Technology
TimingTeam
Overcoming Digital Transformation failures
Is the
technology
ready for your
organizational
goals?
Understanding the readiness of your Digital Toolbox
30
Machine
vision
Predictive
maintenance
Self-driving
cars
Blockchain
Augmented
reality
Cloud
computing
DIGITAL TOOLBOX
Technology readiness needs to match organizational strategy
31
MORE
MATURE
LESS
MATURE
Understanding the readiness of your Digital Toolbox
32
Cloud computing
Machine vision
Blockchain
Predictive maintenanceAugmented Reality
Self-driving cars
MORE
MATURE
LESS
MATURE
Agenda
33
1 Digital transformation is hard
Understanding the 3 Ts2
Putting the Ts to work3
Operational
Consumer/Societal
Disciplined capital
Branding
Efficiency
Agility
Start your thinking at the top
34
Social consciousness
Health oriented
Financial/Marketing
Example organizations
MORE
MATURE
LESS
MATURE
Operations timing: fitting a 3 year timeline
35
Cloud computing
Machine vision
Blockchain
Predictive maintenanceAugmented Reality
Self-driving cars
MORE
MATURE
LESS
MATURE
Operations timing: fitting a 3 year timeline
36
Cloud computing
Machine vision
Blockchain
Predictive maintenanceAugmented Reality
Self-driving cars
Operations technology: Understanding the innovation landscape of
predictive maintenance (PdM)
37
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
0%
20%
40%
60%
80%
100%
Number of PdM publications per year, by type
Pd
M p
ub
lica
tio
ns
Share of PdM publications per year, by type
Academic
papers
Patent
publications
Academic
papers
Patent
publications
Operations technology:
Where do key predictive maintenance innovations come from?
38
0%
20%
40%
60%
80%
100%
Share of PdM publications per year, by type
Academic
papers
Patent
publications
Geographic distribution
39%
16%
13%46%
10%
18%4%
1%34%19%
0%
20%
40%
60%
80%
100%
Patent Publications Academic Papers
U.S. Europe China Japan Other
Operations technology:
What organizations have the key innovations?
39
0%
20%
40%
60%
80%
100%
Share of PdM publications per year, by type
Academic
papers
Patent
publications
Patent publications
Academic papers
Operations technology:
Choose a partner with the right feature set for your requirements
40 Report to be published shortly “Predictive Maintenance: A Pragmatic Outlook”
Key features include:
Analytics
Hardware
Business model
Operations team:
Choosing a partner for usability
41
Technology
TimingTeam
Predictive
maintenance
Build your toolbox to suit
? ?????
Multifunctional toolbox
Train and maintain
Use correctly
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Are you part
of the 80%
of failures or
20% of
successes?
www.luxresearchinc.com
@LuxResearch
Lux Research, Inc.
Lux Research
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Thank you for joining us.
Jon Melnick, Ph.D.
+1-617-502-5324
