The webinar held on October 20, 2022, focused on digital transformation and process optimization in manufacturing, emphasizing the integration of AI across business platforms. Key insights included the necessity of transitioning from siloed operations to a unified approach, facilitating improvements such as inventory cost reduction and increased productivity. The presentation also discussed the importance of anomaly detection in predictive maintenance and the advantages of using tools like BigML for realizing business value through digitalization.

1. Webinar 20.10.2022
A1 Digital and BigML
Digital Transformation
and Process Optimization
in Manufacturing

2. This live webinar is being recorded.
You are muted.
You will receive an email after the webinar containing
the recording link, contact information etc.
About this webinar
Please type your questions as a text message
in the webinar tool.
?

3. Speakers and moderation
Keyanoush Razavi
Digital Business Consultant
A1 Digital
Guillem Vidal
Senior Data Scientist
BigML
Philipp König
Product Marketing Manager
A1 Digital

4. .csv
THIS IS NOT SCALABLE
Why I care about Digital Transformation

5. Being AI first means using it
last.
- Will Grannis, CTO at Google

6. § The best AI isn‘t in a separate technology
silo
§ AI needs to be everywhere – across all
platforms, products, and services
Being AI first means using it
last.
- Will Grannis, CTO at Google

7. Transformation
The Traditional
Siloed Company
Silo 2 Silo …
Silo 1
Department 1 Department 2 Department …
Models Models
Models
Data Data
Data
Silo
…
Running Pilots in Departments
Silo
2
Silo
3
Silo
1
Data
Data
Models Models
Data
Data
Common Data Platform
Use
Case 2
Use
Case …
Use
Case 1
Common Libraries
Data
Performance
PoC Stage
Development Stage
Common AI Platform
Common Libraries / ML Models
Data
Agile Teams
APIs
AI Factory
+
Digital Transformation – A Necessity to Scale the Business
D
I
G
I
T
A
L
T
R
A
N
S
F
O
R
M
A
T
I
O
N

8. Transformation
The Traditional
Siloed Company
Silo 2 Silo …
Silo 1
Department 1 Department 2 Department …
Models Models
Models
Data Data
Data
Silo
…
Running Pilots in Departments
Silo
2
Silo
3
Silo
1
Data
Data
Models Models
Data
Data
Common Data Platform
Use
Case 2
Use
Case …
Use
Case 1
Common Libraries
Data
Performance
PoC Stage
Development Stage
Common AI Platform
Common Libraries / ML Models
Data
Agile Teams
APIs
AI Factory
+
Digital Transformation – A Necessity to Scale the Business
D
I
G
I
T
A
L
T
R
A
N
S
F
O
R
M
A
T
I
O
N

9. D
I
G
I
T
A
L
T
R
A
N
S
F
O
R
M
A
T
I
O
N
15-20%
Inventory-holding
cost reduction
15-30%
labor-productivity
increase
30-50%
Machine downtime
reduction
10-30%
throughput
increase
Digitally-Enabled Industries Provide Value
*McKinsey – Capturing the true value of Industry 4.0

10. D
E
C
I
S
I
O
N
F
R
A
M
E
W
O
R
K
Up-Front Analysis of the
entire manufacturing
network
Value-driven
rollout. Business
challenges,
Cross-functional teams,
planning and regular
communication.
Business needs and
current performance
challenges.
Three- to five year
vision of the digital
strategy journey.
Digital Strategy – Setting the Groundwork

11. D
E
C
I
S
I
O
N
F
R
A
M
E
W
O
R
K
Digital Strategy – Ask the Right Questions
Top three
strategic
priorities
Core services
delivered
Value of
digitization
Decision
Making
Bottlenecks
Existing
Software

12. Monitoring of energy consumption in a manufacturing plant
U
S
E
C
A
S
E
–
V
A
L
V
E
S
&
A
C
T
U
A
T
O
R
S

13. Monitoring the utilization of machines in metal processing
Comparison of production lines Detection of uneven workload Anomaly detection
U
S
E
C
A
S
E
–
V
A
L
V
E
S
&
A
C
T
U
A
T
O
R
S

14. „I suppose it is tempting, if the one
tool you have is a hammer, to treat
everything as if it were a nail.“
- Adam Maslow
Transition to

15. 4 Machine Learning Use Cases
Film metallization outcome prediction
Tunnel machine oil temperature analysis
Chemical plant outcome optimization Weld errors detection

16. Anomaly Detection
An unsupervised machine learning algorithm that looks for unusual instances in a dataset.
Anomaly detectors provide an anomaly score to each instance, the higher is the score the
most unusual is the instance. Example:
date customer account auth class zip amount
Mon Bob 3421 pin clothes 46140 135
Tue Bob 3421 sign food 46140 401
Tue Alice 2456 pin food 12222 234
Wed Sally 6788 pin gas 26339 94
Wed Bob 3421 pin tech 21350 2459
Wed Bob 3421 pin gas 46140 83
Thr Sally 6788 sign food 26339 51

17. 27.01.22
Webinar: Fünf IoT-Baustein, die Ihr Smart Device benötigt 17
Isolation Forest: Grow random decision
trees until each instance is in its own leaf
using random features and splits
Isolation Forest
“easy” to isolate
“hard” to isolate
Depth
Now repeat the process several times and
use average depth to compute anomaly
score: 0 (similar) 1 (dissimilar)

18. 27.01.22
Webinar: Fünf IoT-Baustein, die Ihr Smart Device benötigt 18
Isolation Forest Splits
https://arpitbhayani.me/blogs/isolation-forest

19. 27.01.22
Webinar: Fünf IoT-Baustein, die Ihr Smart Device benötigt 19
Factory Failures Demo
A fake dataset is used for demonstration purposes
Each row represents a factory moment
A label field marking failures exist, the data is very unbalanced
id min_x avg_x avg_y avg_current
0 -1.3598 0.0727 2.5363 1.3781
0 -1.1918 0.2661 0.1664 0.4481
1 -1.3583 1.3401 1.7732 0.3797
1 -0.9662 0.1852 1.7929 -0.8632
2 -1.1582 0.8777 1.5487 0.4030
2 -0.4259 0.9605 1.1411 -0.1682
4 -1.2296 0.1410 0.0453 1.2026
7 -0.6442 1.4179 1.0743 -0.4921
7 -0.8942 0.2861 -0.1131 -0.2715
9 -0.3382 1.1195 1.0443 -0.2221
10 -1.4490 1.1763 0.9138 -1.3756
noise avg_z force failure
0.1335 -0.0210 149 0
0.0089 0.0147 269 0
0.0553 -0.0597 378 0
0.0627 0.0614 123 0
0.2194 0.2151 69 0
0.2538 0.0810 367 0
0.0345 0.0051 499 0
1.2069 1.0853 408 1
0.0117 0.1424 932 0
0.2462 0.0830 368 0
0.0428 0.0162 78 0
…
…
…
https://bigml.com/user/vidal/gallery/dataset/6150a84d9193b9173301b015

20. 27.01.22
Webinar: Fünf IoT-Baustein, die Ihr Smart Device benötigt 20
Failure Detection Results
Decision Threshold = 0.53
Actual Predicted No Failure Failure
No Failure 8491 True Negatives 127 False Positives
Failure 3 False Negatives 27 True Positives
Precision = 27 / 127 = 17.5%
(one in every 5 failure predictions would be correct)
Recall = 27 / 30 = 90%
(90% of all failures would be detected)
Precious feedback
for plant operators

21. 27.01.22
Webinar: Fünf IoT-Baustein, die Ihr Smart Device benötigt 21
Summary
• Anomaly detectors can be an unsupervised alternative to classifiers in extremely unbalanced datasets.
• Failures detection is an example, predictive maintenance or fraud detection can be addressed similarly.
• With this approach the most challenging aspect is finding the features that work.
• False positives are difficult to eliminate, human supervision or posterior treatments are recommended.
HISTORIC
FACTORY
ACTIONS
ANOMALY
DETECTOR
NEW
FACTORY ACTIONS
ANOMALY
SCORE
KEEP HIGH
SCORES
SUSPICIOUS
FACTORY
ACTIONS
PLANT OPERATOR
EXPERT

22. Value
Scale
Traditional Operating
Model
Digital Operating
Model
Give up
here
Scalable tools like BigML and the Exoscale cloud are at
the core of the transformation.
Digital Transformation and
Process Optimization
in Manufacturing
Conclusion

23. 1500+
customers
worldwide
Data centers
in 4 European countries
Founded in 2017,
part of A1 Telekom Austria
Group
Around 200 employees
Locations in Munich, Vienna
& Lausanne
Portfolio:
IoT & ML
Security & NaaS
Cloud (Exoscale)
A
1
D
I
G
I
T
A
L
We make digitalization happen.

24. Hardware
On-Premise
Data Center
Processes
Measurement
Hardware
Connectivity Monitoring
Platform
Analytics
+
Machine Learning
Technology Experts
Industry Experts Industry Experts
New Business
models
A
1
D
I
G
I
T
A
L
A1 Digital – Technology Experts

25. Request free 2h exploration workshop
Discuss your idea with one of our data scientists. Together, we
analyze potential use cases to generate business value from your
data. Here’s what you get after the workshop:
§ A clear idea of your use case and the potential solution.
§ Identification of the “low-hanging fruits” for fast results.
§ A plan for the next steps.
§ Yes, it’s free. And without a commercial break.
www.a1.digital/iot/advanced-analytics/

26. Contact
Keyanoush Razavi
Digital Business Consultant
A1 Digital
Guillem Vidal
Senior Data Scientist
BigML
vidal@bigml.com
keyanoush.razavidinani@a1.digital