The presentation discusses the application of data science in ambient intelligence and industry, focusing on methodologies like CRISP-DM and the importance of data acquisition. It emphasizes lessons learned from case studies, including predictive maintenance and the connection between research institutions and companies. The presenter encourages adapting processes, finding the right tools, and integrating data-driven methodologies into business operations.

1. Lessons learned from
data science in
industry
Jeroen Linssen
Ambient Intelligence
Twente Data Meetup, Saxion edition
2019-11-07
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2. Some personal data
Associate Lector Ambient Intelligence @Saxion
Artificial Intelligence
Human Media Interaction
Human-Robot Interaction
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3. Main data points for this evening
Ambient Intelligence
Lectoraat
Data mining methodology
CRISP-DM
Use cases in industry
Lessons learned
Other applications
Predictive maintenance; fashion
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4. The lectoraat Ambient Intelligence
Think
Act
Sense
Connected
Embedded
Systems
Applied Data
Science
Augmented Interaction
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5. Applied Data Science in short
THINK
Sense Act
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6. Ambient Intelligence: the people
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Smart Industry
Sport (Health)
Safety (Areas)
Augmented
Interaction
Connected
Embedded
Systems
Applied Data
Science
29 members
10 FTE
~40 students

7. Technology Readiness Levels
Fundamental research → ‘into the wild’
Direct involvement with companies (SMEs)
Link between universities and company R&D
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8. Industry 4.0
8Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing.
Journal of Manufacturing Systems, 48, 157-169.

9. Data in smart industry
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Business question → Acquisition → Analysis → Feedback
‘We want to do
something with data…!’

10. Where is the data?
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Product & Manufacturing Process Design
• Marketing Research
• Demand Research
• Process Parameters
• Raw Material SCM
• Scheduling /Job shop
Production
• Equipment and Process Control
• Equipment Monitoring Maintenance
• Quality Control
• Energy Optimization
Sales & Service
• Marketing
• Supply chain management
• Customer service

11. CRISP-DM
CRoss-Industry
Standard Process
for Data Mining
Data acquisition
Sensor solutions (IoT)
Data analysis
Machine learning for predictive models
Data visualization
From data to information
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12. The CRISP-DM cycle
Nice, but data acquisition…?
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13. CRISP-DMME
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Huber, S., Wiemer, H., Schneider, D., & Ihlenfeldt, S. (2019). DMME: Data mining methodology for
engineering applications–a holistic extension to the CRISP-DM model. Procedia CIRP, 79, 403-408.

14. Data acquisition at EuroMouldings
Case
 Study power consumption
 Energy usage per resource
Approach
 Continuously monitoring power
 Monitoring ‘idle time’
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15. Analysis for Nijhuis
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16. Analysis for Nijhuis
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17. Overview of IoT frameworks
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18. Overview of tools for data analysis
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19. What’s possible and what might be (or not)?
‘We want to do something with data…’
- With which purpose?
- Which data?
- Is it available already?
- Can it be acquired?
- Is the data ‘rich enough’?
- What does preliminary inspection of the data show?
- Who will do it?
- How will it be integrated within the business process?
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20. Scania Uptime Improvement
Zwolle: 200 trucks a day
500 euro/min stop time
Decreasing downtime
- Predicting failures
- Predictive maintenance
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21. Passende Mode via Internet
https://youtu.be/XvOM29ZtIeU
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22. Lessons (to be) learned
Work with a process model
(and adapt it)
Find the right tools for the trade
(and the available people)
Get your hands on the data
(prepare for the worst, hope for the best)
Integrate new information and new methodologies in the business process
(how?)
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23. Thanks for your attention! Want more?
Free coffee on the 4th
floor of Epy Drost in
Enschede!
Send an email!
j.m.linssen@saxion.nl
Visit the website of
Ambient Intelligence!
saxion.nl/ami(Data mining)