Use of data in Manufacturing

1. © CGI Group Inc. Internal use only.
Use of data in Manufacturing
”Sprøjtestøbesektionens Forårsmøde 2018”
Jens Christian Volhøj
Director, Consulting Services
CGI NEXT & Emerging Technology

2. Local experts
Vertical experience – e.g.:
Government, Health, Banking,
Insurance, Retail, Utility,
Communications, Manufacturing,
Logistic, Oil & Gas
Leading end-to-end IT and business
process services:
Business and IT consulting
Systems integration
IT managed services
Business process services
577employees in
5 cities in
Denmark
Technology expertise – e.g.:
Microsoft, Oracle, SAP, Open
Source – and more than 150 own
developed solutions
Strong foundation to support your business
goals:
Accountability, commitment and
more than 40 years of experience
Kolding
Aarhus
Aalborg
Ballerup
København
CGI
is the worlds
5th biggest
IT service provider
First class
Business and IT
consulting
71.000 employees;
approx.. 80% are
shareholders
Support over
5.000 customers
Globally from hundreds of
Offices
End-to-end-
services in IT and
Business processes

3. Self Service
Business Intelligence
Big Data
Augmented Reality /
Virtual Reality
Robotic Process Automation /
Compliance /GDPR
MDM/Data Quality/
Customer journey
Industry 4.0Internet of Things
(IoT)
Dashboards/KPIs
Video Analytics
Data Warehouse /
Data integration
Advanced Analytics /
Artificial Intelligence
Cloud
CGI NEXT & Emerging Technology - Focus Areas

4. 1778 1882 1969 NOW
INDUSTRY 1.0
Mechanization, Steam
Power
INDUSTRY 2.0
Electricity, Mass
Production
INDUSTRY 3.0
Automation, Computers,
Electronics
INDUSTRY 4.0
IoT, Cloud and Cognitive
Computing
Industrial Revolution

5. 5
Digital
Strategy
IoT - IIoT
Advanced
Analytics
Big Data &
Cloud
Augmented
Reality
Cyber
Security
Application
Development
Automation
“We create value for Industrial
customers through innovative data
driven solutions for Industry 4.0”
Software and IT related
areas in Industry 4.0

6. Enabling and consolidating data sources
6
Reference data
from ERP
Files from production
equipment (PLC’s with
UPC-UA interface)
Manufacturing
Execution System
CSV and Excel files
from users
Databases Web services IoT sensors
Cloud enables
powerful
data processing and
flexible storage

7. Data driven value creation in manufacturing with Analytics
7
Optimize Asset
Availability & Life
Reduce Failures,
Optimize Performance Decrease Planned &
Unplanned Maintenance
Improve
Safety
Automated
Inspection
Optimize Workforce
Productivity
Lower Risk
Exposure
Optimize Labor &
Operation Costs
Improve
Energy Cost
Efficiency
Reduce required
Compliance Activity
Improve
forecast
Improve
Quality

8. Video analytics for defect identification
• Deep learning artificial intelligence with unsupervised learning within video
analytics
• Time-efficient and accurate quality checking on every item on the production line
• Detect even unforeseen and rare defects
• Addition of x-ray imaging can detect defects such as voids in injection molded
plastic items, identifying weaknesses difficult to see from the outside
• Automatically collect statistics on different defect types to help improve
processes
sinkhole
0.93
pen 0.98

9. Data from plastic molding machines?
9
Examples of data:
• Heater Temperature and pressure
• Mold temperature and pressure
• Mold cooling temperature
• Screw rotation speed
• Backpressure
• Colour concentration and material degradation (Infrared)
• Vibrations and sound
• Energy consumption
• Machine configuration parameters
• Product quality and quality issues
• Batch data - Mould ID, work shift, operator, materials, etc.
• Video or pictures for analysis
Examples of Benefits:
• Intelligent recommendation for optimal configuration – maybe
automated
• Reduction of set up time
• Best practice “knowledge” data storage
• Increased productivity, process optimization and control
• Higher OEE (e.g. OEE = Availability × Performance × Quality)
• Improved Quality
• Scrap reduction
• Energy reduction
• Improved Sustainability
• Shorten time-to-market for new and highly competitive products
• Enable smaller batch size
• Automated inspection with e.g. Video analytics
• Predictive maintenance and less unplanned stops

10. Data Driven
Implement and monitor the right asset maintenance
strategy to meet business drivers and challenges
Maintenance
Reactive / unplanned
Corrective
Emergency
Proactive / planned
Predictive
Reliability-centred
Condition-based
Preventive
Constant interval
Age based
10
Source: Typology of condition based maintenance – Veldman, Wortmann & Klingenberg

11. Enablers for proof of value
11
IoT Explorer Kit
Proof of Value use cases
InnoLab
Training and work with own data
Advanced Analytics –
Workshop
Enabling existing tools like
MATLAB
Cloud enabling Analytics
Fast IoT deployment with pre-
defined templates
IoT Rapid deployment
1-2 weeks PoV exploration in
data set
Data Scientist Exploration

12. IoT Explorer kit
12

13. Innovate
Use cases
Explore
Potential
Capture
Value
Sustain
Benefits
• Innovate
• Identify
• Prioritise
• Analyse
• Prepare
• Explore
• Scale
• Implement
• Capture value
• Maintain
• Support
• Evolve
Approach to data driven value creation

14. 14
Commercial estimate of phases
Example from customer case
Duration: ≈180 hours
Output: • Catalogue of prioritized use cases
• PoC selection matrix
• Recommendations for next steps
Estimate: ≈ XXX.000 DKK
Duration: ≈ 300 hours
Output: • PoC findings and evaluation
• Qualification of value creation potential
• Recommendations for next steps
Estimate: ≈ XXX.000 DKK

15. 15
Recommendations
• Start with existing data and a known
problem to solve - keep it simple.
• If data don´t exist then start with simple
tools to collect data. Verify your hypnosis
before you do large investments.
• Find an experienced Data Scientist from
similar projects. Fast understanding of your
challenges gives fast results.
• Get input from external experts this will
enhance your results and help keep internal
focus on your daily operations.
• Create a good foundation for future analytic
projects. Share knowledge among multiple
employees and document findings.
• Use known methodology like the CRISP-
model (Cross Industry Standard Process
for data mining).

16. THANK YOU
16
Jens Christian Volhøj
Director, Consulting Services
jenschristian.volhoj@cgi.com
Lyngbyvej 28, 2100-Copenhagen
+45 29 49 89 22