Talk at Poltcon New York- November 2016

1. @carlotorniai

2. • The 5th world’s large tyre
manufacturer
• Leader in the Premium and
Prestige market
• Only supplier of Formula 1 Tyre
• The Cal

3. Settimo Bollate
Slatina
Yanzhou
Merlo
Campinas
Bahia
Silao
Breuberg
Carlisle
Over 20 Manufacturing sites around the world

4. Smart Manufacturing - Industry 4.0
“ The current trend of
automation and data
exchange in manufacturing
technologies. It includes
cyber-physical systems, the
Internet of things and cloud
computing? - Wikipedia

5. Smart Manufacturing - Industry 4.0
“ The current trend of
automation and data
exchange in manufacturing
technologies. It includes
cyber-physical systems, the
Internet of things and cloud
computing? - Wikipedia

6. Real Time Analytics
Pirelli Smart Manufacturing Vision
Predictive
Manufacturing
Advanced Data
mining Data
products
Prescriptive
Manufacturing
Predictive Models
Algorithms
Detect trend, outliers,
issues
in (near) real time
Build and deploy models
that
Can forecast product
quality
from process data
M2M communication for
Process tuning and
resource allocation
With the goal o maximizing
quality and efficiency
ML + Smart
integrated
communication
Virtual
Factory

8. Factory
Local data
Local Analytics
Infrastructure
Issue
tracking and
Notification
system
(ICAP)
Hadoop
Cluster
Pirelli VPC / HQ
Data Products
Development and Deployment
Data Ingestion
ETL
Real Time Data
ML /
Analytics
Data
Products
Development
Factory users
Data Products
Interaction
Smart Alert
Notification
Pirelli Smart Manufacturing Architecture

9. Controlled user group Factory users
Production Deployment
Iteration loops
Fast prototyping
Data Products Development

10. Smart AlertingKPIs visualization
and analytics
- Data is not human interpretable
- Large Volume
- Non straightforward KPIs
- Machine Learning / Algos
Examples:
- Trends detection
- Anomaly detection in
production process
Alerts triggers actions that can
be validated by visualization
- Data is human interpretable:
- Small volume
- Straightforward KPIs
- Descriptive Analytics
Examples:
- Imbalance detection
(for production cycle times)
- Production efficiency KPIs
visualization
Real time Visualization triggers
actions
Data Products Categories

12. Fitting density distributions on
cycle time data
Detection of discrepancies
between different distributions
GOAL: Analyze process time
discrepancies on different
machines
Curing timeMachine 1
Machine 2
Machine 3
Example of KPI visualization: Cycle time Machine imbalance

13. Product
category 1
Product
category 2
Product
category 3
Cycle Time
Results are ordered
according to the
Discrepancy calculated
between the
distributions
Cycle-time density
distributions
Machine 1
Machine 2
Machine 3
Imbalance: Prioritization of intervention

14. Final products
Uniformity KPIs
(high dimensionality
data)
Step A Step B
Trend detection by
product category
Step C
Products processed in Step B within a time delta
contributed to the low final quality.
M-01
M-02
M-03
M-04
Example of Smart Alerting: Quality assessment

15. An alert is sent every
time a trend is
detected.
Python code running
on
renders reports for
decision support
Plotly with Pandas
(Cufflinks) served via
a

16. For each KPI we can identify trends using a Sliding window on a rolling basis
(4 hours batches, analysis is run every hour)
Time
Trend detection on uniformity KPIs
How did we implement and deploy it?

17. Implement tests (almost) from
scratch using Numpy/Scipy Use tests implementation from the R
packages (served by a Domino API
endpoint)
Option 1 Option 2
Deploying trend detection
Our codebase is mostly Python
Lots of R packages for Time series Analysis

18. This is all it takes to start an R API endpoint from Domino:
*Mann-Kendall test for monotonic trend in a time series z[t] based on the
Kendall rank correlation of z[t] and t (Hipel and McLeod, 2005)
Example of Python/R integration: trend detection on uniformity KPIs

19. Input time series
Test for trend significance
Example of Python/R integration: trend detection on uniformity KPIs

20. Machine learning Model (One-
class SVM with RBF kernel)
Batch model training
(~ once a week) on reference
data -> inliers’ dataset
Anomaly/novelty detection: i.e. classifying new data as similar or different to
the training set
Anomaly detection in production process: ML approach

21. We want to identify not the anomaly in “some” process parameters but
we want to label the process overall as an outlier
Normalized reference distributions for process parameters (training set)
p1 p2 …
Anomaly detection: a machine learning approach

22. Observation (1)
classified as inlier
Observation (2)
classified as outlier
Reference model
parameters distribution

23. Visualization plays a big role in factories as mean to convey key information to
the workforce in the field
Domino + plotly have provided a nice combination for:
• Fast prototyping / iterating with users in the exploratory phase
• Combine output from algorithms and Machine learning models into
interactive web-based visualizations to be used in production
Next steps:
• Expand towards predictive and prescriptive manufacturing
• Embed plotly within our data viz framework
• We are hiring
Summary