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@carlotorniai
• The 5th world’s large tyre manufacturer
• Leader in the Premium and Prestige market
• Only supplier of Formula 1 Tyre• The Cal
Settimo Bollate
Slatina
Yanzhou
Merlo
Campinas
Bahia
Silao
Breuberg
Carlisle
Over 20 Manufacturing sites around the world
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
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
Real Time Analytics
Pirelli Smart Manufacturing Vision
Predictive Manufacturing
Advanced Data mining Data
products
Prescriptive Manufacturing
Predictive ModelsAlgorithms
Detect trend, outliers, issuesin (near) real time
Build and deploy models thatCan forecast product quality from process data
M2M communication for Process tuning and resource allocationWith the goal o maximizing quality and efficiency
ML + Smart integrated
communication
Virtual Factory
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 / AnalyticsData ProductsDevelopment
Factory users
Data ProductsInteraction
Smart Alert
Notification
Pirelli Smart Manufacturing Architecture
Controlled user group Factory users
Production Deployment
Iteration loops
Fast prototyping
Data Products Development
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
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 2Machine 3
Example of KPI visualization: Cycle time Machine imbalance
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 1Machine 2Machine 3
Imbalance: Prioritization of intervention
Final productsUniformity 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
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
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?
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 PythonLots of R packages for Time series Analysis
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
Input time series
Test for trend significance
Example of Python/R integration: trend detection on uniformity KPIs
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
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
Observation (1) classified as inlier
Observation (2) classified as outlier
Reference model parameters distribution
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