@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