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"How Pirelli uses Domino and Plotly for Smart Manufacturing" by Alberto Arrigoni, Senior Data Scientist, Pirelli (pirelli.com)

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"How Pirelli uses Domino and Plotly for Smart Manufacturing" by Alberto Arrigoni, Senior Data Scientist, Pirelli (pirelli.com)

Abstract:

Pirelli, a global performance tire manufacturer, uses data science in its 20 factories to improve quality and efficiency, and reduce energy consumption. For this “Smart Manufacturing” initiative, Pirelli’s data science team has developed predictive models and analytics tools to monitor processes, machines and materials on the factory floors. In this talk we will show some of the solutions we deploy, demonstrate how we used Domino’s data science platform and Plot.ly to build these solutions, and discuss the next steps in this journey towards predictive maintenance.

Bio:

Alberto Arrigoni is a data scientist at Pirelli, where he works to process sensors and telemetry data for IoT, Smart Factories and connected-vehicle applications.
He works closely with all major business units such as R&D, industrial engineering and BI to develop tailored machine learning algorithms and production systems.
He holds a PhD in biostatistics from the University of Milan Bicocca and prior to joining Pirelli was a staff data scientist at the National Institute of Molecular Genetics (Milan), as well as a Fulbright student at the Santa Clara University and visiting PhD student at Pacific Biosciences (Menlo Park, CA).

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"How Pirelli uses Domino and Plotly for Smart Manufacturing" by Alberto Arrigoni, Senior Data Scientist, Pirelli (pirelli.com)

  1. 1. "How Pirelli uses Domino and Plotly for Smart Manufacturing" Alberto Arrigoni, PhD. DSA, Pirelli
  2. 2. Settimo Bollate Slatina Yanzhou Merlo Campinas Bahia Silao Breuberg Carlisle Over 20 Manufacturing sites around the world
  3. 3. 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
  4. 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. 5. 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 of maximizing quality and efficiency ML + Smart integrated communication Virtual Factory
  6. 6. 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
  7. 7. Controlled user group Factory users Production Deployment Iteration loops Fast prototyping Data Products Development
  8. 8. 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
  9. 9. 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
  10. 10. 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
  11. 11. 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
  12. 12. 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
  13. 13. 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?
  14. 14. 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
  15. 15. 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
  16. 16. Input time series Test for trend significance Example of Python/R integration: trend detection on uniformity KPIs
  17. 17. 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
  18. 18. 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
  19. 19. Observation (1) classified as inlier Observation (2) classified as outlier Reference model parameters distribution
  20. 20. 1. User centric approach: factory folks are the key 2. Provide tools for data exploration to factory folks 130 people Trained Trained from exploratory data analysis to deploy a web app Smart manufacturing: training for people in the factories
  21. 21. 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 Summary

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