SlideShare a Scribd company logo
How a global manufacturing
company built a data science
capability from scratch
@carlotorniai
Head of Data Science and Analytics
Pirelli
Outline
§ Why Data Science and Analytics in Pirelli
§ What did we do differently
§ Lessons learned
Pirelli
§ The 5th world’s largest tyre
manufacturer
§ Leader in the Premium and Prestige
market
§ Only supplier of Formula 1 tyres
§ The Calendar
Why Data Science and Analytics in Pirelli?
§ Capitalize on the amount of
data available
§ Build services around data
§ Drive a cultural change
Main clusters of activities
Smart Manufacturing Integrated value Chain
Demand forecasting
Services built on top of
Cyber Technologies
What didn’t work before?
§ Tech-centered approach
within IT
§ Old approach: client -
supplier relationship
§ Core competence
outsourced
What did we do differently?
§ People
- Org structure and team
composition
§ Process
- Agile to break silos
§ Technology
- Right tools for the task
People: outside the company grid
§ Start up
§ Outside ICT
§ Reporting directly to the
CTO
People: insource the right talents
§ Diversity of backgrounds
§ Small and flat organization
§ Be as much “independent”
as you can across the full
DS spectrum
Process: agile to break silos
§ Transparency and trust
§ Break the contract game
§ Dealing with uncertainty
§ Cross team and cross
hierarch interaction
Process: how to stick around
§ Business driven
§ Have clear KPIs
§ Identify actionable items
§ Redefine the “idea” be data
driven
Technology: right tools for the task
§ It’s never about the tools
(first)
§ Democratising data and
enable smart data
interaction at every level of
the organization
§ Choose the right tools at
the right time
Tech stack and architecture evolution
MES
Local repo
Hadoop
Cluster
ETL
pipelines
PirelliVPC AWS Factory
Tech stack and architecture evolution
PirelliVPC AWS Factory
MES
Local repo
Hadoop
Cluster
ETL
pipelines
Analytics
Infrastructure
Tech stack and architecture evolution
PirelliVPC AWS Factory
MES
Local repo
Hadoop
Cluster
ETL
pipelines
Analytics
Infrastructure
Local analytics
Infrastructure
Data Products
Dev & Deploy
Tech stack and architecture evolution
PirelliVPC AWS Factory
MES
Local repo
Hadoop
Cluster
ETL
pipelines
Analytics
Infrastructure
Local analytics
Infrastructure
Data Products
Dev & Deploy
Issue tracking
Notification
Smart
Alerts
Tech stack and architecture evolution
PirelliVPC AWS Factory
MES
Local repo
Hadoop
Cluster
ETL
pipelines
Analytics
Infrastructure
Local analytics
Infrastructure
Data Products
Dev & Deploy
Issue tracking
Notification
Smart
Alerts
Requirements
§ Top management
commitment
§ Integration with the
business
§ Relations with IT
Challenges
§ Expectations and portfolio
management
§ Recruit and maintain talents
§ Resistance to change

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How a global manufacturing company built a data science capability from scratch

  • 1. How a global manufacturing company built a data science capability from scratch @carlotorniai Head of Data Science and Analytics Pirelli
  • 2. Outline § Why Data Science and Analytics in Pirelli § What did we do differently § Lessons learned
  • 3. Pirelli § The 5th world’s largest tyre manufacturer § Leader in the Premium and Prestige market § Only supplier of Formula 1 tyres § The Calendar
  • 4. Why Data Science and Analytics in Pirelli? § Capitalize on the amount of data available § Build services around data § Drive a cultural change
  • 5. Main clusters of activities Smart Manufacturing Integrated value Chain Demand forecasting Services built on top of Cyber Technologies
  • 6. What didn’t work before? § Tech-centered approach within IT § Old approach: client - supplier relationship § Core competence outsourced
  • 7. What did we do differently? § People - Org structure and team composition § Process - Agile to break silos § Technology - Right tools for the task
  • 8. People: outside the company grid § Start up § Outside ICT § Reporting directly to the CTO
  • 9. People: insource the right talents § Diversity of backgrounds § Small and flat organization § Be as much “independent” as you can across the full DS spectrum
  • 10. Process: agile to break silos § Transparency and trust § Break the contract game § Dealing with uncertainty § Cross team and cross hierarch interaction
  • 11. Process: how to stick around § Business driven § Have clear KPIs § Identify actionable items § Redefine the “idea” be data driven
  • 12. Technology: right tools for the task § It’s never about the tools (first) § Democratising data and enable smart data interaction at every level of the organization § Choose the right tools at the right time
  • 13. Tech stack and architecture evolution MES Local repo Hadoop Cluster ETL pipelines PirelliVPC AWS Factory
  • 14. Tech stack and architecture evolution PirelliVPC AWS Factory MES Local repo Hadoop Cluster ETL pipelines Analytics Infrastructure
  • 15. Tech stack and architecture evolution PirelliVPC AWS Factory MES Local repo Hadoop Cluster ETL pipelines Analytics Infrastructure Local analytics Infrastructure Data Products Dev & Deploy
  • 16. Tech stack and architecture evolution PirelliVPC AWS Factory MES Local repo Hadoop Cluster ETL pipelines Analytics Infrastructure Local analytics Infrastructure Data Products Dev & Deploy Issue tracking Notification Smart Alerts
  • 17. Tech stack and architecture evolution PirelliVPC AWS Factory MES Local repo Hadoop Cluster ETL pipelines Analytics Infrastructure Local analytics Infrastructure Data Products Dev & Deploy Issue tracking Notification Smart Alerts
  • 18. Requirements § Top management commitment § Integration with the business § Relations with IT
  • 19. Challenges § Expectations and portfolio management § Recruit and maintain talents § Resistance to change