how a global manufacturing company built a data science capability from scratch
Post on 21-Mar-2017
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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
HadoopCluster
ETLpipelines
Pirelli VPC AWS Factory
Tech stack and architecture evolutionPirelli VPC AWS Factory
MES Local repo
HadoopCluster
ETLpipelines
AnalyticsInfrastructure
Tech stack and architecture evolutionPirelli VPC AWS Factory
MES Local repo
HadoopCluster
ETLpipelines
AnalyticsInfrastructure
Local analytics Infrastructure
Data ProductsDev & Deploy
Tech stack and architecture evolutionPirelli VPC AWS Factory
MES Local repo
HadoopCluster
ETLpipelines
AnalyticsInfrastructure
Local analytics Infrastructure
Data ProductsDev & Deploy
Issue trackingNotification
SmartAlerts
Tech stack and architecture evolutionPirelli VPC AWS Factory
MES Local repo
HadoopCluster
ETLpipelines
AnalyticsInfrastructure
Local analytics Infrastructure
Data ProductsDev & Deploy
Issue trackingNotification
SmartAlerts
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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