1. The role of the data scientist
Why a car part manufactory company  needs data experts
ICT Innovation
Andrea Condorelli, Manager Data Scientist
Statale di Milano, Italy
5° of June, 2017

2. MAGNETI MARELLI Quick overview on MM with a special focus on most innovative products
WHY BIG DATA An overview on some hot topics/opportunities/ongoing projects in manufactory regarding Big Data
FROM PRODUCT TO SERVICES PARADIGM Some companies where the business model moved from selling pieces of hardware to selling services
WHO WORKS WITH DATA Data Scientist vs Data Engineer: identikit of the perfect data expert
ENTERPRISE DATA SCIENTIST TOOLBOX Technologies and frameworks, security and privacy in companies
OPPORTUNITIES IN MAGNETI MARELLI
Q&A

4. HR Leadership Development Team
HR ORGANIZATION
Giovanni Quaglia
CHRO
Stefano Facchetti
Head of Leadership Development and Process & Systems
Donatella Callerio
Staffing and Recruitment Manager / Leadership Development Finance & ICT
Marta Ragazzi
Staffing and Recruitment /
Leadership Development ICT

5. Data Science team ICT GOVERNANCE ICT INNOVATION Dario Castello
CIO
ICT INNOVATION
Luca Demarchi
Head of ICT Innovation
Condorelli Andrea
Data Science Manager
Valentina Arrigoni
Alberto Catena
Data Science team

6. MAGNETI MARELLI

7. Company Overview
Magneti Marelli is an international company committed to the design and production of hi-tech systems and components for the automotive sector.
AUTOMOTIVE LIGHTING
(Headlamp, Rearlamp, Lighting and Body Electronics)
POWERTRAIN
(Gasoline and Diesel engine control, Electric Motor, Inverter and Transmission)
ELECTRONICS
(Instrument Clusters, Infotainment & Telematics)
SUSPENSION SYSTEMS AND SHOCK ABSORBERS
(Suspension Systems, Shock Absorbers and Dynamic Systems)
EXHAUST SYSTEMS
(Manifolds, Catalytic converter, Diesel Particulate Filter and Mufflers)
PLASTIC COMPONENTS AND MODULES
(Bumper, Dashboard, Central Console, Pedals, Hand Brake Levers and Fuel System)
AFTERMARKET PARTS & SERVICES
(Mechanical, Body Work, Electrics and Electronic and Consumables)
MOTORSPORT
(Injection Systems, Electronic Control Units, Hybrid Systems, Telemetry Systems, Electric Actuators)

8. PP: Production Plant R&D: R&D Center AC: Application Center
Magneti Marelli Worldwide Presence
PP: Production Plant R&D: R&D Center AC: Application Center
FRANCE
PP - R&D – AC
GERMANY
PP - R&D – AC UK
POLAND
PP - AC
CZECH REP.
PP - AC
USA PP - R&D – AC
SLOVAKIA PP
MEXICO
PP - AC
RUSSIA
PP - AC
SERBIA PP
BRASIL
PP – R&D - AC
CHINA
PP - R&D – AC
ARGENTINA PP
JAPAN AC
MALAYSIA
PP - AC
ITALY
PP – R&D – AC
INDIA
PP – R&D - AC
KOREA
SPAIN
PP - AC
TURKEY
PP - AC 12
R&D Centers
5.9%
R&D (of sales)
7.9 bn €
Sales 2016 86
Production units
5.8%
Investments (of sales) 30
Application Centers
42,830
Employees
Sales (€ bn)
2009 ACT
2010 ACT
2011 ACT
2012 ACT
2013 ACT
2014 ACT
2015 ACT
2016 ACT

9. Organization COUNTRY/REGION REPRESENTATIVES GLOBAL KEY ACCOUNT
LATAM
NAFTA
COUNTRY/REGION
REPRESENTATIVES
INDIA
INFORMATION &
COMMUNICATION
TECHNOLOGY
CHINA
HUMAN
RESOURCES
MANUFACTURING
JAPAN
TECHNOLOGY
INNOVATION
QUALITY
MARKETING
COMMUNICATION
PROJECT
MANAGEMENT
OFFICE
RISK
GOVERNANCE
GLOBAL KEY
ACCOUNT
PURCHASING
GENERAL
AFFAIRS
BUSINESS
DEVELOPMENT
FINANCE
BUSINESS AREAS
MOTORSPORT
CENTRAL FUNCTIONS
AUTOMOTIVE
LIGHTING
SHOCK
ABSORBERS
AFTER MARKET
PARTS &
SERVICES
ELECTRONICS
EXHAUST
SYSTEMS
PLASTIC
COMPONENTS
& MODULES
POWERTRAIN
SUSPENSION
SYSTEMS

10. WHY BIG DATA

11. How to turn data in money
Piece cost reduction:
decrease number of scraps
lower stocks
enhance productivity
Making new business:
sell new services

12. Finite Product Warehouse
Complexity behind a “simple” product
Factory
Material Warehouse
Pre Production
Lines
Raw materials
External Logistic
Internal Logistic
Customer
WIP Warehouse
External Logistic
Assembly Lines
External Logistic
WIP
Finite Product Warehouse
Internal Logistic

13. Industry 4.0

14. Finite Product Warehouse
Industry 4.0 – Traceability and IOT
Factory
External Logistic
Material Warehouse
Pre Production
Lines
Step 1
Step 2
Step 3
Raw material
Material ID
Step 1 data
Machine 1 …
Leave TS
Item ID
Arrive TS
Lot ID
Arrive TS
Supplier
Material info
Item ID ts
Step 1…
Internal Logistic
WIP Warehouse
Assembly Lines
Finite Product Warehouse
Internal Logistic
Step 4
Step 3
Step 2
Step 1

15. Industry 4.0 – Traceability and IOT
Enhancing recall campaigns
Deeply understanding of each process
Compute the real cost of each piece

16. Industry 4.0 – Predictive Quality
Step 1
Worker
Machine Parameters
Machine Sensors
Step 2
Worker
Machine Parameters
Machine Sensors
Step 3
Worker
Machine Parameters
Machine Sensors
Material Warehouse
WIP Warehouse
SCRAP
SCRAP
SCRAP
SCRAP
SCRAP
SCRAP

17. ? ? Industry 4.0 – Predictive Quality SCRAP Step 1 Step 2 Step 3
Worker
Machine Parameters
Machine Sensors
Step 2
Worker
Machine Parameters
Machine Sensors
Step 3
Worker
Machine Parameters
Machine Sensors ?
Material Warehouse
SCRAP ?

18. Industry 4.0 – Predictive Quality
Classification/Prediction problem
“Given a context, predict the probability the specific item will arrive to the following station/it will be discarded”
A scrap could be done due to several reasons:
Human error
Some HW/SW machine failure
Material problem
Wrong process/issues on line design

19. Industry 4.0 – Predictive Quality
The context is pretty hard to describe (feature engineering):
Each piece worked before each scrap has a very similar context
Tasks are complex and different
People are involved, it is hard to quantify:
fatigue
experience in a given task
mood
stress
A lot of machines are sensorless
The data change over time
The problem is not linear and has “memory”

20. Industry 4.0 – Predictive Quality
Data Extraction
Feature Engineering
Classification
Show Results
Precision: very high
zero

21. Descriptive Statistics
Industry 4.0 – Predictive Quality
Data Extraction X
Feature Engineering
Classification
Show Results
Precision: Medium
Low
Descriptive Statistics
Visual Exploration
Visual Exploration
Cleaning Data

22. Descriptive Statistics
Industry 4.0 – Predictive Quality
Data Extraction
Feature Engineering
Classification
Show Results
Precision: >Medium
>Low
Lesson learned:
some shift must be filtered out
we must add additional pieces of information
Descriptive Statistics
Visual Exploration
Visual Exploration
Cleaning Data

23. Industry 4.0 – Predictive Quality
Data Extraction
Hard Cleaning
CLEAN
DATA
Feature Engineering
Classification
Show Results
Precision: High
High

24. Industry 4.0 – Predictive Quality
Reducing scraps working on “critical” context
Simulating different context to “explore” new configurations (e.g., one arm bandit on team configurations)
Reducing the cost of each scrap

25. FROM PRODUCT TO SERVICES PARADIGM

26. Rolls Royce
1904
F H Royce is founded in 1904 by Charles Stewart Rolls and Frederick Henry Royce
1915
The Rolls-Royce Eagle was the first aero engine to be developed by Rolls-Royce Limited.
1987
In April 1987 the government offered for sale all Rolls-Royce plc shares.
1996
Birth of TotalCare® as a service for America Airlines for motor repairing
2013
47% of total revenue (7.3B£) on plane engines are from services
2016
80% of Rolls-Royce engines are not sold, but rented out on a hourly basis.

27. WHO WORKS WITH DATA

28. Data team members
Data Scientist
Data Engineer
Data Architect

29. Data Scientist Data Scientist Definition: Must know:
“Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician.” Josh Wills, Slack Director of Data Engineering
Must know:
Python, Sql, Supervised/Unsupervised models, linear algebra, statistic
Main everyday tasks:
Formalizing any given problem into specific research questions and looking for State of the Art solutions for them
Designing and developing Proof of Concepts and Prototypes to show the real value behind data and algorithms
Translating Proof of Concepts into something Business people can understand and creating stunning presentation

30. Data Engineer Data Engineer Definition: Must know:
A Data Engineer is a Data Scientist who prefers talking about infrastructures and design patterns over Bayesian statistics and XGBoost classifier
Must know:
Java/Scala, Python, Sql and noSQL DBs, design patterns
Main everyday tasks:
Moving Proof of Concepts from data scientist playground to production
Designing, constructing, installing, testing and maintaining highly scalable data management systems
Employing a variety of languages and tools (e.g. scripting languages) to marry systems together

31. Data Architect Data Architect Definition: Must know:
A Data Architect is a aged Data Scientist/Engineer with a lot of experience in enterprise infrastructures.
Must know:
Hadoop, SQL and noSQL DBs, design patterns, enterprise infrastructures, security, lambda/gamma architecture, Docker
Main everyday tasks:
Building data products from scraps
Design and deploy complex data processing workflows
Deploy Big Data ready environments to support Data Engineer and Data Scientist work

32. Ideal world Real world The team Data Superman 1*
1 Data Architect/Data team manager
2-3 Data engineer to support the development and go-lives
3-5 Data Scientist for fast prototyping and complex models/analysis 1*
2-3*
3-5*

33. Data Superman
Learn how to use the whole data toolbox He must be able to face the majority of IT challenges by yourself, from bash to Dockerfile

34. Data Superman
Learn how to use the whole data toolbox He must be able to face the majority of IT challenges by yourself, from bash to Dockerfile
Learn how to explain your work A good story with average results is better than a boring story with good results (stunning results always win)

35. Data Superman
Learn how to use the whole data toolbox He must be able to face the majority of IT challenges by yourself, from bash to Dockerfile
Learn how to explain your work A good story with average results is better than a boring story with good results (stunning results always win)
Learn how to write “good code” Working like the person you are going to show your code is a psychotic killer.

36. Data Superman
Learn how to use the whole data toolbox He must be able to face the majority of IT challenges by yourself, from bash to Dockerfile
Learn how to explain your work A good story with average results is better than a boring story with good results (stunning results always win)
Learn how to write “good code” Working like the person you are going to show your code is a psychotic killer.
Learn how to understand business needs Well posed questions are very rare

37. Data Superman
Learn how to use the whole data toolbox He must be able to face the majority of IT challenges by yourself, from bash to Dockerfile
Learn how to explain your work A good story with average results is better than a boring story with good results (stunning results always win)
Learn how to write “good code” Working like the person you are going to show your code is a psychotic killer.
Learn how to understand business needs Well posed questions are very rare
Learn how to make models work Data Superman needs to be comfortable with mathematics and statistics

38. Data Superman
Learn how to use the whole data toolbox He must be able to face the majority of IT challenges by yourself, from bash to Dockerfile
Learn how to explain your work A good story with average results is better than a boring story with good results (stunning results always win)
Learn how to write “good code” Working like the person you are going to show your code one day is a psychotic killer.
Learn how to understand business needs Well posed questions are very rare
Learn how to make models work Data Superman needs to be comfortable with mathematics and statistics

39. ENTERPRISE DATA SCIENTIST TOOLBOX

40. 2-3 tools…

41. A short list basket
Programming Language

42. A short list basket – Programming Language
Python R
High-Level
Lower-Level
C++
Scala
Java

43. A short list basket – Programming Language
Python
Why Python:
Fast prototyping language
Easy to install and to manage
Lambda functions
Tensorflow + sklearn

44. A short list basket
Programming Language Python
Big Data framework

45. A short list basket – Big Data Framework
Apache Flink
Apache Spark
MapReduce
Apache Storm

46. A short list basket – Big Data Framework
Why Spark:
In-memory computation
Great community
Stable and easy to manage
ML-LIB is great for fast prototyping
Offered “as-a-service” by different players (Amazon EMR, Google Dataproc, Cloudera, DataBricks, …)
Apache Spark

47. A short list basket Programming Language Python
Big Data framework Apache Spark DB

48. ElasticSearch + Kibana
A short list basket – DB
MySQL
MariaDB
ElasticSearch + Kibana
Cassandra
MongoDB

49. ElasticSearch + Kibana
A short list basket – DB
ElasticSearch + Kibana
Why ElasticSearch+Kibana:
Big Data ready
Fast full text search (based on Lucene)
Ultra-fast dashboarding (Kibana)
MongoDB
Why MongoDB:
Big Data ready
Document based queries
Bson + array

50. A short list basket Programming Language Python
Big Data framework Apache Spark
DB ElasticSearch+Kibana or MongoDB
Other

51. A short list basket – Other
Plotly
SkLearn
Tensorflow
Jupyter

52. A short list basket Programming Language Python
Big Data framework Apache Spark
DB ElasticSearch+Kibana or MongoDB
Other
Python libraries: Plotly, SkLearn, Tensorflow
“IDE”: Jupyter

53. A data product Spark Python HTML+JS Backend Frontend GUI
ML Algorithm
ETL
Plotly
Django
HTML5
Bootstrap
MongoDB
Backend
Frontend
GUI

54. A short list basket Programming Language Python
Big Data framework Apache Spark
DB ElasticSearch+Kibana or MongoDB
Other
Python libraries: Plotly, SkLearn, Tensorflow
“IDE”: Jupyter
Container engine Docker

55. OPPORTUNITIES IN MAGNETI MARELLI

56. Opportunities in MAGNETI MARELLI
Marta Ragazzi:
Andrea Condorelli:
Big Data Workshop
October 31st, 2015