PPT Template_169_MM_CORPORATE_2015 - UniMI

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

2 .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

3 .3

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

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

6 .6 MAGNETI MARELLI

7 .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 ) ELECTRONICS ( Instrument Clusters, Infotainment & Telematics ) SUSPENSION SYSTEMS AND SHOCK ABSORBERS ( Suspension Systems , Shock Absorbers and Dynamic Systems) 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) EXHAUST SYSTEMS (Manifolds, Catalytic converter, Diesel Particulate Filter and Mufflers ) POWERTRAIN (Gasoline and Diesel engine control, Electric Motor, Inverter and Transmission) MOTORSPORT ( Injection Systems, Electronic Control Units, Hybrid Systems, Telemetry Systems, Electric Actuators)

8 .8 Magneti Marelli Worldwide Presence 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 PP - AC PP - AC PP - AC USA PP - R&D – AC MEXICO BRASIL ARGENTINA GERMANY POLAND CZECH REP . SLOVAKIA RUSSIA SERBIA TURKEY PP - R&D – AC CHINA JAPAN KOREA MALAYSIA INDIA ITALY SPAIN PP - AC PP – R&D - AC PP PP - AC PP – R&D – AC PP – R&D - AC PP - AC PP - R&D – AC PP - R&D – AC PP PP - AC AC PP 2015 ACT PP: Production Plant R&D: R&D Center AC: Application Center UK FRANCE 2016 ACT

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

10 .10 WHY BIG DATA

11 .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 .12 Complexity behind a “simple” product Raw materials WIP Factory Pre Production Lines Assembly Lines WIP Warehouse Material Warehouse Internal Logistic External Logistic External Logistic Internal Logistic Finite Product Warehouse External Logistic Customer

13 .13 Industry 4.0

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

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

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

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

18 .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 .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 .20 Industry 4.0 – Predictive Quality Data Extraction Feature Engineering Classification Show Results Precision : very high Recall@scrap : zero

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

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

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

24 .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 .25 FROM PRODUCT TO SERVICES PARADIGM

26 .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 .27 WHO WORKS WITH DATA

28 .28 Data team members Data Engineer Data Scientist Data Architect

29 .29 Data Scientist Data Scientist Definition : “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 Main everyday tasks: Must know: Python, Sql , Supervised/Unsupervised models, linear algebra, statistic 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