Advanced Analytics Turin April, 2016

Index 2 ■ Advanced Analytics Approach –Architecture Overview –Methodology –Professional Skills ■ Impacted Areas / Goals

Advanced Analytics Approach 3 Business Intelligence Structured & Traditional Analysis Asks questions Structures data/reporting to answer business questions BusinessIT Advanced Analytics Approach (Big Data) Discovery & Predictive Analysis Delivers a platform to collect data and enable discovery activities Explores what questions could be asked ITIT & Business Answers the Questions What happened? When? Who? How many? Answers the Questions Why did it happen? Will it happen again? What will happen if we change a variable? What else does the data tell us that we never thought to ask? Advanced Analytics approach requests: Platform Skills Methodology

Analytic Layer NoSQL Layer Data Lake Internal Data SourcesExternal Data Sources Ingestion Platform PHD + HAWQ PHD + HAWQ PHD (Admin) PHD (Admin) DIA Big data Suite on DCA HAWQ 4

Methodology 5 Phase 1 Problem Formulation: Make sure to formulate a problem that is relevant to the goals and plain points of the stakeholders Phase 2 Data and Modeling Step: Identification of the appropriate analysis models based on data acquisition processes analysis and relevant data sources Phase 3 Presentation Results representation through dashboard and relative report production (positioning matrix and SWOT) Phase 4 Actions Tips and sharing of action / strategic intervention areas, priorities and quick-win. Identification and sharing of the evolutionary roadmap about touch points or support systems to address business requirements. Business Opportunity Prioritize more relevant use cases to cover the business needs Business Opportunity Prioritize more relevant use cases to cover the business needs Building a Narrative Create a fact-based narrative that clearly communicates insights to stakeholders Building a Narrative Create a fact-based narrative that clearly communicates insights to stakeholders Iterative Approach Perform each phase in an agile manner an iterate as required Iterative Approach Perform each phase in an agile manner an iterate as required Creativity Take the opportunity to innovate at every phase Creativity Take the opportunity to innovate at every phase

Professional Skills – Data Scientist 6 Expert Statisticians  Machine learning Domain Knowledge  Process Experience Programming  Parallelized algorithms  Database practitioners Data Scientist

Supply Chain Connected Car Impacted Areas / Goals 7  Goal: −Plant:  Proactive prevention A nticipate detection of emerging issues from the plant through correlations analysis between Quality manufacturing issues and Warranty claims −After Sales:  Early detection Improve capabilities and timing of issue detection leveraging all aftersales available data  Goal: −Plant:  Proactive prevention A nticipate detection of emerging issues from the plant through correlations analysis between Quality manufacturing issues and Warranty claims −After Sales:  Early detection Improve capabilities and timing of issue detection leveraging all aftersales available data Plant  Production processes  Quality gates  Order change analysis Plant  Production processes  Quality gates  Order change analysis Quality Customer  Technical service support  Warranty claim  Parts sales order  Connected Car Customer  Technical service support  Warranty claim  Parts sales order  Connected Car After Sales Customer  Eco:Drive  MyCar Customer  Eco:Drive  MyCar Connected Car Plant  Production Plan  Product Engineering  Supply Chain management Plant  Production Plan  Product Engineering  Supply Chain management Supply Chain Early Warning System Connected Vehicles Complexity & Forecasting Manufacturing How to: quality production process, recall campaign and technical support knowledge analysis

Supply Chain Impacted Areas / Goals 8 Plant  Production processes  Quality gates  Order change analysis Plant  Production processes  Quality gates  Order change analysis Quality Customer  Technical service support  Warranty claim  Parts sales order  Connected Car Customer  Technical service support  Warranty claim  Parts sales order  Connected Car After Sales Customer  Eco:Drive  MyCar Customer  Eco:Drive  MyCar Connected Car Plant  Production Plan  Product Engineering  Supply Chain management Plant  Production Plan  Product Engineering  Supply Chain management Supply Chain Early Warning System Manufacturing  Goal: −Driver segmentation  Improve target campaign management based on real usage of the car and better knowledge of our customer −Vehicle pedigree:  Propose maintenance services −Alarm warning detection:  Improve loyalty of customer through the action based on risk score definition of failure  Goal: −Driver segmentation  Improve target campaign management based on real usage of the car and better knowledge of our customer −Vehicle pedigree:  Propose maintenance services −Alarm warning detection:  Improve loyalty of customer through the action based on risk score definition of failure Connected Vehicles Connected Car Complexity & Forecasting How to: car usage, alarm cockpit and drive style analysis

Impacted Areas / Goals 9 Plant  Production processes  Quality gates  Order change analysis Plant  Production processes  Quality gates  Order change analysis Quality Customer  Technical service support  Warranty claim  Parts sales order  Connected Car Customer  Technical service support  Warranty claim  Parts sales order  Connected Car After Sales Customer  Corporate Website  Lead Generation Customer  Corporate Website  Lead Generation Marketing Plant  Production Plan  Product Engineering  Supply Chain management Plant  Production Plan  Product Engineering  Supply Chain management Supply Chain Early Warning System Manufacturing Connected Vehicles Connected Car  Goal: −Product Complexity:  Complexity reduction in order to enhance production and supply chain processes  Support Product Manager in Product grid definition and updates  Guide customer within decisional process while configuring the product suggesting possible/best optional of interest −Order Forecasting:  Automatic creation of Production MIX forecast with high level of accuracy  Reduction of real orders requirements due to planning reworks  Reduction of real material costs due to urgent transportation and scrapping costs of build out processes  Goal: −Product Complexity:  Complexity reduction in order to enhance production and supply chain processes  Support Product Manager in Product grid definition and updates  Guide customer within decisional process while configuring the product suggesting possible/best optional of interest −Order Forecasting:  Automatic creation of Production MIX forecast with high level of accuracy  Reduction of real orders requirements due to planning reworks  Reduction of real material costs due to urgent transportation and scrapping costs of build out processes Complexity & Forecasting Supply Chain How to: best sellers configurations, sell seasonality and processes analysis

Backup

Dynamic and automatic plan and actions adjustments based on future events Approaches 11 HindsightInsightForesight Complexity Value of Analytics ($) Descriptive Analytics Predictive Analytics Prescriptive Analytics What happened? Why did it happen? What will happen? BI Data Science How can we make it happen? Business Intelligence Data Science Keys Data Science Business Intelligence What is What is Advanced Analytics? What is What is Advanced Analytics? units of 500 X sold in the last month Units sold due to specific marketing campaign proposed to dealers Proposed campaign to increase units sold by 2% Diagnostic Analytics

Main Benefits 12  Analysis timing reduction and manipulations evolution  New Data Availability time-lag reduction (no preliminary transformation to load data)  Flexible and seamless access and manipulation of structured/ unstructured data  High reduction of processing time of mining operations and application of statistical functions  Improvement statistical performance of the models (i.e. the statistical iterations can be multiplied for better performance)