MSC- Who We Are and What We Do

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2 MSC- Who We Are and What We Do Transformation, Technology and Analytics Supply Chain Mgmt./ New Product Introduction >77M Units Manufactured & Shipped >42,000 Number of Active SKU s Care Strategic Sourcing ~$8B Total Amount of Annual Spend >390 Number of Suppliers (includes component suppliers) Global Supply Chain Safety, Compliance & Sustainability Manufacturing Integrated Business Planning 1TB Total Daily SC Data Generation 1.7M+ Est. FY17 Number of Retail and Commercial Sales Orders 107 Countries Served 33 Number of Mfg & Distribution Operations 2.0M+ Est. FY17 SAP Deliveries 30K Ship To Locations

3 Our Products Xbox Surface Studio Surface Hub HoloLens Surface PC Hardware Software

4 MSC Global Presence Netherlands Finland Russia Canada UK France Germany Austria Czech Republic Japan Mexico Colombia UAE India Taiwan Hong Kong SAR Singapore Brazil Malaysia Australia Argentina

5 Microsoft Supply Chain Challenges Pricing and Margin Pressure Increasing Customer Service Expectations 1 Digitized Supply Chain

6 MSC s 1DSC Transformation Journey

7 The Azure Platform Enables our Connected Digital Supply Chain Foundation Supply Chain Partner Systems Supplier ERP BOM Management Shop Floor Control System Warehouse Management Transportation Management Point of Sales Freight Management Enterprise Services Corporate systems Supply Chain Services Data & Advanced Analytics Platform Device Telemetry Commerce CRM Azure API Management Microservices Azure Application Insights Azure Data Factory Azure Data Lake HD Insight Event Hubs Product Catalog Sales Subscriptions Supply Chain Cloud Microsoft Supply Chain Core Transactional Systems Dev/NPI Plan Source Make (MDS) Deliver Care Finance

8 Connected: MSC Purpose is reflected in what we measure via Power BI Aggressively Manage Costs Effectively Deliver Products & Services to Market Enable Scale and Complexity Drive world class Customer Care, Product Quality & Compliance

9 Factory Video Microsoft Manufacturing Digital Transformation: Leading the Industry with Microsoft Tools/Technologies

10

11 Predictive Factory s with MDS, a Azure-based system for real-time factory floor & product lifecycle analytics Before: Static, siloed data (TCS, Shop Floor ) After: Enables proactive alerting, predictive analytics Other T1 T2 Critical T2 Share Point T1 T2 SQL JDM1 Shop Floor EIP JDM1 Shop Floor Returns MTE TCS Returns JDM1 TCS TCS Any T1 or Critical T2 Scale Capture - 1B data points/day, Analyzed < 1% Speed Accessibility to data measured in hours/days Reduced Returns, Higher Yields, Better Productivity

12 System enables Power BI, machine learning, advanced analytics & decision making capability E2E Visibility from Incoming to Customer Proactive Alerting, Real time Insights Incoming Manufacturing Customer Incoming Part Quality & Availability Product Yield Outgoing Quality Returns Repair & Refurb T2 Quality CTF/CTQ IoT Process Monitoring Output & Shipment Progress Field Quality Big Data Mining Proactive Alerting Real time Insights Predictive Analytics Creating Clarity via Personalized Dashboards Increasing Collaboration, Connected Data Streams enable Teamwork Improving Factory Productivity/Optimization Big Data insight and Machine Learning Proactive Alerting Predictive Analytics

13 Implementation is changing the way we (Microsoft & partners) manage the day to day factory operations Example: Factory Manager Product Dashboard Example: Proactive Alerting Personalized dashboards align to Factory Manager s purpose Proactive alerting monitoring critical IoT enabled measurement data Surface Pro 4 Alerts identify one tester drifting out of control. Tester taken off line, and issue corrected before yield impacted Alerts identify significantly different battery placement measurements on one line vs others Fixture alignment issue corrected before quality impact

14 As a result of the foundation, we are now starting to use predictive analytics & machine learning in the factory Example: Predictive Yield Improvement Example: Predictive Return Insights Factory Data Assembly component, Genealogy, MES, Environmental Product Big Data Product Design & Mfg Mfg/Test process Components/supplier Hardware/device design Product Experience SW release/updates App/accy Incompatibility Windows telemetry Azure ML Analytics engine evaluated multiple attribute variables to predict top patterns across Wafers, Chips Azure ML Training Machine Learning models to identify drivers/patterns of SP4 returns attributed to critical HW genealogy, software telemetry & CSS data Predictive insights Model predicted optimal patterns and optimized manufacturing parameters for improved yield Current insights Current model predicts ~80% recall rate for a returned device. Improving precision with additional Factory and Customer/Retailer data Results Targeted patterns/combinations demonstrated ~30% yield improvements and $2M in scrap reduction Expected Results Targeting to achieve: Improved CSS/Customer interaction 1st Returns reductions, better quality Improved returns forecasting

15 Digital Transformation increases Pace Data is the new Currency

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