Siemens 2020 Our strategy and innovation focus Lecture at Koç University Unrestricted Siemens AG All rights reserved.

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2 Contents Siemens strategy and key drivers of our innovation agenda Electrification Automation Digitalization Innovation and Business Excellence Page 2 September 2014 Corporate Technology Unrestricted Siemens AG All rights reserved.

3 Siemens Vision 2020: Strategic focus 1 Long-term growth agenda in attractive fields 2 Align portfolio along strategic imperatives 3 Cost reduction and business excellence Ownership culture and leadership based on common values Page 3 September 2014 Corporate Technology Unrestricted Siemens AG All rights reserved.

4 Electrification, Automation and Digitalization are the key drivers of our innovation agenda Megatrends, innovation drivers, Siemens value chain Digital transformation Digitalization Globalization Urbanization Demographic change Automation Power generation Power transmission and distribution, smart grid Automation, efficient use of energy Buildings Mobility Production Medical imaging, in-vitro diagnostics Climate change Electrification Page 4 September 2014 Corporate Technology Unrestricted Siemens AG All rights reserved.

5 Stringent resource allocation for growth fields in Electrification, Automation and Digitalization Selected growth fields Digitalization Business Analytics & Data-driven Services Software & IT solutions 8% Automation Flexible & Small GT Distribution Grid Automation & Software Road & City Mobility, tolling 'Digital twin' Software Key Verticals e.g. O&G, F&B Image guided Therapy, Molecular Diagnostics, etc. 7% Electrification 8% Offshore 7% 6% 6% 8-10% 18% x% Est. market growth (CAGR) Power to last Drive energy management Efficient urban mobility Shape Industry 4.0 Grow in Process Industries Next Generation Healthcare Page 5 September 2014 Corporate Technology Unrestricted Siemens AG All rights reserved.

6 Digitalization: Sensors everywhere, everything connected, everything analyzed Selected technology-driven trends with impact for Siemens Pervasive Sensing Digital twin Internet of things Computer-aided collaboration Data / computing in the cloud Autonomous systems Ubiquitous mobile computing Electricity everywhere Smart data / everything analyzed Industry Buildings Transportation Healthcare Power generation, transmission, distribution Sustainability / energy efficiency Page 6 September 2014 Corporate Technology Unrestricted Siemens AG All rights reserved.

7 IT for Industry and Infrastructure is different from Internet IT Industrial / Infrastructure IT vs. Internet IT IT for Industry and Infrastructure: Human-triggered decision Internet IT: Machine-supported decision Mgmt. Planning SCADA Control Field Machine-generated data Analysis/decision at lowest possible level Seamless integration of processes Lean data volumes ( smart data ) Industry-grade quality and resilience Domain as key differentiator Typical focus Analytics Intelligence Data Integration Cloud Services Consumer Devices & Apps Human-generated data Analysis/decision based on big data pool Integration and combination of data Bandwidth and large-scale data storage Good-enough quality Network effect as key differentiator Page 7 September 2014 Corporate Technology Unrestricted Siemens AG All rights reserved.

9 Energy systems worldwide are facing significant challenges Challenges in power distribution Outdated infrastructure Growing load imbalances 1) Emergence of microgrids 2) Rise of the prosumer Non-technical losses 20% 9% 5% 10% 3% Capacity problems Large-scale renewable power Electrification of mobility 1) Chart: Weakly load on a rural transformer station in 2003 (red) and today 2) Picture: Microturbine within microgrid of Genova University Page 9 September 2014 Corporate Technology Unrestricted Siemens AG All rights reserved.

10 Modern-day challenges in power transmission and distribution require specific smart grid solutions IV Software solutions for end-to-end management Storage Solutions Diesel Smart Street Generator Lighting Pumped Storage Power Plant Large Scale PV Plant Wind Parks Private Solar Electrical Vehicles Microgrids & III Smart Rail grids I Rail Electrification & Smart Transmission II Smart Distribution Balancing Peak avoidance Resiliency CO 2 & cost avoidance Theft avoidance Decentral optimization Page 10 September 2014 Corporate Technology Unrestricted Siemens AG All rights reserved.

12 Challenges: Manufacturing is changing faster than ever before Automation is constantly advancing Increasing competitiveness 1 Increase efficiency Shorten 2 3 Enhance flexibility time-to-market Energy and resource efficiency are decisive competitive factors Shorter innovation cycles More complex products Larger data volumes Individualized mass production Volatile markets High productivity Industry 4.0 should help to solve manufacturing challenges Page 12 September 2014 Corporate Technology Unrestricted Siemens AG All rights reserved.

13 Industry 4.0: Automation plays a crucial role in making the future of manufacturing become reality 1 Production network Realization of flexible value chains based on powerful MOM: Manufacturing Operations Management 2 Fusion of virtual and real world Integration of product design and production engineering based on a common Digital Enterprise Platform 3 Cyber-Physical Systems Migration towards a plug 'n' produce integration of automization Page 13 September 2014 Corporate Technology Unrestricted Siemens AG All rights reserved.

15 Smart data to business principle Combination of domain, device and analytics From raw data to smart data to business Power plants Power grids Factories Hospitals Data from products, systems, sensors, processes Data Data analytics ''Smart data to business'' Business Intelligence Business Innovation Value Generation Customer benefit Performance increase Cost reduction Energy saving Risk avoidance/ security/ safety New business models Domain Device Analytics Smart Data Page 15 September 2014 Corporate Technology Unrestricted Siemens AG All rights reserved.

16 Wind park optimization: 1% increase of annual energy with optimized control policy Smart Data to Business Example: Optimization of wind parks (Project ALICE, CeBIT) s t A Q C tanh Target 1 = r t - B a t s t A D Q C tanh tanh E B a Results 1% increase of annual energy with optimized control policy Wind power Market drivers Customer needs Aerodynamics Meteorology Wind turbines ~12,000 installed Mechanical Engineering Sensor properties Controller design Autonomous Learning Neural Networks Input space contains 3,000 features Robust policy generation despite noisy data Domain Device Analytics Smart Data Page 16 September 2014 Corporate Technology Unrestricted Siemens AG All rights reserved.

17 In turbine operation, smart data allows for reduced NOx emissions and extended service intervals Smart Data to Business Example: Optimization of gas turbine operation Results Reduced NOx Emissions Extension of service intervals Energy system Market drivers Customer needs Product cycles Gas turbines Mechanical Engineering Thermodynamics Combustion chemistry Sensor properties Autonomous Learning Neural Networks Smart Data Architecture to process data from 5,000 sensors per second Domain Device Analytics Smart Data Page 17 September 2014 Corporate Technology Unrestricted Siemens AG All rights reserved.

18 Aspern Smart City Research Vienna Smart Data Analytics in a Greenfield Environment City infrastructure Market drivers Customer needs Power networks Building technology Smart Grid/Smart building Electrical engineering Power storage Smart meters Smart City Cockpit Integration of smart grid, smart buildings, water and mobility Interactive visual analytics dashboard Objective ''My clear goal now is to become the greenest city in the world. Therefore we will build 'Aspern Vienna's Urban Lakeside', one of the biggest city development projects in Europe, Michael Häupl, Mayor of Vienna Domain Device Analytics Smart Data Page 18 September 2014 Corporate Technology Unrestricted Siemens AG All rights reserved.

20 Technology leadership and new business models market leadership profit Interrelations of technology leadership, new business models, market leadership and profit Technology leadership Profit Market leadership Margin and margin band Energy Healthcare Industry Infrastructure and Cities drives scale effects and New business models Siemens margin Margin band Siemens Page 20 September 2014 Corporate Technology Unrestricted Siemens AG All rights reserved.

21 Business Excellence is very high on Siemens management agenda Mandatory elements of top + Targets for BUs Implementation of Business Excellence in BU (according to top + framework) Targets for all BUs Strategy Strategic Planning Strategic Initiatives Implementation Benchmarking Transparency Benchmarking 2 4 years Improvement program Competitiveness Growth / Innovation Measures are based on V-Concept Technology/portfolio Competitive position Regional focus Sales channels HR/competencies Regular assessment of competitive positioning via Benchmarks (business, innovation and process) Continuous follow-up via recurring cycle of Benchmarking and Improvement Program addressing innovation, efficiency and growth levers Develop roadmap to secure or achieve a leading technology position Continuous improvement in all processes, using lean approaches (e.g., SPS) Systematic competence development for Business Excellence via training of employees on all levels from blue collar to management Page 21 September 2014 Corporate Technology Unrestricted Siemens AG All rights reserved.

22 Innovation and Business Excellence 2 crucial elements of Siemens operating system Financial target system One Siemens Operating system Corporate Memory Customer proximity Innovation Business excellence People excellence Lead country concept NPS Board / Survey Leverage Key account management Market Development Boards Architecture for vertical IT and horizontal platforms Open innovation / R&D in partner networks Development of new, techno-based growth businesses Benchmarking of technology positioning and innovation power Technology leadership along the entire value chain for business growth top + framework Benchmarking, Productivity, Improvement programs Quality management Optimization of functions / cross-functions SW, R&D, Engineering Manufacturing Lean Project risk management / PM@Siemens Service Employee share programs Diversity Office Siemens Global Engagement Survey / followup Performance Management Process (PMP) Learning and development (SLE, LC) Sustainability and Citizenship Page 22 September 2014 Corporate Technology Unrestricted Siemens AG All rights reserved.