Siemens Q2 2023 Analyst Call Presentation

Outstanding performance continues –

Outlook raised again

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Roland Busch, CEO Siemens AG

Ralf P. Thomas, CFO Siemens AG

Unrestricted | © Siemens 2023 | Investor Relations | Q2 Analyst Call | 2023-05-17

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THE 2022 DIGITAL TWIN REPORT

EXAMINING DIGITAL TRANSFORMATION MATURITY FOR PRODUCT DESIGN AND PRODUCTION ENGINEERING

The 2022 Digital Twin Report

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Lifecycle Insights

Siemens

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Siemens Q1 2022 Analyst Call Presentation

Siemens Smart Infrastructure Update

西门子2022财年Q3业绩报告

西门子第三季度销售额178.7亿欧元，同比增加11%，预估173.8亿欧元。净亏损16.6亿欧元，上年同期收益13.5亿欧元，预估亏损6.336亿欧元。自由现金流22.9亿欧元，同比增加0.6%。订单220.1亿欧元，同比增加7.4%。

Consumers today want increasingly smart, efficient and customized products that can be delivered in a timely manner. We are seeing a new product generation emerge across industries, powered by a spectacular innovation wave, including ever-increasing mechatronics, new lightweight materials and digital tech-nologies like the cloud and the Internet of Things (IoT). This additional complex-ity is no longer manageable in a traditional, verification-centric develop-ment process, so manufacturers are deploying new approaches that enable them to predict product behavior on an individual level over the entire lifecycle, including all multi-physics. Test depart-ments are feeling the effects of this evolution in their work, both in volume and technical content. More than ever, they need innovative testing solutions that help them achieve maximum productivity.

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Today’s simulation methodologies often lack the realism to deliver prediction-capable models. Yet those models are essential for complex product develop-ment or for use in embedded software to enable smart behavior. Test departments play a crucial role in filling the current gaps. This goes way beyond measuring accurate data for standard structural correlation and model updating. Testing allows companies to explore uncharted design territories and build knowledge about new materials and all the addi-tional parameters that come with mechatronic components. This requires specialized, high-precision tools. And it adds an enormous amount of validation work for test departments on top of their standard tasks, which already are per-formed under growing time pressure. These include prototype validation and certification.

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It has never been more critical to optimize design in a system’s early development stages when it is still conceptual. There is immense pressure to reach the ever-increas-ing performance levels in the context of increasingly complex, interconnected and smart products. Any defect identified early in the process will be easier to solve and have little impact on the project timeline and cost. That cost will be negligible compared to product recalls and the negative impact on brand image if the issue is discovered later in the design cycle.

Smart products, tailored to personal needs, preferences and habits – that’s what consumers want today. As a manufacturer, you must fulfill this request, and design products with increasing complexity. These products include mechanical and electronic components, software and controls.

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The industry mandate for energy efficiency also persists. Environmental concerns such as the scarcity of fossil fuels and global warming continue to raise the importance of fuel efficiency. New lightweight materials and manufacturing methods are required to address this challenge, as are intelligent systems that optimize energy consumption.

Smart products also evolve over time. They include many sensors that optimize product performance after delivery, communicate with each other and their environment, and feed information back to design teams for further improvement. This scenario requires powerful data management and analytics in a digital solution that involves the entire product lifecycle.

All these challenges come in addition to classic performance requirements such as comfort and durability, and further pressure the design process. Engineering departments must continuously find ways to deal with more parameters, data and product variations in less time. The traditional verification and validation approach needs to gain efficiency and close the loop with the product in use.

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