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시장에서 성공하기 위해서는 어떤 사양이 필요할까요? 어느 OEM이 변하는 시장 수요를 충족할 적절한 기술을 장착한 제품을 제공할 것으로 예상되나요? 수많은 컴포넌트에 걸쳐 수백 가지 기능이 장착된 오늘날에는 차량을 서로 견주어 보며 정확하게 벤치마킹하기란 어려울 수 있습니다. 당장 오늘만 살펴보더라도 그러한데, 아직 출시되지 않은 미래의 차량 및 신기술까지 고려하면 더더욱 불가능 한 일이 됩니다. 지난 6년간은 최첨단 기술 번들을 갖춘 75개의 새로운 브랜드가 light vehicle 세그먼트에 입장했습니다. 다가올 6년 동안은 얼마나 많은 브랜드가 출시되고 어떤 기능이 탑재될까요? 더 중요한 질문은, 우리는 어떻게 이 모든 것을 파악하고 차량을 대조 할 수 있을까요? 기능 및 기술 벤치마킹은 15개의 도메인에서 1,000개 이상의 기술 유형을 다룹니다:
S&P Global은 OEM과 공급업체가 최신 기술 동향과 더불어 현재와 미래의 OEM 기술 편향 및 경쟁력 있는 포지셔닝을 이해하도록 돕습니다. 새로 선보이는 '기능 및 기술 벤치마킹(FTB)' 솔루션은 사용자가 어느 차량 세그먼트에서 어떤 기술을 채택할지 파악하고, 특정 시장들에 존재하는 격차에 대한 통찰을 제공합니다.
FTB 그래픽 분석을 사용하여 복잡한 질문에 대한 통찰력을 쉽게 확보하세요.
He leads an integrated product team focusing on Automotive Supply Chain, Technology and Aftermarket (SCT&A) team with research, forecasting, product management and data science responsibility. Matteo specializes in automotive supply chain, automotive technology research and OEM procurement strategy since 2004, having developed several quantitative metrics and indexes on supply chain-related topics, for example on OEM-supplier relations, supplier benchmarking and supply chain resiliency. Before leading the 90-strong (SCT&A) global product team, he established a team of consultants, advising a variety of auto sector stakeholders around market entry strategies, supply chain assessment and technology benchmarking. In his early career with S&P Global in an analyst capacity, he authored numerous studies on OEM procurement strategy in all major regions as well as automotive technology, ranging from carbon fibre to infotainment systems.Before joining S&P Global, Matteo worked in Procurement at an OEM, focusing on product development and procurement business processes. He holds a Master of Science in Automotive Engineering with a major in vehicle systems and sub-systems development from the Polytechnic University of Turin, Italy.
Jeremy specializes in the areas of autonomous driving, mobility, and automotive technology. He has worked in automotive electronics market research, forecasting, and analysis with a focus on driver assistance, sensors, autonomous vehicles, and mobility since 2008. Complementary research includes technical topics, regulation and legislation, and the growth of new and emerging technologies into markets around the world. He has extensive experience in making presentations to clients and media worldwide.He has worked with a number of OEM, supplier, and technology companies in supporting both syndicated and custom analysis to support critical business decisions that shape the landscape of the automotive and transportation industries.Jeremy earned his Bachelor of Arts in Italian Studies from the University of Wisconsin-Madison, US, including a period of study at the Università di Bologna, Italy.
Previously employed within Engineering, Graham has 10 years' experience in Powertrain Development and Noise, Vibration and Harshness (NVH) having previously worked for Ricardo, Bentley Motors and Jaguar Land Rover. He joined IHS Markit in 2016. IHS Markit merged with S&P Global in March 2022.Graham graduated from Loughborough University in the UK with a Master's Degree in Automotive Engineering and is a Chartered Member of the Institution of Mechanical Engineers.
Brian primary responsibilities include looking after research related to automotive connectivity, infotainment, and in-vehicle user experience. His role also includes speaking to the market on behalf of S&P Global at automotive technology-related conferences.Prior to joining S&P Global (Now a part of S&P Global) in 2015, his work experience includes several market research positions in automotive, finance, and urban development.Brian received both a Bachelor of Business Administration in Marketing and a Bachelor of Arts in Political Science from the University of Michigan-Dearborn.
Understand the dispersion of specifications among competing brands and technology domains
Evaluate market share by technology
Understand the dispersion of specifications among competing brands and technology domains
Query and export 300+ technologies at model level