The automotive sensors category is expected to grow at a CAGR of 9.4% from 2023 to 2030. This category is driven by the increasing demand for advanced safety features in vehicles due to the rising number of road accidents, the growing trend of vehicle electrification such as electric vehicles, and the increasing demand for sensors that can monitor battery performance and other critical parameters. Additionally, the growth is aided by the increased demand for connected cars, which are designed to collect data on the vehicle's performance and location. It can be used to provide real-time information to the driver and to improve the overall driving performance.
This sector is highly relevant to clients in procurement, as sensors are a critical component of modern vehicles. It involves conducting thorough research and due diligence to identify the most suitable suppliers and negotiating favourable pricing and terms.
This category makes use of cutting-edge technologies like micro-electromechanical systems (MEMS), radar, and Lidar. To detect and measure different characteristics like pressure and acceleration, Robert Bosch uses MEMS. Modern automobiles now perform and operate more safely and efficiently thanks to this technology. To identify and measure the separation between automobiles and other objects, Denso uses radar technology. Advanced Driving Assistance Systems (ADAS) employ this technology to notify motorists of potential hazards. Continental uses Lidar technology to build a 3D map of the area around the car and make safe navigation possible which will help them to reach USD 2,445 million in 2030 by revenue.
Automotive sensors’ price and cost depend on the type of sensor used and how well it performs. The dynamics of supply and demand play a vital role in defining the price of automobile sensors. Due to improvements in manufacturing technology and economies of scale, the cost of making automobile sensors has considerably lowered over time. Businesses typically employ the absorption costing method, which is cumulative of the cost incurred on labor, material, and administration. This approach is frequently utilized because it offers a more accurate depiction of the actual production costs of a product. Printed Circuit Boards (PCBs) and Micro-electromechanical systems (MEMS) contain a variety of components that are made mostly from raw materials like copper and aluminum. Around USD 5.2 is spent on a pound of copper, and the cost of aluminum is around USD 2,150 per ton.
Germany is a major player in this category with several leading companies based in the country such as Continental and Robert Bosch. The German category was valued at USD 4.8 billion in 2022. Japan is another major player with several leading companies such as Denso and Panasonic and was valued at USD 3.8 billion in Japan in 2022.
The automotive sensors category is highly fragmented.There are several participants, ranging from major organizations to small and medium-sized businesses. It is highly competitive, and businesses are always coming up with new ideas to get an advantage. Different sensors utilized for various purposes, such as temperature, pressure, and motion sensor, are used to segment this sector. With the growth of electric and autonomous vehicles, this industry offers enormous potential for growth and innovation. This will open up new doors for businesses to enter the market and create cutting-edge products.
Report Attribute |
Details |
Automotive Sensors Category Growth Rate (CAGR) |
9.4% from 2023 to 2030 |
Base Year for Estimation |
2022 |
Pricing Growth Outlook (Copper) |
4 - 7% |
Pricing Models |
Absorption costing method |
Supplier Selection Scope |
Cost and pricing, Past engagements, Productivity, Geographical presence |
Supplier selection criteria |
Micro-Electromechanical Capabilities,Radar And Lidar Technology,Chrome Data Auto Planner, Integrated Storage, Number Of Production Units, Technical Specifications, Operational Capabilities, Regulatory Standards And Mandates, Category Innovations, And Others. |
Report Coverage |
Revenue Forecast, Supplier Ranking, Supplier Matrix, Emerging Technology, Pricing Models, Cost Structure, Competitive Landscape, Growth Factors, Trends, Engagement, And Operating Model |
Key companies profiled |
Robert Bosch, Denso, Sensata Technologies, Continental, Panasonic, TE connectivity, Delphi Technologies, Infineon Technologies, BorgWarner, and Allegro MicroSystems. |
Regional scope |
Global |
Revenue Forecast in 2030 |
USD 53.35 billion |
Historical data |
2020 - 2021 |
Quantitative units |
Revenue in USD billion and CAGR from 2023 to 2030 |
Customization scope |
Up to 48 hours of customization free with every report. |
Pricing and purchase options |
Avail customized purchase options to meet your exact research needs. Explore purchase options |
b. The global automotive sensors category size was valued at approximately USD 26 billion in 2022 and is estimated to witness a CAGR of 9.4% from 2023 to 2030.
b. The increasing demand for advanced driver assistance systems (ADAS) and increasing demand for fuel-efficient vehicles are driving the growth of the category.
b. According to the LCC/BCC sourcing analysis, Germany and Japan are the ideal destinations for sourcing automotive sensors.
b. This category is highly fragmented with many large players accounting for 50 – 60% of the total share. Some of the key players are Robert Bosch, Denso, Sensata Technologies, Continental, and Panasonic.
b. Raw materials such as aluminum and copper, position sensors, and speed sensors are the major key components of this category. Other key costs include quality control and testing and shipping
b. Cost-saving measures such as bulk purchasing, sourcing from low-cost countries, utilizing E-sourcing tools, and maintaining open communication with suppliers to address any issue are some of the best sourcing practices.
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Component wise cost break down for better negotiation for the client, highlights the key cost drivers in the market with future price fluctuation for different materials (e.g.: steel, aluminum, etc.) used in the production process
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Determining and forecasting salaries for specific skill set labor to make decision on outsourcing vs in-house.
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