Project Background：

With the rapid development of self-driving technology, self-driving heavy trucks, as an emerging force in the logistics and transportation industry, are gradually changing the traditional freight mode. Especially in the Guangdong-Hong Kong-Macao Greater Bay Area, the development of this technology is particularly rapid, attracting the attention and investment of many enterprises. However, with the continuous expansion of the market and the intensification of competition, enterprises need to understand the competitors in the industry in order to better formulate their own development strategies and market positioning.

Customer pain points：

As an enterprise with a certain market share in the field of self-driving heavy trucks, the main pain points faced by customers include: insufficient understanding of competitors' technological development level, market strategy and future planning; lack of accurate prediction of industry trends, it is difficult to grasp market opportunities; and insufficient exploration in the application of emerging technologies and business model innovation, it is difficult to form a differentiated competitive advantage.

Project Purpose：

The purpose of this project is to provide in-depth competitor research services for autonomous heavy truck companies in the Guangdong-Hong Kong-Macao Greater Bay Area, and to help customers fully understand the operating conditions, technological innovation, market strategies and development potential of major competitors in the industry, so as to provide customers with Strategic planning, market expansion and product innovation provide strong data support and decision-making basis.

Research object：

The research objects mainly include self-driving heavy truck manufacturers, technology research and development companies, logistics service providers and relevant policy-making and regulatory agencies in Guangdong Province. The total number of research objects is 20.

Project Content：

1. Competitor profiling: Collect and analyze basic information, development history, organizational structure and market positioning of competitors.

2. Evaluation of the level of technological development: to study the technological research and development progress, patent applications and technical cooperation of competitors in the field of self-driving heavy trucks.

3. Comparative study of market strategies: analysis of competitors' market entry strategies, product positioning, pricing strategies and promotional activities.

4. Operating conditions and financial analysis: Assess the operating efficiency, profitability and financial condition of competitors through public financial statements and market data.

5. Forecast of future development trends: based on current data and market conditions, predict the future development direction and industry trends of competitors.

Policy Environment and Regulatory Analysis: Study the policy environment and regulatory measures affecting the autonomous heavy truck industry and assess their potential impact on competitors.

Project Value：

Through this competitor survey, customers can obtain the following values:

1. Systematization of competitive intelligence: A complete system of competitive intelligence collection and analysis has been established to improve the market sensitivity and resilience of enterprises.

2. Data support for strategic decision-making: provides detailed data and in-depth analysis, providing strong data support for customers' strategic planning and decision-making.

3. Enhancement of market competitiveness: By understanding the strengths and weaknesses of competitors, customers can better adjust their market strategies and enhance market competitiveness.

4. Establishment of risk management and early warning mechanism: help customers identify potential market risks, establish early warning mechanism, and prepare in advance.

5. Improvement of operational efficiency and profitability: By learning from the successful experiences and lessons of competitors, customers can optimize their own management and improve operational efficiency and profitability.

Entrusted project execution time: January 2024