不要告诉别人（企业案例及分析）周日静学（275）：博士论文5.6 企业案例分析，_美妆技巧

不要告诉别人（企业案例及分析）周日静学（275）：博士论文5.6 企业案例分析，

小小兔 107 2025-11-22

This issues tweet will introduce the doctoral dissertation 《Research on technology innovation cooperation strategy of new energy vehicle supply chain considering information asymmetry under the double credit policy》 from three perspectives: mind mapping, detailed content analysis, and supplementary knowledge, focusing on 5.6 enterprise case analysis.

6.不告诉妈妈

思维导图：Mind mapping

精读内容：Intensive reading content本节以宁德时代与哪吒汽车的合作案例对第五章的部分命题进行说明This section uses the cooperation case between CATL and NETA Auto to illustrate some of the propositions presented in Chapter 5.。

首先，作者介绍了新能源汽车未来发展趋势、电动化何智能化的优势消费者最为关注纯电动汽车的续航里程，随着电化学技术的发展，电动汽车的动力电池经历了从铅酸电池、磷酸电池到三元锂电池技术的演变，电动汽车的续航里程也从最初的100多公里，发展到目前主流的500-600公里甚至更高的续航里程，消费者的里程焦虑问题得到缓解。

未来，电动汽车的发展趋势将以数字化、智能化为特征First, the author introduces the future development trends of new energy vehicles and the advantages of electrification and intelligence. Consumers are most concerned about the driving range of pure electric vehicles. With the advancement of electrochemical technology, the power batteries used in electric vehicles have evolved from lead-acid batteries and lithium iron phosphate batteries to ternary lithium batteries. Consequently, the driving range of electric vehicles has increased from just over 100 kilometers in the early stages to the current mainstream range of 500–600 kilometers or even higher, effectively alleviating consumers’ range anxiety. In the future, the development of electric vehicles will be characterized by digitalization and intelligentization.

然后，作者介绍了共享需求信息对技术创新合作的影响和技术创新合作对新能源技术的影响宁德时代和哪吒汽车共享各自拥有的客户需求信息，促进了双方的技术创新合作，从而可以使新研发的CIIC智能底盘更好地满足消费者的需求。

CIIC智能底盘一旦研发成功，预计可以缩短30%的产品开发时间，降低30%的技术研发投入，底盘通用率可达70%-80%，新能源汽车的续航里程将突破1000公里，这将有助于哪吒汽车在续航里程、安全性及智能驾驶方面达到新水平，可以为用户提供更安全、更舒适、更具操控性的电动汽车。

因此，宁德时代和哪吒汽车共享各自拥有的客户需求信息，以客户需求驱动供应链变短，促进双方的技术创新合作，使产品的技术创新更好地满足消费者需求，从而提升产品的市场竞争力Then, the author discusses the impact of sharing demand information on technological innovation cooperation and the influence of such cooperation on new energy technologies. CATL and NETA Auto share their respective customer demand information, which promotes joint technological innovation and enables the newly developed CIIC intelligent chassis to better meet consumer needs. Once successfully developed, the CIIC intelligent chassis is expected to shorten product development time by 30%, reduce R&D investment by 30%, and achieve a chassis generalization rate of 70%–80%. Moreover, it will enable new energy vehicles to exceed a range of 1,000 kilometers. This advancement will help NETA Auto reach a new level in terms of range, safety, and intelligent driving, providing users with safer, more comfortable, and more controllable electric vehicles. Therefore, by sharing customer demand information and adopting a demand-driven approach, CATL and NETA Auto effectively shorten the supply chain, promote technological innovation cooperation, and ensure that product innovations better align with consumer needs—thereby enhancing the market competitiveness of their products.

最后，作者对本章进行总结先介绍了本章的研究内容，研究了双积分政策下新能源汽车供应链的技术创新与需求预测信息的共享问题考虑制造商和供应商拥有需求预测信息，构建了双积分政策下新能源汽车供应链的技术创新决策模型，分别得到了信息共享和信息不共享情形下新能源汽车供应链的最优技术创新策略。

通过对最优解进行比较，发现信息不共享削弱了双积分政策对技术创新的激励效果，进一步设计了激励机制以实现信息共享随后，介绍了本章的研究结论：（1）双积分政策促进了新能源汽车的技术创新，并且随着积分交易价格或技术创新得分系数的提高，新能源汽车的技术创新提高。

（2）供应商总是愿意共享需求预测信息；而制造商是否共享需求预测信息取决于供应商的技术创新成本系数（3）双积分政策下，制造商和供应商的信息共享区间增加，双积分增强了制造商和供应商信息共享的意愿（4）需求预测信息的准确度及预测信息的相关性都会影响制造商和供应商的利润。

Finally, the author concludes this chapter. The chapter first presents the research content, focusing on technological innovation and demand forecast information sharing in the new energy vehicle supply chain under the dual-credit policy. Considering that both manufacturers and suppliers possess demand forecast information, a technological innovation decision model for the new energy vehicle supply chain under the dual-credit policy is constructed. The optimal technological innovation strategies for both information-sharing and non-sharing scenarios are derived. By comparing the optimal solutions, it is found that the lack of information sharing weakens the incentive effect of the dual-credit policy on technological innovation. Therefore, an incentive mechanism is further designed to achieve information sharing. The main conclusions of this chapter are as follows: (1) The dual-credit policy promotes technological innovation in new energy vehicles. As the credit trading price or the technological innovation coefficient increases, the level of technological innovation in new energy vehicles also improves. (2) The supplier is always willing to share demand forecast information, whereas the manufacturer’s willingness to share depends on the supplier’s technological innovation cost coefficient. (3) Under the dual-credit policy, the information-sharing range between manufacturers and suppliers expands, indicating that the dual-credit policy enhances their willingness to share information. (4) The accuracy and correlation of demand forecast information both affect the profits of manufacturers and suppliers.

知识补充：Knowledge supplement预测方法有哪些？What are the prediction methods?1. 定性预测法：定性预测法主要依赖主观判断和经验进行预测这种方法通常适用于数据不足或难以量化的情况。

1. Qualitative Forecasting Method: This method mainly relies on subjective judgment and experience. It is typically used when data are insufficient or difficult to quantify.

2. 时间序列预测法：时间序列预测法利用历史数据随时间变化的规律进行预测2. Time Series Forecasting Method:This method uses historical data and identifies patterns or trends over time to make forecasts.。

3. 因果预测法：因果预测法通过分析变量之间的因果关系进行预测3. Causal Forecasting Method:This method predicts outcomes by analyzing the causal relationships between variables.。

4. 判断预测法：判断预测法依赖于专家的知识和经验进行判断4. Judgmental Forecasting Method:This method depends on experts’ knowledge and experience to make informed judgments.。

5. 模拟预测法：模拟预测法通过建立系统模型来模拟系统的运行过程，从而进行预测5. Simulation Forecasting Method:This method involves building a system model to simulate its operational process, thereby enabling prediction.。

6. 机器学习预测法：随着大数据和人工智能技术的发展，机器学习在预测中发挥着越来越重要的作用6. Machine Learning Forecasting Method:With the development of big data and artificial intelligence technologies, machine learning plays an increasingly important role in forecasting.。

7. 混合预测法：混合预测法结合了多种预测方法的优点，以提高预测的准确性和鲁棒性例如，可以将时间序列分析与机器学习算法相结合，或者将定量分析与定性分析相结合7. Hybrid Forecasting Method:This method combines the advantages of multiple forecasting approaches to improve accuracy and robustness. For example, it can integrate time series analysis with machine learning algorithms or combine quantitative and qualitative analyses.。

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参考文献：[1] 马淼淼. 双积分政策下考虑信息不对称的新能源汽车供应链技术创新合作策略研究 [D]. 重庆: 重庆大学, 2023.文案|Whisper排版|Whisper审核|Ann

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