This study introduces an innovative method to regulate the target price of a new automotive product by amalgamating deep learning, game theory, and control theory. Through this interdisciplinary approach, we aim to enhance pricing strategies to align with market demands and optimize profitability. Specifically, we leverage Artificial Intelligence, employing Convolutional Neural Networks (CNNs) to extract and analyze cost features from design data, such as Product Lifecycle Management Software. Subsequently, a Generative Adversarial Network (GAN) facilitates effective management of design choices. By integrating Learning-based control, utilizing a reinforcement learning model, the game dynamics converge to the Nash equilibrium, thereby achieving optimal cost values aligned with the target cost.

Car manufacturers face challenges in optimizing platforms and portfolio management to meet evolving customer demands, regulatory requirements, and technological advancements. By leveraging the power of data science and deep learning, automakers can gain valuable insights into consumer preferences, market trends, and product performance, enabling informed decision-making for platform and portfolio strategies. In the automotive industry, product features play a significant role in shaping consumer perceptions, preferences, and purchasing decisions. This article explores how data science and optimization techniques can be used to develop and implement effective product feature strategies. By integrating Python programming with advanced analysis and optimization algorithms, automakers can Deploy IDSS to enhance competition, customer satisfaction, and innovation in the automotive sector.