Dynamic Decisions in Automotive Product Development: A Stage-Gate Framework Enhanced by Data Science and System Dynamics

The automotive industry is characterized by rapid technological advancements and increasing market competition, necessitating effective product development strategies. The complexity of product development in the automotive industry calls for structured methodologies to ensure efficiency and effectiveness. The stage-gate process offers a framework for managing product development, dividing the process into stages with decision points (gates and milestones) to review progress. However, traditional methods often lack the agility required in contemporary markets. This study explores how data science and system dynamics can enhance managerial decision-making in new product stage gates under uncertainty within the automotive industry. By integrating data-driven techniques including neural networks, deep learning models and system dynamics modeling, we aim to refine decision-making processes, reduce risks, and choose the right strategic direction to improve product development outcomes. We apply these methods to a case study in the automotive sector, demonstrating the practical benefits and effectiveness of data science in managing uncertainties during the new product development lifecycle.

کلیدواژه ها:

Stage Gate Decisions ، Data Science ، System Dynamics ، Neural Networks ، Strategic Direction ، Automotive Industry