An improved triangular management framework for improve the automotive vehicles using decision based data analysis

The automotive industry changes quickly due to incredible new technologies and client demands. Automotive manufacturers must constantly push the envelope to improve their vehicles to be competitive. In practice, this is easier said than done — it demands near-impossible management and decision-making throughout all levels of the organization. To handle this, we put forward a novel decision-driven data triangulation framework. Data Collection and Analysis A framework consists of data collection, decision-making, and implementation. Component 1: — Here, data is collected from different sources like customer feedback, market trends and internal processes. Machine learning and predictive analytics are all employed to sift out in the data what it is that's valuable. The decision-making component then uses this data to make informed decisions and set direction for improvement. This involves establishing a sense of the areas most important for change and detailing the exact process directing change. The third component is executing these decisions, continually monitoring and iterating to determine a better decision. Fused with sound decision-making and implementation requirements, our framework empowers automotive manufacturers to drive tangible vehicle improvement through data-driven decisions. It helps manufacturers maintain fast development in the automotive market and improves customer satisfaction and loyalty.