Optimizing demand forecasting and inventory management with AI in automotive industry

Abstract

Market dynamics are continuously influenced by changing consumer demands and technological advancements in the automotive industry and the traditional demand forecasting and inventory management methods fall short for these rapid evolvements. This thesis validates an approach on integrating Artificial intelligence (AI) to enhance these methods in the industry particularly in focus with the volatile and evolving market. By adopting AI driven models, the study demonstrates how automotive manufacturers can improve accuracy of forecasts and optimize inventory levels for excess and shortages of stocks.

The study uses a qualitative research method, incorporating extensive literature review and practical case studies. The study highlights the comparative effectiveness of the traditional vs AI powered systems for demand forecasting and inventory management. The results reveal AI being effective and surpassing conventional methods in handling complex, volatile market conditions by utilizing real time data analysis and adaptive learning algorithms. The practical usage of AI is reflected through case studies highlighting potential cost savings and overall efficiency improvements.

The thesis also discusses the barriers in adoption of AI in the industry, such as data quality, integration challenges, and the necessity of shift in organizational culture towards data-driven decision-making. The conclusion ends with strategic recommendations for transition towards AI enhanced operations, improving the competitive edge and contributing to a robust and responsive supply chain within the automotive industry. This study is expected to be beneficial for the automotive industry, stakeholders, policymakers, and academic scholars aiming to understand or implement AI driven solutions in similar contexts.