Optimization of Financial Resources Allocation in Medical Device Production Companies through Artificial Intelligence: An Integrated Approach

Abstract

This proposal for my master's thesis aims to investigate how artificial intelligence (AI) can be used to optimize the allocation of financial resources in medical device production companies. The proposal focuses on an integrated approach that combines principles from engineering management, such as technical understanding, systems thinking, process optimization, innovation and technology adoption, data analysis and decision-making, and continuous improvement. These principles will be applied to the context of financial resource allocation optimization. The research question is how AI can enhance the allocation of financial resources in medical device production companies and its impact on financial performance and operational efficiency. By incorporating principles from engineering management, this research will address the complex challenges associated with optimizing financial resource allocation. The framework proposed will combine principles of engineering management into the optimization process. Technical understanding will be utilized to gain insights into the intricate financial dynamics of medical device production companies. Systems thinking will help identify the interconnections and relationships within the financial resource allocation system, enabling a comprehensive analysis of the entire ecosystem. In this research, process optimization techniques will be used to make resource allocation more efficient and cost-effective. Innovative technologies by implementing AI algorithms and models should be also adopted. These AI tools will use historical data and advanced analytics to help managers make decisions based on data. By doing so, the research aim to improve the outcomes of financial resource allocation and empower managers to make informed choices. Data analysis will be essential in finding patterns, trends, and areas where the financial system can be improved. By analysing financial data, managers can gain valuable insights to make better decisions about how to allocate resources. AI models will also help in decision-making by allowing real-time adjustments and identifying the best strategies for resource allocation. Continuous improvement is a key focus of this research. It is understandable that the financial situations are always changing, and it's important to be adaptable. By regularly monitoring and evaluating the effectiveness of the AI-based resource allocation models, technological product companies can adjust and improvements to their strategies from the financial point of view. This ongoing process of improvement will help ensure that companies achieve sustainable financial performance and operational efficiency in the long term run especially in medical device production companies which face with a fast technologically changing environment and due to high volume of research and huge need for developing new products, they have to manage their financial resources more intelligently. To make sure the proposed integrated approach is valid, actual financial data and collaborate with industry experts will be analysed. The research will carry out practical evaluations to measure how the proposed framework affects the financial performance and operational efficiency.