Project Management in Electronics Using Artificial Intelligence

 

Project Management in Electronics Using Artificial Intelligence

Autor: Francisco Prats Quílez

1.     Introduction

Currently, electronics project management is conducted analytically by a project manager, who must handle tasks such as personnel management, task allocation, time estimation, and offer generation. This traditional approach can be prone to human errors and inefficiencies. As a team of engineers begins working on the development of an electronic product, the complexity and workload can increase significantly. Artificial Intelligence (AI) has emerged as a powerful tool to optimize this process, providing innovative solutions that improve efficiency and accuracy at various project stages. This text examines the implementation of an AI-based electronics project management system, highlighting its benefits, challenges, and results.

2- Project Objectives

• Process Automation: Implement AI models and tools to automate project processes by analyzing input information and generating output information efficiently. This process will be managed through an AI agent.

• Integration with Project Management Platforms: Use APIs and Python to control and integrate different engineering project management platforms, facilitating synchronization and information flow between them.

• Improvement of Large Language Models (LLM): Address the indeterminacy of current models using techniques such as Retrieval-Augmented Generation (RAG), improving the consistency and accuracy of the results generated by the LLM.

3. Methodology To demonstrate the implementation of AI in electronics project management, a generic project is structured into several key phases:

• Initial Project Phase: High-Level Requirements Analysis – Proposal Presentation In the initial project phase, clients present a problem for which we must offer a solution proposal. Different AI agents can assist in this process by generating summaries of initial meetings or proposing solutions to pending issues. The following image illustrates how AI models and tools support each stage of this initial project phase.

• PCB Hardware Design Phase During the development of a PCB, a hardware engineer creates the PCB requirements based on the general project requirements, from which they then develop the PCB. This process includes several stages, such as creating block diagrams or documenting the hardware description. Methods like Retrieval-Augmented Generation (RAG) can help in component selection, datasheet analysis, or schematic review. The following image provides an example of this phase.

• Product Validation and Verification Phase In the final phases of the project, a Validation and Verification (V&V) engineer and a SW Test engineer can use different AI agents to, for example, select test instrumentation or develop test software in Python, based on the defined test cases. The following image shows an example of this phase.

Conclusions The implementation of an AI-based electronics project management system proved to be an effective solution for addressing the traditional challenges of the sector. AI's ability to automate processes, optimize resources, and proactively detect errors resulted in a significant improvement in project efficiency and quality. This case study highlights the potential of AI to transform electronics project management.

Future Directions

• Capability Expansion: Based on the cited demonstration, the project could be extended to address phases not mentioned in an electronics project, as well as generalize the initial interpretation to adapt it to projects in production areas or a predefined work methodology.
• Continuous Research: Maintain constant research to explore new applications of AI in electronics project management and adapt to emerging market trends and new model capabilities.