Artificial Intelligence-Based Tool for Obsolescence Analysis and Alternative Component Selection

 

Artificial Intelligence-Based Tool for Obsolescence Analysis and Alternative Component Selection

Author: Francisco Prats Quílez

Introduction

In the electronics industry, managing component obsolescence is a critical challenge. Electronic components can quickly become obsolete due to technological advancements and market demands, which can impact the production, maintenance, and updating of electronic products. This case study describes an innovative project that employs artificial intelligence (AI) to automate the analysis of component obsolescence in a Bill of Materials (BOM) and provide viable alternatives for each component.

Objective

The primary objective of this project is to develop an AI-powered tool that enables electronic engineers to automatically analyze the obsolescence of components within a BOM. Additionally, the system should be capable of suggesting alternative components, ensuring production continuity and minimizing risks associated with component obsolescence.

Development

  • BOM Generation and Loading:
    • BOM generation: The BOM is created in Altium Designer (or other software like Flux AI) and exported in .xlsx format.
    • BOM loading: Through a web interface developed in Vue.js, users can upload the .xlsx file path or select a previously saved BOM.


  • Component Visualization:
    • The web interface presents a table displaying all BOM components and their key characteristics. For each component, buttons are provided to:
      • Access the datasheet directly.
      • Access the sales points of various distributors through their APIs.
  • Obsolescence Analysis:
    • Upon clicking the obsolescence analysis button, the system performs the following tasks:
      • Availability evaluation: Determines if the component is obsolete.
      • Risk analysis: Using an LLM model, the probability of each component becoming obsolete in the short, medium, or long term is estimated by analyzing the historical updates of its datasheet and component stock (based on distributor data accessed through APIs).
  • Alternative Analysis:
    • A large language model (LLM) is used to suggest alternative components that meet the specifications of the original component and are not at risk of obsolescence.
  • Automation and Notifications:
    • The system can perform obsolescence analysis automatically every day and send an email if any obsolete components are found.

Conclusions

The implementation of this AI-powered tool for BOM component obsolescence analysis and alternatives has proven effective in proactively managing obsolescence. The ability to automatically assess component availability and provide viable alternatives significantly improves operational efficiency and reduces risks associated with component discontinuation.

Future Development

To enhance and expand the system's functionalities, the following future developments are proposed:

  • Alternative Analysis with RAG Techniques:
    • Implement RAG techniques to improve the accuracy of identifying alternative components.
    • Store all datasheets in a vector database, enabling more efficient and accurate searching.
  • Improved Prompts:
    • Optimize prompts used by the LLM to enhance the quality of suggested alternatives.
    • Incorporate user feedback to continuously refine prompts and results.
  • Integration with More Distributor APIs:
    • Expand integration with more distributors to obtain a wider variety of component sources and prices.
  • Advanced Predictive Analysis:
    • Implement more advanced predictive models to improve the accuracy of obsolescence risk analysis.
  • Enhanced User Interface:
    • Develop a more intuitive user interface with better data visualization capabilities to facilitate the interpretation of results.

The evolution of this tool will continue to focus on incorporating advanced AI techniques to provide even more robust and accurate solutions to the challenges of electronic component obsolescence.

 

Comentarios

Publicar un comentario

Entradas populares de este blog

Generación Automática de Procedimientos de Test con IA

Imagen
  Generación Automática de Procedimientos de Test con IA Autor: Francisco Prats Quilez Introducción Este proyecto presenta una solución ágil y eficiente para la generación automatizada de procedimientos de test, aprovechando las capacidades de los modelos de lenguaje de gran tamaño (LLM). Al integrar una interfaz de usuario intuitiva en Vue.js con un backend que gestiona la generación de prompts y la interacción con el LLM local, el sistema ofrece una forma rapida de crear documentación de alta calidad a partir de una variedad de fuentes. Objetivo El objetivo principal del proyecto es agilizar y mejorar la precisión en la elaboración de procedimientos de test, reduciendo la carga de trabajo de los ingenieros de validación y verificación. Al automatizar gran parte del proceso, se busca garantizar la coherencia y exhaustividad de la documentación, al tiempo que se minimiza el riesgo de errores manuales. Desarrollo Interfaz de Usuario: Se desarrolló una interfaz ...
Leer más

Automated Generation of Test Procedures with AI

Imagen
  Automated Generation of Test Procedures with AI Author: Francisco Prats Quilez   Introduction This project presents an innovative solution for the automated generation of test procedures, leveraging the capabilities of large language models (LLMs). By integrating an intuitive user interface in Vue.js with a backend that manages prompt generation and interaction with a local LLM, the system offers an efficient way to create high-quality documentation from a variety of sources. Objective The primary objective of the project is to streamline and improve the accuracy of test procedure development, reducing the workload of validation and verification engineers. By automating a large portion of the process, the goal is to ensure the consistency and comprehensiveness of documentation, while minimizing the risk of manual errors. Development User Interface: A Vue.js-based interface was developed to allow users to easily upload initial project documents. T...
Leer más

Implementation of an Automated Information Query System for NoSQL Databases with Artificial Intelligence

Imagen
  Implementation of an Automated Information Query System for NoSQL Databases with Artificial Intelligence Author: Francisco Prats Quílez Introduction In today's data management context, NoSQL databases such as MongoDB play a crucial role due to their flexibility and scalability. However, querying and extracting relevant information from these databases can be challenging due to the diversity and variability of data structures. This case study presents the development and implementation of an automated system for searching and querying information in NoSQL databases using artificial intelligence, specifically large language models (LLM). Objective The objective of this project is to design a system that automates the detection of data structures in MongoDB databases and allows for natural language queries. This system should generate and execute Python code to extract and visualize the requested information, significantly improving the efficiency and accessibility of data...
Leer más