Development of an Automated Suppliers Search Service for a Metallurgical Company


Гасанов Ильяс Запирович

Высшая Школа Экономики
Дата публикации 03.05.2023
Период публикации
Рекомендуемая ссылка: Гасанов Ильяс Запирович, Development of an Automated Suppliers Search Service for a Metallurgical Company. Публикация в СМИ: "Наука и образование ONLINE" (). Дата публикации: 03.05.2023. Регистрация СМИ: ЭЛ №ФС77-70153 от 30.06.2017. URL доступа:


This project aims to develop an automated suppliers search service for a metallurgical company in Russia. The goal of this service is to reduce the time and cost required for purchasing managers to find and compare suppliers. The service will be designed using Python and the Scrapy framework, and will collect data about suppliers from the internet by creating search requests in Yandex. It will include an algorithm for the search process and data collection, pre-filtering of potential suppliers, and a data visualization dashboard for purchasing managers to analyze and compare suppliers. The service will be implemented as a three-tier architecture, ensuring data storage, and data processing are performed separately. By streamlining the supplier selection process, the company may be able to reduce costs, improve product quality and safety, and ultimately gain a competitive advantage in the market. The proposed service has the potential to be monetized and sold to other companies, providing a comprehensive solution for automated supplier search.

Ключевые слова

Python, automated supplier search, dashboard, data collection, metallurgical company, search algorithm

Библиографический список

  1. Copeland, R. (2008). Essential sqlalchemy. " O'Reilly Media, Inc.".
  2. Dabbas, E. (2021). Interactive Dashboards and Data Apps with Plotly and Dash: Harness the power of a fully fledged frontend web framework in Python–no JavaScript required. Packt Publishing Ltd.
  3. Ebrahim, M., & Mallett, A. (2018). Mastering Linux Shell Scripting: A practical guide to Linux command-line, Bash scripting, and Shell programming. Packt Publishing Ltd.
  4. Gokhale, Leena & Mahajan, Kirti. (2020). Comparative Study of Data Visualization Tools.
  5. Gourley, D., Totty, B., Sayer, M., Aggarwal, A., & Reddy, S. (2002). HTTP: the definitive guide. " O'Reilly Media, Inc.".
  6. McKinney, W. (2022). Python for Data Analysis. " O'Reilly Media, Inc.".
  7. Molinaro, A. (2005). SQL Cookbook: Query Solutions and Techniques for Database Developers. " O'Reilly Media, Inc.".
  8. Mitchell, R. (2018). Web scraping with Python: Collecting more data from the modern web. " O'Reilly Media, Inc.".
  9. Nickoloff, J., & Kuenzli, S. (2019). Docker in action. Simon and Schuster.
  10. Patel, J. M. (2020). Getting structured data from the internet: running web crawlers/scrapers on a big data production scale. Apress.
  11. Schönig, H. J. (2020). Mastering PostgreSQL 13: Build, administer, and maintain database applications efficiently with PostgreSQL 13. Packt Publishing Ltd.
Если прикрепленный файл не отображается, перегрузите, пожалуйста, страницу

Скачать (DOCX, 30KB)