REQUIREMENTS FOR THE AUTOMATED OIL SELECTION SYSTEM

Abstract

In the current context of the development of transport infrastructure and industrial technologies, the issue of selecting the correct lubricants, particularly engine oils, is becoming increasingly important. The compliance of oil with technical specifications and real operating conditions directly affects the efficiency, durability, and safety of engines and machinery. However, the wide range of lubricants available on the market, differences in their properties and standards, as well as the lack of universal digital solutions for selection, complicate this process for both professionals and ordinary users. The paper presents an analysis of modern oil selection tools, including online platforms, mobile applications, and ERP system modules. It has been established that existing solutions have significant limitations: a focus on specific brands, insufficient consideration of operating conditions, limited scalability, and weak integration with other systems. This confirms the need to create a new automated decision support system that would be manufacturer-independent, flexible, scalable, and capable of taking into account a wide range of technical and operational parameters.A concept for building such a system is proposed, including modular architecture, integration with external databases via APIs, automatic information updates, and the use of intelligent data processing algorithms, including machine learning.Functional and non-functional requirements for the system have been developed, including high performance, load resistance, personal data protection, and an intuitive user interface.Requirements for the system architecture are proposed in the form of a client interface as a web portal or mobile application, server-side logic for request processing, centralized data storage, and an analytical-recommendation module. Variants of implementing the system as either a local solution for enterprises or a cloud service are considered separately. The technical implementation should be based on open technologies such as Python, Django/Flask, PostgreSQL or MySQL, using modern frontend frameworks.Thus, the proposed approach to creating an automated engine oil selection system ensures high adaptability, recommendation accuracy, and compliance with modern market requirements, which will contribute to improving the efficiency of mainte- nance of vehicles and industrial equipment.