Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{202300,
        author = {Naveen G and Gnanasekar V and Bhuvanesh K and Jaishreeakash S and Palanimurugan J and Avinash S.K},
        title = {Smart Industrial Management and Analytics System Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {6528-6542},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202300},
        abstract = {Industrial management involves handling different forms of industrial information, including company investment details, employment statistics, sector classification, and operational growth records. For administrators and investors, manually analyzing these details can often become difficult, time-consuming, and less reliable, especially when managing large industrial datasets. To simplify this process, this project presents the SIPCOT Industrial Management and Analytics System using Machine Learning, a web-based platform developed to support efficient industrial administration and intelligent decision-making. The system integrates industrial data management, company authentication, analytical processing, and machine learning techniques within a unified environment so that industrial performance can be evaluated from multiple perspectives.
The framework analyzes industries by examining a combination of industrial parameters and analytical indicators. Important factors such as investment value, employee count, sector category, company growth, and industrial performance records are evaluated to understand the stability and development potential of organizations. At the same time, machine learning techniques and clustering methods are used to identify industrial patterns, compare company performance, and generate meaningful analytical insights. To improve accessibility and user interaction, the platform also includes an AI-powered chatbot interface that enables users and investors to ask questions regarding industries and quickly obtain relevant suggestions based on verified company authentication data.},
        keywords = {},
        month = {May},
        }