Home
World Journal of Advanced Research and Reviews
International Journal with High Impact Factor for fast publication of Research and Review articles

Main navigation

  • Home
    • Journal Information
    • Editorial Board Members
    • Reviewer Panel
    • Abstracting and Indexing
    • Journal Policies
    • Our CrossMark Policy
    • Publication Ethics
    • Issue in Progress
    • Current Issue
    • Past Issues
    • Instructions for Authors
    • Article processing fee
    • Track Manuscript Status
    • Get Publication Certificate
    • Join Editorial Board
    • Join Reviewer Panel
  • Contact us
  • Downloads

eISSN: 2581-9615 || CODEN: WJARAI || Impact Factor 8.2 ||  CrossRef DOI

Research and review articles are invited for publication in April 2026 (Volume 30, Issue 1) Submit manuscript

Revolutionizing inventory management: A comprehensive automated data-driven model using power BI incorporating industry 4.0

Breadcrumb

  • Home
  • Revolutionizing inventory management: A comprehensive automated data-driven model using power BI incorporating industry 4.0

Akash Abaji Kadam 1, *, Ramakrishna Garine 2 and Supriya Akash Kadam 3

1 Department of Mechanical Engineering, Servotech Inc, Chicago, IL, 60616, USA.
2 Department of Mechanical Engineering, University of North Texas, Denton TX 76207, USA.
3 Department of Purchasing, TCCI, Decatur, IL, 62526, USA.
 
Research Article
World Journal of Advanced Research and Reviews, 2024, 24(01), 477-488
Article DOI: 10.30574/wjarr.2024.24.1.3035
DOI url: https://doi.org/10.30574/wjarr.2024.24.1.3035
 
Received on 22 August 2024; revised on 01 October 2024; accepted on 04 October 2024
 
This is a study on advanced inventory management approaches in the mechanical industry using data-driven models, accompanied by Power BI analytics. Drawing insights from broad collaboration with relevant industrial expertise at different organizational scales, our research work addresses the intricacies and challenges in maintaining optimal inventory levels concerning growing industries and operational risks.
In this global mechanical firm, we analyzed historical data using descriptive and predictive analytics to understand the trends of the past and to forecast future inventory needs. Descriptive analytics provided base insights about the present status of inventory, while predictive analytics helped proactive management of stock levels classified into on-hand and critical level categories for greater efficiency in strategies related to planning at the coordination end with its suppliers and mitigating risks.
Core to our methodology will be the implementation of an automated data-driven machine-learning approach that brings minimal intervention from humans to solve some of the most critical problems in inventory management. One major contribution this study adds is a major product: a Power BI dashboard that visualizes critical part numbers falling below threshold values and recognizes suppliers with historical shipment shortages. Armed with this intuitive dashboard, supplier-coordinating engineers are better placed to take proactive action on inventory shortages, greatly improving operational efficiency in supply chain resilience.
 
Inventory management; Power BI; Industry 4.0; Big data; Supply chain management
 
https://wjarr.com/sites/default/files/fulltext_pdf/WJARR-2024-3035.pdf

Preview Article PDF

Akash Abaji Kadam, Ramakrishna Garine and Supriya Akash Kadam. Revolutionizing inventory management: A comprehensive automated data-driven model using power BI incorporating industry 4.0. World Journal of Advanced Research and Reviews, 2024, 24(1), 477-488. Article DOI: https://doi.org/10.30574/wjarr.2024.24.1.3035

Copyright © Author(s). All rights reserved. This article is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, as long as appropriate credit is given to the original author(s) and source, a link to the license is provided, and any changes made are indicated.


All statements, opinions, and data contained in this publication are solely those of the individual author(s) and contributor(s). The journal, editors, reviewers, and publisher disclaim any responsibility or liability for the content, including accuracy, completeness, or any consequences arising from its use.

Get Certificates

Get Publication Certificate

Download LoA

Check Corssref DOI details

Issue details

Issue Cover Page

Editorial Board

Table of content

Copyright © 2026 World Journal of Advanced Research and Reviews - All rights reserved

Developed & Designed by VS Infosolution