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
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
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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