Architecting Real-Time Material Tracking from Suppliers across Manufacturing Shifts

The need for uninterrupted production flow and operational efficiency has led to the implementation of real-time material tracking systems in manufacturing industries. One of the most important factors in sustaining this flow is the continuous availability of materials from suppliers, particularly over extended manufacturing shifts. Inventory mismanagement or delayed deliveries from suppliers can lead to cascading inefficiencies, ranging from machine downtime to missed delivery targets. This work suggests an architecture for real-time tracking of material across suppliers to production lines with a focus on operations that extend across multiple shifts. Utilizing IoT sensors, RFID, edge computing, and cloud-integrated dashboards, the system provides real-time visibility into material movement. In addition, it integrates predictive analytics and shift-based scheduling smarts to maximize stock levels and reduce human intervention. The approach is developed to integrate with Just-in-Time (JIT) inventory systems and Industry 4.0 strategies, thus increasing transparency, traceability, and responsiveness in supply chain processes. Field deployment in a mid-sized electronics factory showed a 27% increase in the accuracy of material arrivals and a 34% decrease in downtime attributable to material unavailability. The findings validate the importance of having a solid, smart tracking system in upholding operational effectiveness, particularly in shift-based manufacturing environments. The paper ends with an outline of challenges in scaling, data unification, and coordinating suppliers, providing insights into future improvements using AI and blockchain for secure, autonomous decision-making.

Authors

Publication Details

Journal
International Journal For Multidisciplinary Research
Published
2024-12-10
DOI
https://doi.org/10.36948/ijfmr.2024.v06i06.42373
Primary Topic
Assembly Line Balancing Optimization
Type
article
Field-Weighted Citation Impact
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article

Architecting Real-Time Material Tracking from Suppliers across Manufacturing Shifts

Ravikumar Thinnatti Palanicham
International Journal For Multidisciplinary Research
Assembly Line Balancing Optimization
article

Architecting Real-Time Material Tracking from Suppliers across Manufacturing Shifts

Ravikumar Thinnatti Palanicham
article en

Abstract

The need for uninterrupted production flow and operational efficiency has led to the implementation of real-time material tracking systems in manufacturing industries. One of the most important factors in sustaining this flow is the continuous availability of materials from suppliers, particularly over extended manufacturing shifts. Inventory mismanagement or delayed deliveries from suppliers can lead to cascading inefficiencies, ranging from machine downtime to missed delivery targets. This work suggests an architecture for real-time tracking of material across suppliers to production lines with a focus on operations that extend across multiple shifts. Utilizing IoT sensors, RFID, edge computing, and cloud-integrated dashboards, the system provides real-time visibility into material movement. In addition, it integrates predictive analytics and shift-based scheduling smarts to maximize stock levels and reduce human intervention. The approach is developed to integrate with Just-in-Time (JIT) inventory systems and Industry 4.0 strategies, thus increasing transparency, traceability, and responsiveness in supply chain processes. Field deployment in a mid-sized electronics factory showed a 27% increase in the accuracy of material arrivals and a 34% decrease in downtime attributable to material unavailability. The findings validate the importance of having a solid, smart tracking system in upholding operational effectiveness, particularly in shift-based manufacturing environments. The paper ends with an outline of challenges in scaling, data unification, and coordinating suppliers, providing insights into future improvements using AI and blockchain for secure, autonomous decision-making.

International Journal For Multidisciplinary ResearchVol. 6(6)
Openalex Percentile: Top 57%
Assembly Line Balancing Optimization
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