International Journal on Science and Technology

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Cloud-Based Predictive Analytics for Slot Machine Supply Chain and Casino Operations: A GCP BigQuery Approach

Author(s) Urvangkumar Kothari
Country United States
Abstract With cloud covering your back, the slot machine industry changed entirely and it now offers real-time, scalable, and cost-effective solutions for supply chain management and casino operations. Scalability, latency, and data integration with traditional on-premises data architectures can hinder bet-tracking, which is why slot machine operators increasingly implement IoT-based monitoring for their slot machines. In this post, we will look at how Google Cloud Platform (GCP) which includes Big Query, Dataflow, Pub/Sub, cloud composer and looker (BI) is making data driven decision making real-time possible. Through a cloud-native approach its size and its operational ability, we show how predictive analytics on both casino slot machine performance and supply chain logistics together give rise to not only an optimal slot machine inventory design but also predictive maintenance and casino floor profitability.
Keywords Airflow, Machine Learning, Forecast, Dataflow, Slot Machines, Casinos, Predictive Analytics, Big Query, Google Cloud Platform
Field Engineering
Published In Volume 13, Issue 4, October-December 2022
Published On 2022-10-05
Cite This Cloud-Based Predictive Analytics for Slot Machine Supply Chain and Casino Operations: A GCP BigQuery Approach - Urvangkumar Kothari - IJSAT Volume 13, Issue 4, October-December 2022. DOI 10.71097/IJSAT.v13.i4.2822
DOI https://doi.org/10.71097/IJSAT.v13.i4.2822
Short DOI https://doi.org/g899fx
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