Python

Analyzing RFID Data Streams to Improve Boeing’s Supply Chain & Logistics Operations

Radio frequency identification (RFID) is a technology that can identify and track tags attached to objects using electromagnetic fields. Throughout Boeing’s main supply chain & logistics facilities, RFID technology is used to track the movement of their inventory, tools, machinery, and employees. This system generates hundreds of thousands of data points, and our task was to analyze this data to provide Boeing with meaningful insights about their facilities. Our final product consisted of an interactive Tableau dashboard that can easily showcase the most important metrics. Our hope is that Boeing can continue to use and build upon our work to enable a faster, more efficient supply chain.

Cloud Coverage Prediction to Improve Solar Power Management

Cloud movement prediction is a difficult problem that has been studied for many years. As humans, we can easily identify where the clouds are and maybe predict their movement within a couple of minutes; however, we are unable to predict beyond a short period of time as cloud size, altitude, wind velocity, and other weather conditions contribute to it being a very complicated task. But this research is important because cloud prediction gives advance notice to grid managers of coming solar energy shortage or spikes, allowing the system to prepare and buy energy from other sources as needed. In this paper, we continue the work of many researchers over the last decade, finding new ways to process the images and creating new neural networks to handle predictions.

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