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Predicting Crime, Protecting Austin: Data-Driven Forecasts for Safer Communities
We strategically leverage US Census data to select optimal mill locations, minimizing logistics costs and enabling sustainable operations close to major metropolitan markets across the United States.
Powered Prevention Dashboards combines advanced NLP and visual analytics spotlighting patterns behind manufacturing hand injuries, transforming insights into proactive safety strategies.
"Optimizing Kimberly-Clark’s U.S. Expansion: Data-Driven Mill Locations to Slash Logistics Costs & Boost Market Reach"
Ergonomic Insights to Prevent Machine Related Hand Injuries
We've created an automated tool to analyze workplace injuries. and help organizations design better awareness campaigns and make data-backed decisions to reduce injuries before they happen.
We used Python, Maps API & Tableau to find Sheboygan, WI as a top KC mill site with 47% savings and $3.5M logistics cost—smart, strategic, and data-driven.
AI-powered injury analysis tool that helps SEG identify root causes, assess severity, and prioritize safety actions by company, event type, and location.
"We turned 20,000+ OSHA injury reports into AI-driven safety insights. Using NLP, we decoded how and why hand injuries happen — so companies can stop them before they do."
In this project, we try to find out the factors of injuries in each industry, manufacturing for example in this current version, and propose solutions or advice for enhancing safety in.
Data Visualization on Kimberly-Clark’s Problem Statement
Empowering manufacturing with data-driven insights to prevent hand injuries, enhance safety protocols, and ensure a safer workplace for all.
To overcome resource availability challenges, we prioritize access to a skilled workforce, tackle labor shortages, and strategically position facilities near key transportation and supply hubs
We use public data and machine learning to find the best locations for Kimberly-Clark’s next tissue mills—maximizing demand coverage while minimizing logistics cost.
decision-makers in logistics, procurement, or sustainability seeking data-backed efficiency gains
Leveraging OSHA data , Python, Tableau and NLP, we identify root causes of hand injuries in manufacturing to help SEG drive smarter, targeted, and preventive safety interventions.
The objective of this study is to analyze OSHA's Severe Injury Report dataset to identify prevailing patterns and root causes of hand-related injuries within the manufacturing industry.
By analyzing OSHA injury data with AI and NLP, we uncover root causes like pinch points and PPE gaps, delivering targeted safety strategies to prevent hand injuries in manufacturing workplaces.
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