College-Presentations

This is a repository featuring several different projects created during different courses at UNT. These include:

• Pathways to a Greener Future: CO₂ Emissions - This project examines global CO₂ emissions trends from 1960 to 2022 to identify key contributors, and propose data-driven solutions to support a 15% reduction target by 2030. The analysis was conducted using Excel, Tableau, and Power BI, leveraging the Six Sigma DMAIC methodology to ensure a structured and effective approach.
• Waiting Line Analysis - Froth Coffee Bar - This project analyzes utilizes descriptive statistics, probability distributions, and Monte Carlo simulations to identify bottlenecks, evaluate service capacity, and recommend data-driven solutions using Excel.
• Amazon’s Strategic Innovation - This project analyzes Amazon’s innovative strategies to mitigate supply chain disruptions by leveraging vertical integration and data-driven logistics optimization

This project was original created in a GitHub Repository. If you experience any formatting issues, please view the original GitHub project.

Table of Content

1) CO₂ Emissions Project

3) Waiting Line Analysis

5) Amazon Strategic Innovations

Pathways to a Greener Future: CO₂ Emissions - Project Link

Project Overview

This project examines global CO₂ emissions trends from 1960 to 2022 to identify key contributors, and propose data-driven solutions to support a 15% reduction target by 2030. The analysis was conducted using Excel, Tableau, and Power BI, leveraging the Six Sigma DMAIC methodology to ensure a structured and effective approach.

DMAIC Approach

1) Define: Identified key sources of fossil fuel-based CO₂ emissions (industries, transportation, food production).

3) Measure: Collected and cleaned data from OurWorldInData, ensuring accuracy in global emissions reporting.

5) Analyze: Discovered a 311% increase in CO₂ emissions (1960-2022) and identified top contributors (U.S., China, India, Russia).

7) Improve: Developed interactive dashboards to visualize emissions trends and propose actionable insights.

9) Control: Recommended clean energy initiatives (electric vehicles, renewable energy adoption) to help governments and organizations track progress toward reduction goals.

Key Findings

• CO₂ emissions have increased by 311% since 1960. Top 4 contributors (U.S., China, India, Russia) account for a significant share of global emissions.
• High-income nations are plateauing, while upper-middle-income nations’ emissions are rising
• Technology and policy measures impact emissions more than income levels alone
• Deficiencies in energy reporting affect accuracy—better tracking mechanisms needed

  Conclusion

  This project highlights the urgent need for data-driven strategies to combat rising CO₂ emissions, which increased 311% since 1960 worldwide), and mitigate climate change. By leveraging the Six Sigma DMAIC framework, we analyzed global emissions data, identified top contributors, and proposed practical solutions for reducing emissions by 15% by 2030.

---

Waiting Line Analysis - Froth Coffee Bar - Project Link

Project Overview

To optimize customer wait times and service efficiency at Froth Coffee Bar using data analytics, queueing theory, and simulation modeling. This analysis utilizes descriptive statistics, probability distributions, and Monte Carlo simulations to identify bottlenecks, evaluate service capacity, and recommend data-driven solutions using Excel.

Simulations & Insights:

Simulation 1 (2 Servers – Current Setup)

• Modeled 1,000 customers using Monte Carlo simulations
• Results:
  • Average wait time: 4.19 min
  • Total time in system: 8.9 min
  • 5% of customers wait over 20 minutes
  • Only 60% of customers experience a wait time within the acceptable 2-minute threshold #### Simulation 2 (3 Servers – Optimized Model)
• Increased staffing to 3 servers, reran simulation with 1,000 customers
• Key Results:
  • Wait time reduced by 91% (38 sec avg.)
  • Service time decreased by 38% (5.5 min avg.)
  • Maximum wait time reduced by 61.5% (from 40 min to 15.4 min)
  • 83% fewer customers waiting over 20 minutes
  • 65% of orders completed in under 6 minutes Results indicate that adding a third server significantly improves efficiency, reduces peak-hour congestion, and enhances customer experience.

    Final Recomendations

    1. Add a Third Server
    

  • The cost of hiring another server ($10/hr) is justified by the increased throughput and customer retention.
  • Reducing bottlenecks improves sales potential during peak hours.
    1. Implement a Self-Service Kiosk
  • A tablet-based ordering system ($800 one-time cost, 2.6% per transaction fee) can reduce wait times by shifting order placement to customers.
  • Allows servers to focus on faster order fulfillment.
    1. Optimize Staffing During Off-Peak Hours
  • Analysis suggests reducing staff after 2 PM when demand decreases, cutting labor costs while maintaining efficiency.

    Conclusion

    By applying Monte Carlo simulations and data analytics, this study provides data-driven recommendations that optimize customer wait times, improve service efficiency, and enhance overall business performance. Implementing a third server, self-service kiosks, and online ordering can significantly reduce congestion and increase revenue potential at Froth Coffee Bar.

---

Optimizing Supply Chain Efficiency: Amazon’s Strategic Innovations - Project Link

Project Overview

This project analyzes Amazon’s innovative strategies to mitigate supply chain disruptions, particularly during the 2022 holiday season. By leveraging vertical integration and data-driven logistics optimization, Amazon significantly reduced shipping delays compared to competitors.

Key Insights & Solutions:

• In-House Cargo Containers: Amazon manufactured 10,000 cargo containers to streamline domestic distribution and eliminate dependencies on external shipping cycles.
• Project Dragonboat: Amazon chartered its own ships to bypass congested ports and use smaller, more efficient docking locations.
• Prime Air Expansion: With 85 aircraft and 164 daily flights, Amazon enhanced its ability to quickly move high-demand goods.

  Impact:

• Reduced port delays from 45 days to 2 days
• Increased in-house package handling from 47% (2019) to 72% (2022)
• Lowered reliance on third-party logistics providers

---