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Advanced Data Analytics Study with Schulich School of Business

Overview

A comprehensive data analytics project led by MBA students from the Schulich School of Business. This intensive study focused on the origins of sea vessel inspections, analyzing various metrics to understand their impact on inspection delays at Canadian ports. The project advanced from Technology Readiness Level (TRL) 1 to 4, culminating in the creation of a dashboard from the datasets, with a duration of 4 months and involved Python coding for statistical analysis.

Key Achievements

  1. Advancement to TRL 4

    • Successfully moved the data analytics study from initial concept (TRL 1) to prototype and early-stage development (TRL 4).

    2. Comprehensive Datasets

    • Produced several datasets that provide insights into factors affecting sea vessel inspection delays.

    3. Practical Insights

    • Identified key metrics that significantly influence inspection times, helping ports optimize operations.

    4. Advanced Data Analysis and Visualization

    • Leveraged Python for robust statistical analysis and created an interactive dashboard for data visualization.

    5. Effective Team Collaboration

    • Achieved cohesive results through intensive collaboration and structured research methodologies.

Project Details

  • Project Structure

    • Teams: 1 team consisting of 3 MBA students.

    • Collaboration: Intensive, focused study with comprehensive analysis.

    • Duration: 4 months

Research Areas

Impact of Metrics on Inspection Delays

  1. Geographic Location: Analyzed the influence of different geographic areas on inspection times.

  2. Port Characteristics: Studied how specific port attributes affect inspection delays.

  3. Environmental Factors: Assessed the impact of temperature and time of year.

  4. Cargo and Ship Type: Evaluated how different types of cargo and ships influence inspection durations.

Trade Partner Analysis

  1. Origin and Destination Pairs: Studied how trade relationships between countries impact inspection delays.

Technical Implementation

  1. Data Analysis: Used Python for statistical analysis and data manipulation.

  2. Dataset Creation: Generated several datasets to support findings and recommendations.

  3. Dashboard Development: Created an interactive dashboard to visualize and analyze the datasets.

Challenges

Fast-Paced Environment

  • Data Complexity

    • Managing and analyzing complex datasets with multiple influencing factors.

    Coordination and Time Management

    • Ensuring effective collaboration and timely completion of the study within the 4-month timeframe.

Solutions

  • Structured Data Analysis Framework

    • Developed a clear framework for data collection, analysis, and interpretation.

    • Utilized advanced Python libraries for efficient data processing and statistical analysis.

  • Regular Team Check-ins

    • Conducted regular meetings to review progress, address challenges, and ensure alignment.

Benefits

Enhanced Understanding of Inspection Delays

  • Provided ports with actionable insights to reduce inspection times and improve efficiency.

Advanced Analytical and Visualization Skills

  • Equipped students with practical experience in data analytics, Python coding, and dashboard creation.

Informed Decision-Making

  • Enabled ports and trade partners to make data-driven decisions to optimize inspection processes.

Facts & Figures

  • Project Duration: 4 months

  • Team Size: 3 MBA students

  • Technology Readiness Level: Progressed from TRL 1 to TRL 4

Contact Information

For more information about the UAV Feasibility Study Project, please contact: