I have an assignment where I need to write a Capstone Project Proposal.

The course is COMM163 - Business Decision Models.

Here is the rubric:

1. Understanding the organization - Goals, current DM process. This should help you identify the areas where analytics can add the most value.

2. Problem Definition - identify specific decision problems that can be addressed using analytics

3. Identify data sources - determine the types of data that are available and relevant to the problem. This could include internal data, external data, or public data sources. Be prepared to recommend additional data collection requirements.

4. Determine the analytics approach - which models from across COMM161 (Introduction to mathematical analysis), COMM162 (Managerial Statistics), and COMM163 (Business Decision Models) are most appropriate to help analyze the problem. This could involve descriptive analytics, predictive analytics, or descriptive analytics.

5. Outline of plans for steps 5 to 8.

Here is a list of the main topics we have covered in class so far:

- Introduction to Quantitative Analysis and probability review

- Decision Analysis

- Inventory Control Models

- Linear Programming Models and Sensitivity Analysis

MY QUESTION FOR YOU:

I have copy and pasted my proposal below, however I am very lost with this assignment and I am aware it does not effectively meet all of the rubric deliverables.

Please state changes that need to be made to ensure I achieve a high degree of effectiveness in each category of the rubric.

My topic for a Capstone Project Proposal is Queens Hyperloop Design Team but I want to focus more specifically on their Hyperloop Pod Designs Project.

***After the changes have been stated, please rewrite an improved proposal with the revised changes using information from this website: https://queenshyperloop.ca/our-pods

Please be very specific with stating the changes that need to be made (for example, rewrite exactly what I would put instead of my original)

NOTE: I do not intend to claim this work as my own, it is a simply a good exemplar to use.

Here is what I have so far:

Capstone Project Proposal for Queen's Hyperloop Design Team (QHDT) - Hyperloop Pod Designs

Understanding the Organization - Goals and Current Decision-Making Process:

Queen's Hyperloop Design Team (QHDT) is a dynamic and multidisciplinary student organization at Queen's University, consisting of over 100 talented students from various faculties, including engineering, science, and business. Our overarching goal is to lead the way in Hyperloop technology innovation, with a commitment to changing the future of transportation.

Goals: Our primary objective is to design and build Hyperloop pods that push the boundaries of speed, energy efficiency, and passenger safety. In doing so, we aim to contribute to the development of sustainable and high-speed transportation systems.

Current Decision-Making Process: Our current decision-making process involves iterative design cycles, prototyping, and testing, which generate valuable data and insights. However, to further enhance our decision-making capabilities, we recognize the need to integrate analytical methodologies from COMM161, COMM162, and COMM163.

Problem Definition - Specific Decision Problems:

The capstone project will address the following specific decision problems, drawing on concepts from relevant courses:

Optimizing Pod Aerodynamics (Relevant Courses: COMM161 - Introduction to Mathematical Analysis):

• Problem: Improve the pod's aerodynamic performance to reduce drag and increase speed.
• Course Concepts: Utilize mathematical analysis techniques to model airflow and optimize the pod's shape and components for minimal air resistance.

Enhancing Energy Efficiency (Relevant Courses: COMM162 - Managerial Statistics):

• Problem: Optimize the pod's energy consumption for increased efficiency and reduced operational costs.
• Course Concepts: Apply statistical methods to analyze heat transfer and energy conversion processes, identifying areas for improvement.

Material Selection for Pod Components (Relevant Courses: COMM163 - Business Decision Models):

• Problem: Select the most suitable materials for various pod components, considering factors such as strength, weight, and cost.
• Course Concepts: Employ decision modeling techniques to evaluate material options and make data-driven choices that maximize performance while staying within budget constraints.

Identify Data Sources:

To address these decision problems effectively, we will leverage both internal and external data sources:

Internal Data:

• Historical Pod Design Data: Utilize data from previous pod designs, including performance metrics, design specifications, and test results (accessible on the QHDT website: https://queenshyperloop.ca/our-pods).

External Data:

• Industry Research: Incorporate insights from industry research and case studies on Hyperloop pod design, materials, and best practices.

Additional Data Collection: Depending on project needs, we may consider conducting wind tunnel tests, collaborating with industry partners for real-world performance data, or collecting additional relevant data sources to ensure comprehensive analysis.

Determine the Analytics Approach:

Our analytics approach will integrate key methodologies from the relevant courses:

Computational Fluid Dynamics (CFD) (COMM161):

• Approach: Utilize CFD simulations to model and analyze airflow around the pod for aerodynamic optimization.
• Course Concepts: Apply mathematical analysis concepts to model fluid flow and optimize the pod's shape and components to minimize air resistance.

Statistical Analysis (COMM162):

• Approach: Create statistical models to analyze heat transfer and energy conversion processes within the pod.
• Course Concepts: Apply statistical methods to identify significant factors affecting energy consumption and efficiency, facilitating data-driven improvements.

Decision Modeling (COMM163):

• Approach: Develop decision models to evaluate material options for pod components, considering cost-effectiveness and performance.
• Course Concepts: Employ decision modeling techniques, such as decision trees and cost-benefit analyses, to make informed material selections.

Outline of Plans for Steps 5 to 8:

Project Timeline:

Project Planning (Weeks 1-2): Define project scope, objectives, and team roles, emphasizing the importance of aligning analytics with the organization's goals and decision-making processes.

Data Collection and Preparation (Weeks 3-4): Gather historical pod design data, validate, and clean the dataset for analysis, ensuring data integrity and accuracy.

Analytics Model Development (Weeks 5-7): Develop CFD models for aerodynamic analysis, statistical models for energy efficiency, and decision models for material selection. Continuously assess model assumptions and adapt based on data analysis.

Data Analysis and Interpretation (Weeks 8-10): Apply developed models to historical data, interpret results, and identify design improvements. Conduct sensitivity analyses to understand model robustness and quantify the impact of design changes.

Recommendations and Reporting (Weeks 11-12): Summarize findings, formulate design recommendations, and prepare comprehensive reports outlining optimization strategies. Communicate findings effectively using visual aids and clear explanations.

Project Presentation (Week 13): Present project outcomes, insights, and design recommendations to QHDT stakeholders, emphasizing data-driven strategies for enhanced pod performance. Address questions and concerns effectively.