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IFB107TC Information Systems Management

Project Background

Enhancing E-Commerce Supply Chain Data Management

As a data analyst at an e-commerce company, you are tasked with addressing critical challenges in managing the company’s supply chain operations. With the rapid expansion of the business, the supply chain has grown increasingly complex, generating large volumes of data across various areas such as orders, inventory levels, customer details, shipping information, and promotional campaigns. The effective organization and analysis of this data are essential for optimizing supply chain performance and maintaining a competitive edge in the e-commerce market.

Currently, the company relies on unstructured data storage systems, which lead to inefficiencies in order processing, inventory optimization, and delivery management. These inefficiencies manifest as delayed shipments,  stockouts, and missed sales opportunities, ultimately  impacting  customer  satisfaction  and revenue. The lack of an integrated and scalable system hinders the company’s ability to address these issues efficiently.

To address these challenges, this project focuses on designing a relational database system that enhances the visibility and efficiency of supply chain operations. By integrating data across multiple supply chain components, including order management, inventory optimization, logistics, and customer interactions, the proposed database system will support better decision-making and operational performance. For example, the system will enable timely tracking of customer orders, accurate forecasting of inventory needs, and improved visibility into shipping delays by region.

Key Objectives of the Project

1.          Database Optimization: Develop a structured  elational  database to address  data inconsistencies and improve operational efficiency. Normalize the data to minimize redundancy and ensure data integrity.

2.          Data Insights: Use advanced SQL queries to extract actionable insights from the database, focusing on sales trends, customer behavior, and shipping performance.

3.          Visual Decision Support: Generate visualizations to provide senior management with clear and actionable insights for identifying operational bottlenecks and growth opportunities.

4.          Scalability  and Future-Proofing: Explore  advanced database techniques such as sharding, replication, and partitioning to ensure the system remains scalable and reliable during peak periods.

Target Audience

The primary audience for this report is the senior management team, including executives and decision-makers responsible for strategic planning and resource allocation. The report will focus on presenting technical solutions in a clear and professional manner, emphasizing their impact on business performance and scalability.

Overview of the Dataset

The dataset provided for this coursework contains comprehensive supply chain information, structured to simulate the operations of an e-commerce company. Below is a detailed description of the data fields included in the dataset:

Your Tasks

Tasks                                          Subtasks Description Marks

Task 1: Database Theory and Data Normalization

1. Database Theory

Explain the importance of relational databases in managing e-commerce systems. Discuss how relational databases support the organization, retrieval, and analysis of large-scale data, such as orders, products, customers, and shipping information. Highlight the advantages of using a structured database over unstructured data storage, focusing on consistency, integrity, and scalability.   5

2. Data Normalization

Using the provided sample data, document the process of normalizing the database to at least 3NF. Explain the steps taken to identify redundancies and resolve partial and transitive dependencies. For each normalization stage (1NF, 2NF, and 3NF), provide examples of how the schema is refined. Conclude by summarizing how normalization reduces data anomalies, ensures consistency, and improves database efficiency.   10

Task 2: ER Diagram and Justification

1. Create an ER Diagram

Design an ER diagram based on the normalized schema from Section 1. Identify entities, attributes, and relationships. Include primary keys, foreign key constraints, and define the cardinality of each relationship (e.g., one-to-many, many-to-many).   10

2. Justify ER Diagram Design

Explain the decisions made in designing the ER diagram. Discuss how the design ensures scalability, flexibility, and efficient query performance in an e-commerce context. Highlight how the structure minimizes redundancy and supports modular data organization.   5

Task 3: Advanced MySQL Tasks

1. Schema Implementation

Implement the normalized schema from Section 2 in MySQL Workbench. Include primary and foreign key definitions, ON DELETE CASCADE/ON UPDATE CASCADE constraints, and unique constraints where applicable.   10

2. Data Insertion

Use MySQL Workbench’s Import Wizard to load data from provided CSV files into the normalized tables. Ensure data consistency by matching columns, resolving data issues, and verifying successful import using SQL queries.

Note: If you encounter duplicates or inconsistencies during insertion:

• Preprocess the CSV file to remove duplicates.

• Use the Workbench UI to manually clean data where needed.   5

3. Advanced Querying

Write and execute three advanced queries to analyze the database. Each query must provide insights that are directly relevant to e-commerce decision-making. Demonstrate the use of advanced MySQL functionalities, such as joins, aggregations, subqueries, and window functions. Include explanations of how the query results can inform strategic decisions.   10

Task 4: Data Visualization and Interpretation

1. Python Integration

Demonstrate how to connect the MySQL database to Python using a library such as pymysql or SQLAlchemy. Write a Python script to showcase the connection and query execution process.   5

2. Visualization

Create two visualizations using the fetched data. Use SQL queries within Python to extract the exact data needed for each visualization, rather than importing the entire table. This ensures optimal performance and avoids unnecessary data processing   10

3. Impact Assessment

Discuss how the two visualizations impacts decision-making in e-commerce. Focus on identifying operational bottlenecks or sales opportunities and how visualization aids strategic planning.   5

Task 5: Advanced Database Applications

E-commerce systems often face challenges in handling large volumes of transactions, particularly during peak periods. Investigate the concepts of sharding, replication, and partitioning in modern database systems. Discuss how these techniques can be applied to ensure scalability, high availability, and fault tolerance in an e-commerce database. Provide examples of their implementation in popular database systems (e.g., MySQL, MongoDB, or PostgreSQL). Highlight the trade-offs associated with each technique and the potential challenges in implementation.   15

Report Quality

Assess the overall professionalism of the report. Criteria include clear structure, concise explanations, appropriate formatting, and use of references or citations where applicable. Ensure the report meets academic and professional standards.   10