sanasayyed2001 / Enhancing-Supply-Chain-Efficiency-through-Data-Optimization

This project leverages advanced SQL queries to analyze and optimize an e-commerce platform's supply chain operations. It addresses key challenges like transaction analysis, customer segmentation, shipping efficiency, and order cancellation trends, providing actionable insights for enhancing operational performance and customer satisfaction.

Enhancing Supply Chain Efficiency through Data Optimization

Project Overview

This project focuses on solving various business problems related to supply chain management using advanced SQL queries. The dataset used in this project represents an e-commerce platform's supply chain operations, including transactions, orders, customer details, and shipment data.

Problem Statement

The primary goal of this project is to leverage data analytics to optimize e-commerce operations and enhance the customer experience. By addressing key business questions through comprehensive data analysis, we aim to overcome challenges such as fragmented data, real-time decision-making needs, demand forecasting inaccuracies, and logistics inefficiencies.

Objectives

• Transaction Analysis: Identify and analyze the most common types of transactions, ensuring fraudulent cases and specific city exclusions to get an accurate picture.
• Customer Segmentation: Identify the top customers based on completed orders to understand customer behavior, geographical trends, and sales contributions.
• Shipping and Department Efficiency: Analyze shipping modes and departmental preferences to optimize logistics, inventory management, and customer satisfaction.
• Shipment Compliance: Evaluate shipment delays against scheduled shipping days to identify operational inefficiencies and improve delivery processes.
• Order Cancellation Trends: Calculate the order cancellation percentage by state to pinpoint areas with potential issues like fraud risk, operational inefficiencies, or customer dissatisfaction.

Dataset

The dataset includes information about:

• Transactions: Details on various types of transactions (e.g., DEBIT, TRANSFER, PAYMENT, CASH).
• Orders: Order status, shipment details, and customer information.
• Customers: Customer IDs, names, cities, and states.
• Shipments: Shipment modes, scheduled vs. actual shipping days, and order cancellation reasons.

Project Structure

• supply_db.sql: Contains the SQL schema and data used in this analysis.
• Supply_chain_base.sql: Includes the SQL queries used to solve specific business problems.
• Supply db Data Analysis SANA.pptx: Presentation detailing the project's findings, insights, and business recommendations.

Business Problems Addressed

• Transaction Analysis: Identify the most common types of transactions, excluding certain cities and suspected fraud cases.
• Customer Insights: List the top customers based on completed orders, including details like customer ID, name, city, state, number of orders, and total sales.
• Shipping and Department Analysis: Analyze order counts by shipping mode and department, focusing on departments with at least 40 completed orders.
• Shipment Compliance: Create a new field for shipment compliance based on scheduled and actual shipping days, and analyze delays across different shipping modes.
• Order Cancellation Analysis: Calculate and rank the order cancellation percentage by state, identifying areas with potential issues.

Key Insights

• Popular Transaction Types: DEBIT transactions are the most common, followed by TRANSFER and PAYMENT.
• Top Customers: Key customers have been identified, with significant contributions to total sales.
• Shipping Preferences: Standard Class is the most preferred shipping method across various departments.
• Shipment Delays: The project highlights the shipping modes with the highest delays, providing opportunities for operational improvements.
• Cancellation Trends: States with higher cancellation rates have been identified, enabling targeted interventions to reduce cancellations.

Conclusion

Addressing supply chain challenges through data-driven strategies is crucial for operational excellence and customer satisfaction. This project showcases how SQL can be leveraged to analyze complex datasets, providing actionable insights to optimize e-commerce supply chain operations.

Getting Started

To explore the SQL queries and insights:

1. Clone the repository:

git clone https://github.com/your-username/supply-chain-data-analysis.git

2. Import the SQL schema:

source supply_db.sql;

3. Run the queries in Supply_chain_base.sql using your preferred SQL environment.

Technologies Used

• SQL: MySQL for database management and querying.
• Presentation: Insights and conclusions are summarized in a PowerPoint presentation.