https://github.com/nhoiyee/finance-and-supply-chain-analysis

The project involves performing financial and supply chain analytics on Atliq Hardwares data. The primary objective is to answer various analytical questions related to sales, gross sales, market, product, customers, and supply chain forecasting.
https://github.com/nhoiyee/finance-and-supply-chain-analysis

analysis excel mysql-workbench sql

Last synced: 10 days ago
JSON representation

The project involves performing financial and supply chain analytics on Atliq Hardwares data. The primary objective is to answer various analytical questions related to sales, gross sales, market, product, customers, and supply chain forecasting.

• Host: GitHub
• URL: https://github.com/nhoiyee/finance-and-supply-chain-analysis
• Owner: nhoiyee
• Created: 2024-01-01T04:55:27.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-01-02T07:00:52.000Z (11 months ago)
• Last Synced: 2024-01-03T08:10:53.567Z (11 months ago)
• Topics: analysis, excel, mysql-workbench, sql
• Homepage:
• Size: 1.06 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Finance and Supply-Chain Analytics

This project involves performing financial and supply chain analytics on Atliq hardwares data. The primary objective is to answer various analytical questions related to sales, gross sales, market, product, customers, and supply chain forecasting.

 

## Project Overview

In this endeavor, I took a closer look at Atliq Hardware, a fictional leader in computer hardware. The challenge was to empower the management with valuable insights using MySQL.

## Supply-Chain concepts used

- Forecast value: The predicted value of demand for products.

- Net error: The difference between forecast value and actual value.

- Absolute Net error: the positive value of net error even if the net error is negative

- Absolute Net error percent: Total absolute net error divided by total forecast value

- Forecast accuracy percent: 100 – absolute net error percent

  

## Skills learnt

- DDL and DML operations

- Implementing Data Integrity in Entity-Relationship Diagrams

- Handling various data types: numeric, string, date/time, JSON, and Spatial

- Using Stored Procedures and User-defined functions

- Performance optimization by reducing the overall query execution time

- Using CTEs (Common Table Expressions), Temporary tables, and Database Views.

- Windows functions (OVER, PARTITION BY clause, ROW_NUMBER, RANK, DENSE_RANK).

- Using Triggers, Indexes

- Grasping the significance of Primary and Foreign Keys