mithilkothari / supply-chain-management-dashboard-power-bi-

The dashboard delivers valuable insights into distribution logistics, enabling automotive companies to optimize their supply chain operations, reduce costs, and enhance overall efficiency.

Supply-Chain-Management-Dashboard-Power-Bi-

Project Title

A brief description of what this project

Supply Chain Management for Cars in Power BI provides a comprehensive overview of the entire supply chain process for automotive companies. This includes tracking the flow of materials, components, and finished products from suppliers to manufacturers, distributors, and ultimately to customers.

Using Python, MS SQL SERVER, and Power BI, companies can visualize key supply chain metrics such as inventory levels, production schedules, delivery performance, and supplier quality. They can also analyze historical data to identify trends, optimize operations, and make informed decisions to improve efficiency and reduce costs throughout the supply chain.

Supply Chain Management Dashboard

Dashboard Link : https://app.powerbi.com/groups/me/reports/8d42a5e4-9ebd-46c7-bf6a-a66f2648c422/ReportSection?experience=power-bi

Problem Statement

The automotive supply chain involves complex processes and a large number of suppliers, manufacturers, distributors and customers. Tracking and optimizing this supply chain is critical for automakers to reduce costs, improve efficiency and deliver vehicles to customers on time. However, many automakers struggle with siloed data, lack of visibility, and difficulty analyzing key supply chain metrics.

Flow of the project

1. Flow of the Project
2. Project Architecture
3. Project BRD or FRD Documents
4. Data Gathering
5. Data Cleaning / Data Transformation
6. Data Modeling
7. Mockup Preparation
8. DAX Functions (DAX Calculations)
9. Create Visuals (For Dashboard)
10. Add Navigation

Project Architecture

Summary for Steps followed

1. Data Gathering According to the BRD document, we have to connect the SQL Server database with Kaggle APIs and bring data automatically into database by writing some python code. After then from database to the Power BI Desktop using ‘Get Data’ option. First we will gather and manipulate data using Pandas library in python.

Firstly go to the Kaggle website dataset name, ‘Supply chain management for Car‘ you can access it here. For python code we are going to use Jupyter notebook. Install the below libraries first.

2. Data Cleaning Let’s transform data on Power Query Editor, we need to remove some unnecessary attributes from the table and just keep the important columns which we can use for visualization. Below is the list of columns we are gonna work on, and rest of it has removed.

3. Making Sure to See All Data:

   Usually, Power BI shows details for only a limited number of responses. We wanted to see details of entire dataset, so we made sure to look at the information provided.

4. Modeling In Power BI After creating the date master table, establish the relationship between these two tables using modelling. Go to the model view tab and create one-to-many relationship between date columns in both the tables. Also create a new measure for ‘Total Sales’ and keep all the new measures in separate table called ‘DAX Measures’.

6. Data Visualization

In our dashboard project, we are gonna create four different pages and implement navigation between all these four dashboard pages. The dashboard pages are Home, Order, Sales, and Customer View.

Report Snapshot (Power BI DESKTOP & Python Code)

Python Code

Power Bi Dashboard Reprot