The analysis was conducted using the pandas Python library on a Supply Chain dataset, utilizing the Jupyter Notebook extension in Visual Studio Code. The analysis was designed to answer various questions related to the Supply Chain dataset.
The dataset was imported into Jupyter Notebook and preprocessed to ensure that it was clean and structured in a manner that allowed for easy manipulation using pandas.
The data was then analyzed using various pandas functions to gain insights into the different aspects of the supply chain. The answers to the diffrent questions were obtained by applying different pandas functions such as groupby, sum, mean, and count. The results were then visualized using various graphs and charts to provide a clearer picture of the data and insights obtained.
Overall, the analysis provided valuable insights into the Supply Chain dataset, helping to identify trends and patterns that can be used to optimize the supply chain processes and improve customer satisfaction.
The supply Chain is a network of production and logistics involved in producing and delivering goods to customers. Supply chain analysis is the process of examining the various components of a supply chain to identify areas where improvements can be made to enhance efficiency, reduce costs, and increase profitability. This involves analyzing the flow of goods and services, information, and finances between different entities in the supply chain, including suppliers, manufacturers, distributors, retailers, and customers.
The analysis typically involves evaluating factors such as lead times, inventory levels, transportation costs, supplier performance, customer demand patterns, and production capacity. By identifying areas where the supply chain can be optimized, organizations can implement changes to improve overall performance, reduce waste, and increase customer satisfaction.
Supply chain analysis is a critical process for businesses looking to remain competitive in today's global marketplace, where supply chain management has become increasingly complex due to factors such as globalization, technology advancements, and changing consumer behavior.
To analyze a company’s supply chain, we need data on the different stages of the supply chain, like data about sourcing, manufacturing, transportation, inventory management, sales and customer demographics. I found an ideal dataset for this task which includes data about the supply chain of a Fashion and Beauty startup. You can downlaod the dataset from this link; https://statso.io/supply-chain-analysis-case-study/
Descriptive Analysis to show the different statistics
Sales Analysis; to examine the number of products sold and the revenue generated to identify trends in sales.
Customer demographic analysis; , to identify target markets and tailor marketing campaigns accordingly.
Inventory analysis; : Analyzing stock levels, lead times, and order quantities to optimize inventory management.
Transportation analysis; : Analyzing transportation modes, routes, and costs to identify opportunities for cost savings and process improvements. Route optimization analysis: Analyzing transportation routes to identify opportunities for route optimization.
Sales trends analysis: This would involve examining the data over time to identify trends in sales, including seasonality, periodicity, or other patterns that can inform business strategy.
The distribution of customer gender in the database
The total sales per product type
Ability of the company to meet customer demand on time
Ability of the company to meer customer demand without running out of stock or back-orders
The defect rate of the products being sold
The quality ranking of the products
Total revenue generated by each product
Impact of price increase in the revenue
You can export the Power BI dashboard to PowerPoint using the following link: Export Dashboard to PowerPoint