Supply Chain Analysis using Power BI
Kalaiselvi Kumar
Data Analyst | SQL : Power BI : Tableau : Excel | Data Visualization
I had the pleasure of working on this project as part of Avery Smith's Data Career Jumpstart program. I have always been fascinated by the wide variety of products available in stores, whether they are food products, apparel, or cars. I used to check product labels. It is really surprising how products are manufactured in one part of the world and sold in another. This is done systematically using supply chain management. I have some experience in the supply chain industry myself, and I am eager to dig deeper into supply chain data to optimize the performance using Power BI.
Do you know? For many companies, supply chain and logistics are responsible for more than 10% of the overall cost. This also represents one of the greatest opportunities for both cost saving and faster, more reliable delivery to customers.
"Some of the important aspects of supply chain is the overall profit made and the customer satisfaction. Sometimes the delivery is delayed. I want to dig deep into the data and derive a meaningful insight about the different attributes that contribute to the success/failed/delayed delivery and the outcome of the business in terms of income."
Data set:
We are using real data for DataCo Global. The dataset was found here.
Power BI is my favorite business intelligence tool that helps you transform data from multiple sources into actionable insights. Its fascinating features, such as Power Query and DAX calculations, are used to turn data into a wide range of visuals, including pie charts, tree maps, monitor KPIs, combo charts, bar and column charts, maps, and many other options.
Data Cleaning and Transforming
There were columns with null values and a password column with special characters. These columns were removed as they cannot be used in our visuals. I changed the data type of the zip code from numbers to text. Using DAX calculations, I created a column for successful delivery.
Sucessful_delivery = IF(DataCoSupplyChainDataset[Late_delivery_risk]=0, 1,0)
I am interested in some of the following:
Some of the important KPI are ,
Here is the dashboard showing different visuals of our Analysis,
Let us get into our analysis,
Where does the company make the most money?
1.Profit and Sales across different geographical location:
2. Profit made in different departments:
3. Profits made across different customer segment.
How can we have less late deliveries?
Customer satisfaction is a crucial factor in determining the success of the supply chain industry. It focuses on product quality and timely delivery.
1. Shipping Delays Across Different Departments
The Fan Shop, followed by the Apparel Shop, is at risk of late delivery.
Products in high demand are more likely to experience delays in delivery.
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2. Does number of days for delivery cause delay?
3. Is there any delays because of shipping mode?
Actions to take:
Here are some of the actions to take to optimize the supply chain performance,
1. Profit and Sales across different geographical location
2. Understanding customer needs will contribute to increased profit.
a. Corporate Customers
b. Home Office Customers
3. Shipping delays across different departments
There is high rate of late delivery risk if the demand is high. Here in fan shop, apparel department the late delivery risk can be managed by,
4. Does number of days for delivery cause delay?
Delivery can be done effectively on time by,
5. Is there any delays because of shipping mode?
Late delivery risk is comparatively very high in second class and first class shipping mode. some of the steps can be taken to reduce the delays include,
Feel free to comment or connect with me on LinkedIn if you have questions or suggestions, . Keep me in mind if you have any opportunities in the analytics field, as I am always looking for connections. Thank you, Kalaiselvi Kumar.
Data Analyst @ DCJ & Data Evangelist ?? Voice for New Analysts & Data Beginners ?? Helping businesses win with data ?? Teaching, Scraping & Analyzing to Help You Fall in Love with Data
5 个月Your data breakdown is impressive. Well done!
Data Analyst * Excel | SQL | Tableau | Python | Power BI
7 个月This is great Kalaiselvi Kumar!