Welcome to the Company Data Analysis repository! This project aims to provide valuable insights and data-driven decisions by performing in-depth analysis on our company's datasets. Whether you are a team member, data analyst, or stakeholder, this repository will help you explore and understand various aspects of this dataset.
In a data-driven world, the ability to harness the power of data has become essential for businesses. This repository serves as a central hub for our company's data analysis efforts. From sales performance and customer behavior to inventory management and financial trends, we delve into various datasets to extract valuable information and answer critical business questions.
Key objectives of this project include:
- Gaining actionable insights to optimize business processes and strategies.
- Visualizing data in an intuitive manner to facilitate better understanding and communication.
- Maximizing revenue for the company by inspecting revenue, costs and more.
The success of data analysis heavily relies on the quality and diversity of data sources. Our repository incorporates a wide range of datasets, including but not limited to:
- Sales data: Exploring revenue trends and sales performance.
- Inventory data: Analyzing stock levels and supply chain efficiency.
- Carrier data: Analyzing transportation types and costs
I continuously update this repository with the latest analysis and reports. Each analysis is thoroughly documented and presented using Python, Pandas, and interactive visualizations created with Plotly.
In this repo, we'll use Python, Pandas and Plotly libraries to analyze some data from a dataset I've taken from a website. We are using VSCode Interactive Window to run codes. Here's how you can use Interactive Window.
First we need to import all libraries, read the data from .csv file and print it. Dataset is taken from statso.io
import pandas as pd
import plotly.express as px
import plotly.io as pio
import plotly.graph_objects as go
pio.templates.default = "plotly_white"
data = pd.read_csv("supply_chain_data.csv")
print(data.head())
Now we imported the libraries and read the file, we can start analyzing and visualizing it.
sales_data = data.groupby('Product type')['Number of products sold'].sum().reset_index()
pie_chart = px.pie(sales_data, values='Number of products sold', names='Product type',
title='Sales by Product Type',
hover_data=['Number of products sold'],
hole=0.5,
color_discrete_sequence=px.colors.qualitative.Pastel)
pie_chart.update_traces(textposition='inside', textinfo='percent+label')
pie_chart.show()
data.groupby('Product type'): This line groups the DataFrame data by the column 'Product type'. It means that the data will be grouped based on the unique values in the 'Product type' column.
['Number of products sold'].sum(): After grouping, this line calculates the sum of the 'Number of products sold' for each group. It adds up the values of the 'Number of products sold' column within each group.
.reset_index(): This resets the index of the resulting DataFrame after the groupby and sum operations. As a result, the 'Product type' column becomes part of the DataFrame again, and a new DataFrame called sales_data is created with two columns: 'Product type' and 'Number of products sold'.
Above, we can see the product types and how much sale does it have. Next, we'll check the revenue by each carrier.
total_revenue = data.groupby('Shipping carriers')['Revenue generated'].sum().reset_index()
fig = go.Figure()
fig.add_trace(go.Bar(x=total_revenue['Shipping carriers'],
y=total_revenue['Revenue generated']))
fig.update_layout(title = 'Total revenue by each shipping carrier', xaxis_title = 'Shipping carrier', yaxis_title = 'Revenue')
fig.show()
We can see that Carrier B makes the most revenue from all the carriers.
Now let's check average lead time and average manufacturing costs that our company have.
avg_lead_time = data.groupby('Product type')['Lead time'].mean().reset_index()
avg_manufacturing_costs = data.groupby('Product type')['Manufacturing costs'].mean().reset_index()
result = pd.merge(avg_lead_time, avg_manufacturing_costs, on='Product type')
result.rename(columns={'Lead time': 'Average Lead Time', 'Manufacturing costs': 'Average Manufacturing Costs'}, inplace=True)
print(result)
data.groupby('Product type'): Similar to the previous code, this line groups the DataFrame data by the column 'Product type'.
['Lead time'].mean(): After grouping, this line calculates the mean (average) of the 'Lead time' for each product type. It computes the average value of the 'Lead time' column within each group.
pd.merge: This function is used to merge the two DataFrames avg_lead_time and avg_manufacturing_costs based on the common column 'Product type'. It performs an inner join by default, meaning only the product types that exist in both DataFrames will be included in the result.
After running this code, we'll get this output:
| Produt Type | Average Lead Time | Average Manufacturing Costs | |
|---|---|---|---|
| 0 | cosmetics | 13.538462 | 43.052740 |
| 1 | haircare | 18.705882 | 48.457993 |
| 2 | skincare | 18.000000 | 48.993157 |
We can make this analysis from this table:
-
Cosmetics have the shortest average lead time (13.54 units) among the three product types, indicating a relatively quick production process.
-
Haircare has the longest average lead time (18.71 units) among the three product types, suggesting a longer time to produce compared to cosmetics and skincare.
-
Cosmetics have the lowest average manufacturing costs (43.05 units) among the three product types.
-
Skincare has the highest average manufacturing costs (48.99 units) among the three product types, indicating relatively higher expenses in production compared to cosmetics and haircare.
In the columns, you probably saw a column named 'SKU'. SKU stands for "Stock Keeping Unit." It is a unique identifier or code assigned to a specific product or item in a company's inventory or stock. The SKU is used to track and manage individual products separately, making it easier for businesses to monitor their inventory, sales, and restocking processes. Now let's check our company's revenue generated by each SKU.
revenue_chart = px.line(data, x = 'SKU', y = 'Revenue generated', title = 'Revenue generated by each SKU')
revenue_chart.show()
Our stock levels by SKU:
stock_chart = px.line(data, x='SKU',
y='Stock levels',
title='Stock Levels by SKU')
stock_chart.show()
Everything looks good for now but our company has costs for sure. Let's see our shipping costs and see if we can reduce them or how to reduce them.
cost_chart = px.bar(data, x = 'Shipping carriers', y = 'Costs', title = 'Shipping costs')
cost_chart.show()
So, Carrier B costs most from all. But also makes profit the most. It might be a challenging decision to make. If we had more data, maybe we could tell something from them.
Let's see what percentage of our transports are made with which transport vehicle.
transportation_chart = px.pie(data, values = 'Costs', names = 'Transportation modes', title = 'Costs by each transportation type', hole = 0.5, color_discrete_sequence=px.colors.sequential.RdBu)
transportation_chart.show()
And finally, we may check and analyze the cost by routes for each product type. We can use scatter plot for this. Here's how to implement scatter plot:
fig = px.scatter(data, x='Product type', y='Costs', color='Routes', title='Cost - Route and Product Types')
fig.show()
This is the chart:
