Supply Chain Analysis Using Python
When you think of beauty products, the first thing that probably comes to mind is packaging, colors, and skincare routines—not supply chains. But behind every bottle of serum or palette of eyeshadow is a journey filled with data, logistics, and decisions that can make or break a brand’s success.
Recently, I explored a supply chain dataset from a fashion and beauty startup that sells makeup products. With the help of Python and some cool visualizations, I dove into the data to understand how the company operates—what sells, what gets stuck in inventory, what costs too much, and where improvements can be made.
Let’s walk through what I found!
First Things First: What’s in the Dataset?
This dataset was pretty detailed, and that’s what made the analysis exciting. It covered everything from:
Product info (type, price, stock, SKU)
Sales and revenue numbers
Customer demographics
Shipping carriers and transportation modes
Supplier lead times and manufacturing costs
Even inspection results and defect rates!
In total, it had 100 products and 24 features—enough to get a real feel for the business behind the beauty.
Import Libraries in Jup
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"
Read Data
data = pd.read_csv("/users/abc/downloads/supply_chain_data.csv")
print(data.head())
Product type SKU Price Availability Number of products sold \ 0 haircare SKU0 69.808006 55 802 1 skincare SKU1 14.843523 95 736 2 haircare SKU2 11.319683 34 8 3 skincare SKU3 61.163343 68 83 4 skincare SKU4 4.805496 26 871 Revenue generated Customer demographics Stock levels Lead times \ 0 8661.996792 Non-binary 58 7 1 7460.900065 Female 53 30 2 9577.749626 Unknown 1 10 3 7766.836426 Non-binary 23 13 4 2686.505152 Non-binary 5 3 Order quantities ... Location Lead time Production volumes \ 0 96 ... Mumbai 29 215 1 37 ... Mumbai 23 517 2 88 ... Mumbai 12 971 3 59 ... Kolkata 24 937 4 56 ... Delhi 5 414 Manufacturing lead time Manufacturing costs Inspection results \ 0 29 46.279879 Pending 1 30 33.616769 Pending 2 27 30.688019 Pending 3 18 35.624741 Fail 4 3 92.065161 Fail Defect rates Transportation modes Routes Costs 0 0.226410 Road Route B 187.752075 1 4.854068 Road Route B 503.065579 2 4.580593 Air Route C 141.920282 3 4.746649 Rail Route A 254.776159 4 3.145580 Air Route A 923.440632 [5 rows x 24 columns]
print(data.describe())
Price Availability Number of products sold Revenue generated \ count 100.000000 100.000000 100.000000 100.000000 mean 49.462461 48.400000 460.990000 5776.048187 std 31.168193 30.743317 303.780074 2732.841744 min 1.699976 1.000000 8.000000 1061.618523 25% 19.597823 22.750000 184.250000 2812.847151 50% 51.239831 43.500000 392.500000 6006.352023 75% 77.198228 75.000000 704.250000 8253.976921 max 99.171329 100.000000 996.000000 9866.465458 Stock levels Lead times Order quantities Shipping times \ count 100.000000 100.000000 100.000000 100.000000 mean 47.770000 15.960000 49.220000 5.750000 std 31.369372 8.785801 26.784429 2.724283 min 0.000000 1.000000 1.000000 1.000000 25% 16.750000 8.000000 26.000000 3.750000 50% 47.500000 17.000000 52.000000 6.000000 75% 73.000000 24.000000 71.250000 8.000000 max 100.000000 30.000000 96.000000 10.000000 Shipping costs Lead time Production volumes \ count 100.000000 100.000000 100.000000 mean 5.548149 17.080000 567.840000 std 2.651376 8.846251 263.046861 min 1.013487 1.000000 104.000000 25% 3.540248 10.000000 352.000000 50% 5.320534 18.000000 568.500000 75% 7.601695 25.000000 797.000000 max 9.929816 30.000000 985.000000 Manufacturing lead time Manufacturing costs Defect rates Costs count 100.00000 100.000000 100.000000 100.000000 mean 14.77000 47.266693 2.277158 529.245782 std 8.91243 28.982841 1.461366 258.301696 min 1.00000 1.085069 0.018608 103.916248 25% 7.00000 22.983299 1.009650 318.778455 50% 14.00000 45.905622 2.141863 520.430444 75% 23.00000 68.621026 3.563995 763.078231 max 30.00000 99.466109 4.939255 997.413450
Analysis using python with visualization
1.Product type and Price
Analyzing the Supply Chain by looking at the relationship between the price of the products and the revenue generated by them:
fig = px.scatter(data, x='Price',
y='Revenue generated',
color='Product type',
hover_data=['Number of products sold'],
trendline="ols")
fig.show()
2.Sales by Product Type
The company derives more revenue from skincare products, and the higher the price of skincare products, the more revenue they generate. Now let’s have a look at the sales by product type:
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()
3.Total Revenue by Shipping 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 Shipping Carrier',
xaxis_title='Shipping Carrier',
yaxis_title='Revenue Generated')
fig.show()
4.Product type
Now let’s have a look at the Average lead time and Average Manufacturing Costs for all products of the company:
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)
Analyzing SKUs
There’s a column in the dataset as SKUs. You must have heard it for the very first time. So, SKU stands for Stock Keeping Units. They’re like special codes that help companies keep track of all the different things they have for sale. Imagine you have a large toy store with lots of toys. Each toy is different and has its name and price, but when you want to know how many you have left, you need a way to identify them. So you give each toy a unique code, like a secret number only the store knows. This secret number is called SKU.
5.Revenue generated by SKU
revenue_chart = px.line(data, x='SKU',
y='Revenue generated',
title='Revenue Generated by SKU')
revenue_chart.show()
6.Stock Levels by SKU
Stock levels refer to the number of products a store or business has in its inventory. Now let’s have a look at the stock levels of each SKU:
stock_chart = px.bar(data, x='SKU',
y='Stock levels',
title='Stock Levels by SKU')
stock_chart.show()
7.Order Quantity by SKU
order_quantity_chart = px.line(data, x='SKU',
y='Order quantities',
title='Order Quantity by SKU')
order_quantity_chart.show()
8.Shipping Costs by Carrier
shipping_cost_chart = px.bar(data, x='Shipping carriers',
y='Shipping costs',
title='Shipping Costs by Carrier')
shipping_cost_chart.show()
In one of the above visualizations, we discovered that Carrier B helps the company in more revenue. It is also the most costly Carrier among the three.
9.Cost Distribution by Transportation Mode
transportation_chart = px.pie(data,
values='Costs',
names='Transportation modes',
title='Cost Distribution by Transportation Mode',
hole=0.5,
color_discrete_sequence=px.colors.qualitative.Pastel)
transportation_chart.show()
Analyzing Defect Rate
The defect rate in the supply chain refers to the percentage of products that have something wrong or are found broken after shipping.
10.Average Defect Rates by Product Type
defect_rates_by_product = data.groupby('Product type')['Defect rates'].mean().reset_index()
fig = px.bar(defect_rates_by_product, x='Product type', y='Defect rates',
title='Average Defect Rates by Product Type')
fig.show()
Summary
Supply Chain Analysis means analyzing various components of a Supply Chain to understand how to improve the effectiveness of the Supply Chain to create more value for customers.Python helps Analyzing ,cleaning difficult data into simple Analysis.
