Скачать или смотреть Supply Chain Analysis with Python 52 Transportation Problem — Seaborn Visualizations

Hi Everyone !

In this project, we simulated a small transportation problem using NumPy and pandas, and then visualized it with Seaborn to understand the relationships between suppliers, customers, costs, and allocations.

Step-by-step summary:

Data Creation:
We generated synthetic data for three suppliers and four customers.
Each supplier had a given supply capacity, and each customer had a demand requirement.
Using NumPy, we also created a cost matrix showing how much it costs to ship one unit from each supplier to each customer.

Feasible Allocation:
We built a simple greedy allocation algorithm — for each supplier, we shipped goods to the cheapest available customers until all supply and demand were satisfied.
This created an allocation matrix, showing how many units were shipped along each lane.

Visualization:
Using Seaborn and Matplotlib, we explored the data visually with:
Bar charts for supply and demand comparison.
Heatmaps for unit costs and actual allocation flows.
Stacked bar charts showing outbound and inbound mixes (who ships to whom).
Scatter plot visualizing the relationship between cost and shipped volume (bigger bubbles = more shipped units).

Insights:

The cost heatmap revealed expensive and cheap shipping routes.
The allocation heatmap showed which routes were actually used.
The scatter plot helped evaluate whether we were favoring low-cost lanes.

Such visuals help in understanding trade-offs before implementing optimization algorithms like Linear Programming or Transportation Simplex.

Why it’s useful:

This approach is ideal for transportation planners, supply chain analysts, and logistics engineers who want to quickly understand cost structures, supply-demand balances, and routing efficiency — even before running optimization models.

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