Building a Quick Commerce Inventory Optimization Dashboard with Dash, Plotly, and Pandas

Introduction

In this blog, we’ll build a data-driven dashboard to visualize product demand trends and suggest optimal inventory plans for quick commerce operations using historical order data. The project leverages Python's Dash framework, Plotly for interactive visualizations, and Pandas for data processing.

By the end, you’ll learn how to:

  • Load and preprocess demand and inventory data

  • Create interactive dropdown filters

  • Visualize historical order trends and optimized stock levels

  • Build and run a full-fledged web dashboard with Dash.

Prerequisites

Before diving in, ensure you have the following:

  • Basic knowledge of Python and Pandas

  • Familiarity with Dash and Plotly

  • Installed packages: dash, pandas, plotly

Step 2: Load and Inspect the Data


Step 3: Initialize the Dash App







Step 4: Define the Layout





















Step 5: Build the Callback Function









Step 6: Filter & Aggregate the Data

📈 Demand Trend Chart


📊 Inventory Plan Chart











Step 7: Run the App








Summary


In the fast-paced world of quick commerce, where customer satisfaction hinges on rapid delivery and minimal stockouts, inventory planning plays a pivotal role. To address this need, I developed an interactive dashboard using Python’s Dash framework, powered by Plotly for visualizations and Pandas for data handling. This dashboard serves as a decision-support tool, helping planners and operations teams monitor historical order trends and plan optimal weekly inventory at a granular, pincode level.

The project began by working with two datasets: one capturing daily order volumes across multiple products and pincodes (orders_data.csv), and the other containing pre-calculated optimal weekly stock levels (inventory_plan.csv). These datasets were integrated and analyzed to visualize both historical demand behavior and forward-looking stock requirements.

The dashboard interface is kept minimal yet effective. A dropdown menu allows users to select a specific pincode, and based on that selection, two charts are dynamically updated. The first chart is a multi-line trend graph that illustrates how product-level demand has changed over time. This provides clarity on seasonal patterns, product performance, or sudden demand spikes. The second is a bar chart showing the recommended weekly inventory plan for the selected pincode — helping stakeholders make informed stocking decisions without manually sifting through data.

The interactivity is handled via Dash callbacks, where user input (i.e., selected pincode) triggers data filtering, aggregation, and figure generation using Plotly Express. The entire app runs locally in a secure and isolated environment, suitable for individual analysis or prototyping before deployment to a shared setting.

Ultimately, this dashboard project showcases the power of combining data science tools with web frameworks to transform raw data into actionable insights. While the current version is focused on visualization, it opens up pathways for future enhancements like forecasting, anomaly detection, or real-time alerts — all within the same user-friendly interface.


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