DataCo Supply Chain & Sales Analysis

Project conducted: April 2025 (was not uploaded at the time)

Transforming raw operational and sales data into strategic insights to optimize supply chain performance and business decisions.

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Introduction

This project focuses on analyzing the operational performance and sales data of DataCo, an e-commerce FMCG company, to derive actionable insights for improving supply chain efficiency, customer satisfaction, and revenue growth.

Objective:

• Evaluate overall business performance
• Identify supply chain bottlenecks and delivery issues
• Analyze customer behavior and product sales patterns
• Generate recommendations for operational and marketing strategies

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Dataset

Source

The dataset was obtained from Kaggle:

• DataCo Smart Supply Chain Dataset

Alternatively, you can access DataCoSupplyChainDataset.txt via Google Drive (link in the project folder) to download the data.

Key Columns

• Order ID, Order Status, Order Item Quantity, Order Item Total, Order Profit Per Order
• Shipping Mode, Days for shipping (real), Days for shipment (scheduled), Delivery Status, Late_delivery_risk
• Customer City, Market, Category Name
• Order Region, Order State, shipping date (DateOrders)
• And other supply chain and financial performance fields

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Tools & Technologies

• Languages: Python
• Libraries: pandas, numpy, matplotlib, seaborn
• Visualization: Tableau, Power BI, Looker Studio, D3.js
• Database: Google BigQuery

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Project Workflow

1. Data Understanding:

   • Reviewed dataset structure, business context, and data dictionary.

2. Exploratory Data Analysis (EDA):

   • Analyzed sales performance by market, category, region.
   • Evaluated customer purchasing behavior and top-selling products.

3. Operational Performance Analysis:

   • Assessed delivery time performance against SLA.
   • Identified regions, shipping modes, and products with high late delivery rates.

4. Seasonality & Basket Analysis:

   • Investigated seasonal sales trends.
   • Conducted market basket analysis for product bundling opportunities.

5. Financial Performance Analysis:

   • Analyzed revenue, cost, and profit margins by customer segment and product.

6. Visualization Development:

   • Built interactive dashboards using:
     • Tableau: View Tableau Dashboard
     • Power BI: view via dataco_dashboard_pbi.pbix in this repository

7. Insight Generation & Recommendations:

   • Extracted key business insights and compiled actionable recommendations.
   • For detailed insights, read dataco_insights_report.pdf.

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Key Insights (Preview)

• Over 54% of orders were delivered late, highlighting systemic logistics issues.
• First Class shipping had ~95% late deliveries, requiring immediate SLA review.
• LATAM and Europe markets generated highest revenue but had high late shipment rates.
• Basket analysis revealed strong cross-selling opportunities within sports and fitness categories.
• Customer spending peaked at month-end, indicating optimal promotion periods.
• For full insight breakdowns and strategic recommendations, please refer to the dataco_insights_report.pdf included in this repository.

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Learning Outcomes

• Advanced data analysis and visualization skills across multiple BI tools.
• Ability to derive strategic supply chain insights from complex datasets.
• Experience designing interactive dashboards for business decision-making.
• Strengthened storytelling to communicate data-driven recommendations effectively.