Business Intelligence & Analytics

Automated analytics module for parsing transaction logs, visualizing sales velocity, and optimizing inventory levels.

Python Data Visualization Business Analytics Automation

Project Overview

In modern finance and commerce, decision-making is driven by data. This project demonstrates the ability to build an end-to-end analytics pipeline: from parsing raw transaction logs to generating actionable business intelligence reports.

The module automates the calculation of sales velocity, identifies top-performing products, and triggers inventory reorder alerts based on statistical thresholds.

Key Concepts Implemented

Data Aggregation

Processed raw time-stamped transaction logs to aggregate sales data by product and time period, handling gaps and anomalies in data.

Sales Velocity & Reporting

Defined and calculated KPIs like "Sales Velocity" to measure product performance and generated automated visual reports for stakeholders.

Inventory Optimization

Implemented logic to categorize inventory needs (High/Medium/Low priority) based on historical sales trends and current stock levels.

Technical Implementation

The implementation uses Matplotlib for visualization and standard libraries for efficient data parsing, prioritizing clean, modular code structure.

#!/usr/bin/python3

"""
Sales Analytics: Generate plots showing item sales velocity
"""
import sys
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
from inventory_stock import load_sales_history, get_sales_summary
#i need to import for y=only integers
from matplotlib.ticker import MaxNLocator
def plot_sales_velocity(days=30):
    """Generate bar chart showing units sold per item"""
    summary = get_sales_summary(days)
    if not summary:
        print("No sales data available")
        return

    #Sort items by quantity sold (descending to show "winner items")
    sorted_items = sorted(summary.items(), key=lambda x: x[1], reverse=True)
    items = [x[0] for x in sorted_items]
    quantities = [x[1] for x in sorted_items]

    plt.figure(figsize=(10, 6))
    bars = plt.bar(items, quantities, color='steelblue')

    #Highlight top 3 sellers (Gold, Silver, Bronze)
    colors = ['gold', 'silver', '#CD7F32']
    for bar, color in zip(bars[:3], colors):
        bar.set_color(color)

    plt.xlabel('Product')
    plt.ylabel('Units Sold')
    plt.title(f'Sales Velocity - Last {days} Days')
    plt.xticks(rotation=45, ha='right')
    plt.gca().yaxis.set_major_locator(MaxNLocator(integer=True))
    plt.tight_layout()

    filename = f'sales_velocity_{days}days.png'
    plt.savefig(filename, dpi=300)
    print(f"✓ Plot saved as '{filename}'")
    plt.show()

def plot_daily_sales(days=30):
    """Generate line chart showing daily sales over time"""
    history = load_sales_history()
    if not history:
        print("No sales data available")
        return

    end_date = datetime.now()
    start_date=end_date-timedelta(days=days)

    #Initialize daily counts
    daily_counts={}
    curr = start_date
    while curr <= end_date:
        daily_counts[curr.date()] = 0
        curr += timedelta(days=1)

    #Aggregate sales
    for sale in history:
        sale_date = datetime.fromisoformat(sale["timestamp"]).date()
        if start_date.date() <= sale_date <= end_date.date():
            daily_counts[sale_date] += sum(sale["items"].values())

    #Plot
    dates = sorted(daily_counts.keys())
    counts = [daily_counts[d] for d in dates]
    plt.figure(figsize=(10, 6))
    plt.plot(dates, counts, marker='o', linewidth=2)
    plt.xlabel('Date')
    plt.ylabel('Items Sold')
    plt.title(f'Daily Sales - Last {days} Days')
    plt.xticks(rotation=45,ha='right')
    plt.gca().yaxis.set_major_locator(MaxNLocator(integer=True))

    plt.tight_layout()

    filename = f'daily_sales_{days}days.png'
    plt.savefig(filename, dpi=300)
    print(f"✓ Plot saved as '{filename}'")
    plt.show()

def generate_reorder_report():
    """Print report showing items to reorder"""
    summary = get_sales_summary(30)
    if not summary:
        print("No sales data found.")
        return

    print(f"\n {'='*60} \n 📊 REORDER REPORT (Last 30 Days) \n {'='*60}")
    print(f"{'Product':<40} {'Sold':>10} {'Priority':>10} \n {'-'*60}")

    for item, qty in sorted(summary.items(), key=lambda x: x[1], reverse=True):
        if qty > 100: priority="HIGH"
        elif qty > 50: priority="MEDIUM"
        else: priority ="LOW"
        
        print(f"{item:<40} {qty:>10} {priority:>10}")
    print("="*60)

if __name__ == '__main__':
    print("=== Warehouse Sales Analytics ===\n")
    days = int(sys.argv[1]) if len(sys.argv) > 1 else 30
    print(f"Analyzing last {days} days...\n")
    
    plot_sales_velocity(days)
    plot_daily_sales(days)
    generate_reorder_report()
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