Supply Chain & Customer Churn Intelligence

A dual-purpose BI ecosystem designed to optimize logistics performance while minimizing customer attrition. By integrating AdventureWorks sales data with logistics metrics, this solution correlates shipping delays with profit margins.

Beyond standard reporting, it features a prescriptive decision engine driven by complex DAX logic that automatically flags underperforming products for delisting or vendor renegotiation.

View Code View Dashboard
  • Data Domain Supply Chain CRM / Churn
  • Technique Prescriptive Analytics Automated Logic
  • Tech Stack Power BI DAX Geospatial

Supply Chain & Customer Churn Intelligence Dashboard

Live Report

Case Study: Operations & Retention Engine

96.1%

On-Time Delivery Rate

Logic

Automated Vendor Audit

3.03%

Freight Cost % Sales

Managing a global retail operation requires balancing two competing forces: Customer Satisfaction (fast shipping, high retention) and Operational Efficiency (low freight costs, profitable margins). This project bridges the gap between "Sales Data" and "Logistics Performance" using the AdventureWorks dataset.

The Challenge

  • High Churn Visibility: Identifying why customers leave (churn rate: ~3.05%) requires analyzing demographics like occupation and marital status across both Reseller and Internet channels.
  • Freight Cost Leakage: With total freight costs hitting $1.16M, the business needed to visualize cost-per-order efficiency across global routes (specifically from the Florida hub).
  • Actionable Delays: Late deliveries (avg 15.65 days) were damaging relationships, but there was no system to correlate these delays with product profitability.

The Solution & Architecture

  • End-to-End Logistics Tracking: Built a geospatial route map analyzing freight costs from the distribution center to global destinations.
  • Churn Profiling: Developed distinct dashboards for B2B and B2C churn, identifying high-risk segments (e.g., "Skilled Manual" workers showed higher churn).
  • Prescriptive Analytics: Instead of just showing "Late Products," I engineered a dynamic measure (`DeliveryAction`) that recommends decisions.

Core Business Logic (DAX)

The following measure automates inventory decisions by correlating profitability with logistics performance.

DeliveryAction_Measure.dax 
DeliveryAction = 
IF(
    NOT(ISBLANK([AvgDays Late])),
    SWITCH(
        TRUE(),
        [Net Profit Margin] < 0.1 && [AvgDays Late] > 15, 
            "Low Margin and High LD : Delist the Product",
        
        [Net Profit Margin] >= 0.1 && [Net Profit Margin] <= 0.3 && [AvgDays Late] > 15, 
            "Avg Margin and Avg LD : Change Vendor",
        
        "Acceptable Late Shipment"
    ),
    BLANK()
)