Key results
reduction in inventory holding costs by identifying excess and aging SKUs through centralized visibility
increase in order fulfillment accuracy using real-time production and dispatch monitoring
accuracy in predicting client churn through behavior-based ML models
increase in revenue from targeted upselling and cross-selling strategies
About the Client
The client is a fast-growing label and packaging manufacturer with 15+ production facilities across the U.S. and Canada. They specialize in high-volume and custom label solutions for industries like retail, industrial goods, food, and logistics. As the business scaled, disconnected systems and ad-hoc reporting hindered their ability to forecast demand, optimize inventory, and retain customers—necessitating a modern data foundation.
Key Challenges
Disconnected data across ERP, CRM, and production systems limiting visibility
High carrying costs due to overstock and slow-moving label SKUs
No system to proactively flag high-risk customers likely to churn
Untapped potential for upselling and cross-selling to existing accounts
Our Approach
- Designed and implemented a cloud-native data warehouse on AWS using Amazon Redshift
- Consolidated data from ERP, CRM, production, and order systems into a single unified data model
- Built interactive Tableau dashboards for Sales, Operations, Inventory, Procurement, and Finance
- Developed Python-based predictive models for demand forecasting, churn scoring, and upsell recommendations
- Set up data pipelines and scheduled refreshes using AWS Glue and Lambda, ensuring near real-time reporting
Implemented Solution
Data Warehousing
- Unified Data Model integrating sales, production, customer, and inventory data
- Historical Data Retention for trend analysis and predictive modeling
- Optimized Query Layer for fast Tableau visualizations and automated refreshes
Tableau Powered Dashboards
- Production Dashboards – Efficiency, downtime, throughput, scrap rate
- Inventory & Procurement Insights – Stock aging, reorder points, vendor performance
- Sales & Finance Reporting – Customer trends, AR aging, margin analysis
- Marketing Funnel & Segmentation – Quote-to-win ratios, campaign performance, customer LTV
Predictive Analytics
- Demand Forecasting (Python + Redshift) – Seasonal SKU-level predictions for procurement and planning
- Churn Prediction Model – Identifies clients at risk of inactivity using behavioral data
- Cross-sell/Upsell Opportunity Model – Flags customers with high affinity for complementary SKUs
Technologies Used
