Supply Portfolio Management – TrueNorth Group

Project Information

Client Name: HealthCare

Techniques: Woe LR

Technology: Power Bi
Azure Databricks
Synapse

Business Benefits Improved Patient Care
Increaed Productivity
Reduction in cost associated with unnecessary staff

Industry:

Retail / CPG

Techniques:

ARIMA, Association Analysis, FB Prophet

Technology Stack:

Power BI, Azure Databricks, Synapse Analytics

Business Benefits:

50% increase in promotional effectiveness

Better selection of promotional items

Reduced cannibalization

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Overview

A leading retail and consumer packaged goods (CPG) company partnered with TrueNorth to optimize its promotional portfolio and pricing strategy.

The objective was to improve discount effectiveness, manage product cannibalization, and enhance overall promotional ROI using advanced AI-driven demand modeling.

The Challenge

Promotions are essential for driving sales, but without accurate demand forecasting and portfolio insight, they can lead to:

Inefficient discounting that reduces profitability

Inventory imbalances after campaigns end (“post-promo hangover”)

Cannibalization, where one product’s promotion negatively impacts another

The client needed a way to quantify and predict promotional impact, ensuring every campaign contributed to growth rather than margin loss.

Our Approach

Demand & Price Sensitivity Modeling

Supplier Intelligence & Scoring

Analyzed historical sales data to measure:

Price sensitivity and elasticity

Seasonal sales patterns

Cross-product relationships and associated demand shifts

Portfolio Impact Assessment

Built predictive models using ARIMA, FB Prophet, and Association Analysis to identify which promotions increased total portfolio sales and which triggered cannibalization.

Promotional Strategy Optimization

Integrated insights into Power BI dashboards, allowing marketers and supply chain planners to simulate different promotional depths and forecast outcomes in real time.

Collect & Correlate

Collected and structured key datasets, including:

Demographics and population health data

Epidemiology and diagnosis statistics

Historical patient volumes and level-of-care requirements