Supply Chain Optimization – 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:

Supply Chain & Manufacturing

Techniques:

Prophet, Neural Networks, Monte Carlo Simulation, Optimization

Technology Stack:

Prophet, Neural Networks, Monte Carlo Simulation, Optimization Technology Stack: Power BI, Azure Databricks, Microsoft Fabric

Business Benefits:

10% Reduction in Inventory

30% Reduction in forecast error

$50M reduction in working capital

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Overview

TrueNorth partnered with a global supply chain enterprise to improve operational efficiency and financial performance.

The objective was to maximize free cash flow (FCF) across the value chain by optimizing supplier selection, inventory management, and demand forecasting through data-driven insights and AI automation.

The Challenge

The client faced persistent challenges in balancing inventory levels, supplier reliability, and cash flow efficiency.

Key issues included:

Excess stock tying up working capital

Inconsistent supplier lead times disrupting production

Forecasting errors leading to costly over- or under-ordering

To stay competitive, the organization needed an integrated model that could predict demand, evaluate supplier performance, and optimize inventory – all in one ecosystem.

Our Approach

TrueNorth developed an AI-driven supply chain optimization framework that combined supplier scoring, predictive forecasting, and dynamic inventory control to deliver measurable financial gains.

Supplier Intelligence & Scoring

Using AI algorithms, we evaluated suppliers based on:

Lead times and delivery reliability

Commercial value and sustainability metrics

Quality assurance and business continuity

This enabled procurement teams to select top-rated suppliers, reducing bottlenecks and improving cash flow consistency.

Inventory & Cash Flow Optimization

By integrating real-time inventory data across raw materials, work-in-progress, and finished goods, the system identified obsolete stock, streamlined order cycles, and improved liquidity.

Demand Forecasting & Scenario Modelling

Applied advanced forecasting techniques (Prophet, Neural Networks, and Monte Carlo Simulations) to model demand using sales, pricing, seasonality, and macroeconomic data.

These insights allowed leadership to make proactive supply and production decisions with confidence.

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