Logistics Budgeting & Planning

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:

Logistics

Techniques:

ARIMA, FB Prophet, Random Forest

Technology Stack:

Power BI, Azure Databricks, Synapse Analytics

Business Benefits:

Improved cost allocation to modes of transport

Improved negotiations with carriers on rates

Predictive insights

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Overview

TrueNorth partnered with a leading logistics provider to optimize freight movement costs and planning efficiency.

The objective was to improve budgeting accuracy across multiple transport modes (including air, sea, and land) while identifying cost-saving opportunities and enhancing operational predictability.

The Challenge

The client’s logistics network involved complex, multi-modal transport routes with multiple carriers, each offering different pricing structures and reliability metrics.

Their existing planning process lacked accurate forecasting, resulting in:

Inefficient cost allocation across transport modes

Unreliable rate negotiation data

Delays caused by reactive logistics planning

To address this, the client needed a data-driven framework capable of predicting transportation costs, optimizing budgets, and supporting proactive carrier negotiations.

Our Approach

TrueNorth developed a predictive planning and budgeting model that used machine learning to simulate transport costs, optimize carrier selection, and improve overall logistics forecasting.

Real-Time Data Tracking

Collected and unified cost, distance, carrier performance, and operational data across all transport modes - including air, road, and sea freight.

Predictive Cost Forecasting

Applied statistical and AI techniques such as ARIMA, FB Prophet, and Random Forest to predict costs and performance trends for each carrier and mode.

Optimized Budgeting & Allocation

Integrated the predictive model into Power BI dashboards, allowing the client to simulate cost scenarios, identify inefficiencies, and plan the optimal cost-to-route ratio before execution.

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