Case Study: Kotahi achieves more accurate demand forecasting and optimizes container capacity with Microsoft Azure

Kotahi - Customer Case Study

Kotahi is a New Zealand supply‑chain collaborator that coordinates shipping container capacity for more than 40 exporters, importers, and logistics partners—nearly a third of which handle perishable goods. Facing growing volume and larger vessels, Kotahi relied on a manual Excel forecasting process that was only about 80% accurate and consumed four days of skilled staff time each month, limiting its ability to choose the right‑size ships, allocate empty containers, and meet carriers’ eight‑week planning windows.

Working with Microsoft and partner UXC Eclipse, Kotahi implemented an automated demand‑forecasting solution built on the Microsoft Cortana Intelligence Suite and Azure (Azure Machine Learning, Data Factory, SQL Database) with Power BI dashboards for self‑service reporting. The Azure ML time‑series model and automated data pipelines cut forecasting time from four days to about 30 minutes, raised accuracy to over 90%, and is expected to save more than US$1 million annually while giving decision makers timely, actionable insights.

Kotahi

Neville Richardson

Group IT Manager