The client is a leading packaging and logistics provider operating more than 600 facilities and managing over 100,000 ground vehicles across North America. With millions of packages moving through its network each year, accurate shipment forecasting and route planning are critical to maintaining delivery speed, operational efficiency, and customer satisfaction.
However, the organization struggled to predict package volumes across different shipment categories. Existing forecasting methods lacked accuracy and consistency, limiting the ability to plan routes, allocate resources, and anticipate demand fluctuations. As shipment volumes and operational complexity increased, the absence of a standardized process for developing and managing machine learning models created additional challenges for collaboration and deployment.
The client needed a scalable forecasting solution to improve delivery planning and operational decision-making.
Ascendion partnered with the client to design and implement a scalable machine learning and forecasting platform that improves shipment prediction accuracy and supports better route planning.
Advanced Forecasting Models:
Using Azure Machine Learning and Databricks, we developed forecasting models capable of predicting shipment volumes across categories. Multiple algorithms—including ARIMA, Prophet, Exponential Smoothing, and XGBoost—were evaluated within a unified framework to identify the most accurate models for different data scenarios.
Scalable Model Management and Automation:
We implemented MLflow-based MLOps capabilities to track experiments, monitor model performance, and manage model lifecycle workflows. Automated pipelines and continuous integration processes streamlined model development, deployment, and updates. This framework allows teams to continuously refine forecasting models using new operational inputs such as weather, traffic patterns, and shipment trends.
Tech Stack:
Python, Azure ML, Azure MLOps, Databricks
The AI-driven forecasting platform significantly improved planning accuracy, operational efficiency, and delivery performance across the logistics network.
At BMS, we wanted our consultants to focus on the possible, freeing up time lost to reading endless CV’s that could instead be used to really engage with people who are looking for that first step in their sales career. The solution we adopted found the right balance between machine efficiency and human empathy.
– Graham Halewood,
Chief Operations Officer
Case studies
Case studies
Case studies
Case studies
Case studies
Case studies
Case studies
Case studies
Thanks for submitting the form.
Your interest has been captured.