Pause Motion

Advanced Analytics

There’s nothing simple about the work we do or the products we build. Everything we do is grounded in years of research and data science, ensuring every element of our work helps our customers work better. Our analytics efforts span brands and industries, providing valuable insights we can use to reflect, evaluate and continue evolving on our journey to move the world forward.

Advanced product modeling

There are dozens of unique products in the Oshkosh portfolio, many with multiple configuration options. Rather than allowing variety and customization to slow us down, we’re leveraging technology to turn it to our advantage. Our data science teams use historical data across all our brands to inform manufacturing processes, uncovering insights we can use to reduce operational complexity and streamline scheduling, ultimately resulting in shorter delivery times for our customers and improved efficiencies to our business.


At Pierce, every fire truck is unique, featuring thousands of product options for each vehicle. In order to continue delivering vehicles on-time and on-budget, our data scientists built a model that predicts each vehicle’s labor hours and helps the operations team assign trucks to dedicated assembly lanes based on complexity, reducing lead times without limiting customer choice. Today, we’re continuing to optimize the model and exploring new ways to deploy this solution to our other products and brands.

Logistics optimization

We’re proud of the products we make, but we take just as much pride in the behind-the-scenes processes that make production possible. Our global teams are developing and implementing advanced analytics models to improve operational decision making across the business, including logistics, inventory and supply chain management. These continued improvements result directly in better relationships with our suppliers, shorter lead times for our customers and a tremendous number of hours saved, freeing up our talented team members to apply their time and creativity to addressing new and exciting opportunities.


Behind each of our products is an enormously complex operations ecosystem that powers our ability to keep our promises to our customers. One critical element in that ecosystem is our proprietary Economic Order Quantity (EOQ) model. Created by our data science team, the EOQ constantly evaluates production schedules, inventory levels and supplier offerings to simplify decision making around how many parts to order and when. The EOQ solution brings greater visibility into our supply chain, helping us improve customer service and streamline inventory management across any product line. Today, we’re actively piloting this tool across multiple business areas, seeking new efficiencies we can ultimately pass on to our customers.

Turning data into insights

For years, Oshkosh team members have been collecting data to help us better understand our processes and where we can make improvements in our operations. Today, we’re turning that data into insights by predicting outcomes while leveraging data visualization and a team of data scientists to help us make better and faster business decisions.

Predicting part shortages before they occur

The global pandemic tested our supply chain in unprecedented ways. Like many manufacturers, our global supply chain is complex and can be volatile as we make vehicles and equipment that are highly fabricated and full of unique components. Even if a single part is delayed, it can cause a multitude of challenges. We had an opportunity to leverage advanced analytics to create better visibility into our supply chain and operations. Our supply chain and digital technology teams partnered to develop a predictive model that leverages machine learning algorithms to predict part shortages. Our ability to predict part shortages increased by over 80%, allowing us to optimize production schedules, look for alternative sourcing and better meet customer delivery commitments.