Jungheinrich Uses AI to Speed Up Battery Development for Electric Forklifts
Jungheinrich, a major manufacturer of industrial trucks, is using predictive AI models from Monolith to accelerate battery development for its next-generation electric forklifts. The collaboration lets engineers predict battery performance from early test data rather than relying solely on physical testing.
The company generates large amounts of test data during battery development. Jungheinrich now feeds this data into Monolith's AI software to train machine learning models that forecast key performance metrics. The approach lets engineers make technical decisions sooner and reduces the scope of costly physical test campaigns.
Why This Matters for Product Development
Battery technology is advancing quickly, and assessing how new batteries will perform in vehicle platforms has become complex. Data analysis tools that can extract insights from test results early in development cycles help teams move faster.
Research from McKinsey suggests AI-backed approaches can accelerate R&D processes in manufacturing by 20% to 80%. For Jungheinrich, the gains come from reducing the number of prototypes and test campaigns needed before moving forward with a design.
How the System Works
Engineers at Jungheinrich analyse battery test data and use Monolith's tools to build predictive models. The software trains these models on real-world test results to generate reliable forecasts for product performance metrics.
The platform also serves as a central hub where teams can access test data, model insights, and recommendations for future experiments across different development programs. This setup lets decisions happen earlier in the development cycle while cutting costs and testing effort.
The Business Case
As Jungheinrich expands its electric product lineup, the ability to evaluate battery technologies quickly and reliably becomes a competitive advantage. Dr Andreas Münz, head of hardware testing at Jungheinrich, said the collaboration helps the company "identify critical battery performance characteristics earlier and make smarter technical decisions that support the next generation of more efficient, sustainable products."
The pressure on manufacturers is clear: deliver more sustainable products while reducing development times and costs. For product development teams, AI for Product Development offers a direct path to faster decision-making backed by data rather than assumptions.
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