Jungheinrich and Monolith Partner to Speed Battery Development With AI
Jungheinrich, a manufacturer of electric material handling equipment, is working with Monolith to apply machine learning to battery test data. The partnership aims to predict battery performance earlier in development cycles, reducing the need for extensive physical testing and accelerating time-to-market.
The collaboration addresses a real constraint in product development: as battery technologies evolve, evaluating their performance and integrating them into vehicle platforms demands more testing and analysis. Jungheinrich generates significant volumes of battery test data throughout the year.
How the Partnership Works
Monolith will ingest Jungheinrich's test datasets into its engineering tools, train predictive models on the data, and validate results with the engineering team. The models will forecast performance characteristics relevant to product development, allowing engineers to make decisions earlier without waiting for additional physical tests.
Engineers will access a centralised platform where they can view test data, model insights, and recommended next experiments across multiple development programmes. This setup lets teams prioritise which physical tests matter most, reducing prototype cycles and repetitive testing.
The Business Case
McKinsey research suggests AI-enabled approaches could accelerate R&D in complex manufacturing by 20-80%. For Jungheinrich, the stakes are clear: as it expands its electric product range, the ability to evaluate batteries quickly and reliably directly affects competitive position.
Dr Andreas Münz of Jungheinrich said the partnership lets the company "better leverage our test data to understand critical battery performance characteristics earlier and make smarter engineering decisions."
For product development teams, the practical benefit is straightforward: fewer months spent on validation, lower testing costs, and earlier visibility into whether a battery technology will work in your application. That compressed timeline matters when competitors are also racing to bring electric products to market.
The broader pattern reflects how manufacturing is shifting. AI doesn't replace physical testing-it complements it, helping teams focus resources on the experiments that actually move the needle.
Learn more about AI Data Analysis and how it applies to engineering workflows, or explore AI for Product Development to understand how these approaches fit into your development strategy.
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