Raw materials can make up as much as 70% of a battery cell's total cost. In a market where lithium and cobalt prices can swing by double digits in a few weeks, that single figure explains why traditional cost modeling is broken. Static spreadsheets, updated quarterly or even monthly, aren't just inefficient anymore, they're a major financial risk. When a billion-dollar capex decision is on the line, relying on stale data is a recipe for disaster. That's the challenge Wensura, a cost and supply intelligence platform, was built to solve. It turns slow, manual battery materials cost modeling into an instant strategic advantage.
The Fallacy of Static Spreadsheets in a Volatile Market
For years, battery techno-economic analysis (TEA) has been a painfully manual process. An R&D team develops a bill of materials (BOM) in a complex spreadsheet, often referencing academic models like Argonne's BatPaC. When the procurement team gets a new quote for cathode active material or a precursor, an engineer has to manually update dozens of cells, re-run calculations, and check for errors. This process can take days, if not weeks. By the time the updated per-kWh cost is ready, the market data it’s based on is already obsolete.
