That COUNTIFS formula is doing its job, but it's also showing you exactly where traditional spreadsheets start to break down. You have 50,000 rows of defect data, fifteen different descriptions for the same defect type, and a manual workaround that forces you to rewrite conditions every time your naming conventions shift. You're not asking for the impossible. You're asking for a tool that thinks the way you do, by intent, not by exact text match.
The core problem isn't your formula. It's that your spreadsheet treats "similar" and "identical" as the same thing. When defect descriptions change over time, a COUNTIFS can't infer that "crack, surface," "surface crack," and "crack (surface)" all refer to the same issue. You've already done the smart thing: you grouped them into one column. But then you hit the wall, no native OR logic that accepts a list without manual repetition. That's not a failure of your approach. It's a limitation of the tool itself.
What you need is a system that understands categories, not just strings. An AI-native spreadsheet can look at your defect descriptions, learn the pattern behind your grouping, and apply that logic across every row. Instead of writing =COUNTIFS with a dozen OR clauses, you define the group once, call it "Surface Cracks", and the tool recognizes any description that belongs there. Your monthly counts become accurate, maintainable, and resilient to future naming changes. No more rebuilding formulas when someone updates a label.
This is where the real productivity gain lives. You've already proven you know how to structure a multi-criteria count. The missing piece is a layer of intelligence that handles the fuzziness of real-world data. The next step isn't a better workaround. It's a tool that adapts to your data, not the other way around. Explore what that looks like, your analysis deserves a system that keeps up with you.