Can AI Nesting Actually Cut Sheet Metal Scrap in Tier-2 Cities? A Coimbatore Case Study

Sheet metal nesting software has promised AI-driven material savings for years. A mid-sized fabrication shop in Coimbatore agreed to track its actual scrap rate for three months before and after switching to an AI-assisted nesting tool, and shared the numbers on the condition that the software vendor not be named until the shop finishes its own longer evaluation.

The numbers

Material yield improved from an average of 78% to 83% across the shop's standard mild steel and stainless product mix — a meaningful but not dramatic gain, and well short of the "up to 15% material savings" figure the vendor's marketing material had led the shop to expect.

Where the gap came from

The shop's production manager pointed to two factors: a large share of jobs already ran on well-optimised legacy nesting layouts with little room left to improve, and a meaningful minority of jobs involved odd-shaped parts or mixed-thickness batches where the AI tool's suggestions needed manual override anyway.

“Five percent material yield across our whole mix is real money over a year. It's just not the number on the brochure,” the production manager said.

What to ask before buying

• Ask for yield data on your own part mix, not the vendor's benchmark library
• Separate the software cost from expected labour-time savings when running payback math
• Check whether the tool handles mixed-thickness and remnant-sheet nesting, where most real-world savings tend to concentrate