๐—”๐˜‚๐˜๐—ผ๐—บ๐—ฎ๐˜๐—ถ๐—ป๐—ด ๐˜๐—ต๐—ฒ ๐—–๐—ผ๐˜€๐˜ ๐—–๐—ฎ๐—น๐—ฐ๐˜‚๐—น๐—ฎ๐˜๐—ถ๐—ผ๐—ป: ๐—™๐—ฟ๐—ผ๐—บ ๐— ๐—ฎ๐˜๐—ฒ๐—ฟ๐—ถ๐—ฎ๐—น ๐—ฎ๐—ป๐—ฑ ๐—ฅ๐˜‚๐—ป๐˜๐—ถ๐—บ๐—ฒ ๐˜๐—ผ ๐—ฎ ๐—ช๐—ถ๐—ป๐—ป๐—ถ๐—ป๐—ด ๐—ฃ๐—ฟ๐—ถ๐—ฐ๐—ฒ

Every hour spent hunting material prices, calculating machine time, and applying markup rules is an hour lost on the shop floor. Small job shops lose bids when quotes are slow or inaccurate. This lets competitors win work that should be yours.

The foundation of reliable AI quoting is a single source of truth. This source ties geometry, material, process, and business rules together. By storing material costs, supplier info, and update dates, the system connects part features to a calculation engine. This allows the system to derive machine time, add standard operations, and apply the correct markup without manual lookup.

Tool Spotlight: Runtime Calculator

This engine takes inputs like stock diameter, finished diameter, and length to output precise machine hours. It replaces guesswork with repeatable estimates.

An RFQ arrives for a 5 x 5 x 0.5 inch plate of 6061 aluminum. The system pulls the plate cost from the Material Database, sends the geometry to the Runtime Calculator for milling, adds deburring time, and applies the margin based on volume and industry.

How to implement this:

Centralizing material data, linking geometry to a runtime calculator, and automating markup rules turns quoting from a bottleneck into a competitive advantage. You get faster, accurate RFQ responses. This helps you win more work while protecting your margins.

Source: https://dev.to/ken_deng_ai/automating-the-cost-calculation-from-material-and-runtime-to-a-winning-price-311n

Optional learning community: https://t.me/GyaanSetuAi