# Forging Die Life Tracking and Scrap Analysis With Your ERP Data > Source: https://netray.co/resources/forging-die-life-tracking-and-scrap-analysis > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Knowledge Base ## Short answer How forge shops track pieces per die set, repair cycles and scrap causes, and use ERP history and private AI to find which dies and parts cost the most. ## Category ai-automation ## Content **Intro:** Forging die life tracking records how many good pieces each die set produces, when it was polished, repaired or retired, and why, so you can quote tooling cost from facts and spot dies and parts that drain margin. Scrap analysis does the same for lost pieces: where in the route they were lost and for what reason. Most forge shops hold this information in operator notes, die logs and ERP scrap codes that are rarely analyzed together. This guide shows what to capture and how AI over your own data can surface patterns. **Sections:** #### What to Record for Every Die Set **Body:** Give each die set an identifier and treat it as an asset. Record the part number or family, alloy forged, press, date installed, pieces forged per run, polish and weld repair events, cracks or wear found, and the reason and date of retirement. Link each production run to the die set in the work order so piece counts accumulate automatically. Add a short coded reason field for retirement, such as wear, cracking, galling, or customer change, rather than free text only. The record does not need to be elaborate; it needs to be consistent enough that a year of data can be grouped and compared. **Bullets:** - Die set ID, part family, alloy and press - Pieces per run and cumulative pieces - Repair events with date and type - Coded retirement reason #### Scrap Codes That Actually Help **Body:** Scrap categories should point to a cause you can act on. Useful codes distinguish underfill, laps and folds, cracks, die wear out of tolerance, heat treat failure to meet hardness or properties, dimensional rejects at inspection, NDT rejects, and handling damage. Also record the operation where the part was found, because scrap discovered after heat treat is more expensive than scrap found at the press. Operators and inspectors need to be able to enter codes quickly at the point of work; long lists get ignored. Review codes quarterly and merge any that are never used. **Bullets:** - Cause codes: underfill, lap, crack, wear, hardness, NDT, dimensional - Operation where scrap was found - Quantity and weight scrapped #### Connecting Die Life and Scrap to Cost **Body:** Once die and scrap data exist, translate them into cost. Die cost per piece equals die set cost plus repair cost divided by good pieces over its life. Scrap cost per piece includes material, heat, press time and downstream processing already spent. Compare actual die cost per piece to what was quoted, by part family. A few findings recur in forge shops: a stainless family whose dies retire early, a part whose underfill scrap clusters at the start of each run after a die change, or a customer whose inspection rejects drive hidden rework. Each finding suggests a different fix, from die redesign to preheat practice to a pricing change. #### Asking Questions in Plain Language **Body:** Once data is in the ERP or a tracking sheet, a private assistant can answer questions without a reporting request, such as which die sets retired before 70 percent of quoted life this year, or which scrap code rose in the last quarter on a given alloy. It should show the query and rows behind each answer, run read-only, and follow ERP permissions. Netray builds these assistants over SyteLine, NetSuite, M3, LN and other ERPs, and over file shares or spreadsheets with DataRay, on customer-owned hardware. The assistant does not generate data that is not there, so the quality of the log determines the quality of answers. **Bullets:** - Die sets retired earlier than quoted life - Scrap rate by code, alloy and operation - Jobs where die cost per piece exceeded the quote #### When This Is Overkill **Body:** A shop with a handful of dies and a long-standing crew that already knows each die's habits may get little from formal tracking. The effort pays when you carry many dies, quote tooling in piece price, or run alloys that are hard on dies. Start with one press and one part family for a quarter, check that the numbers match what the floor believes, and expand only if the data proves useful. **Cta:** If die logs and scrap records sit in spreadsheets and ERP codes, Netray can make them searchable together, read-only and on your own network. **Faq:** #### How do you track forging die life? **Answer:** Assign each die set an ID, link production runs to it so piece counts accumulate, and log repairs and retirement reasons. Review pieces per die set by alloy and part family and feed the results back into quoting. #### What are the most common causes of forging scrap? **Answer:** Underfill, laps and folds, cracks, die wear out of tolerance, heat treat nonconformance and dimensional or NDT rejects. Your own codes and operation data will show which dominate in your shop. #### Can an ERP track die life? **Answer:** Many can, using tooling or asset records with usage counters, but they often need configuration and discipline at job close. Where the ERP does not fit, a controlled spreadsheet linked by job number is a workable start. ## Related Slugs - spc-integration-erp-quality - shop-floor-data-collection-erp - natural-language-query-erp-data - preventive-maintenance-erp-integration ## Search terms forging die life tracking, forging scrap analysis, die wear forging, forging scrap codes, tooling management forge shop, forging quality data ERP --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json