# Ask Your ERP Questions: AI for Stamping Plant Managers > Source: https://netray.co/resources/ask-your-erp-questions-stamping-plant-manager > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Knowledge Base ## Short answer The questions stamping plant managers ask their ERP daily, such as late releases, coil on hand and scrap by press, and how a private AI layer answers them with sources. ## Category ai-automation ## Content **Intro:** A natural-language layer over your ERP lets a stamping plant manager ask questions like which customer releases will ship late, how much coil is on hand for next week, or which presses had the highest scrap rate, and get an answer from live data with the query shown. It does not replace the ERP; it saves the wait for a report or a call to IT. The approach works when the ERP data is reasonably clean and access respects existing roles. This guide lists realistic questions, how the answers are produced, and where the limits are. **Sections:** #### Questions Stamping Plants Ask Every Day **Body:** The daily questions are specific to the process. Which open releases have less finished goods plus WIP than required by ship date? Which jobs on press 7 this week lack coil of the right width and gauge? What was scrap rate by press last month, and which part numbers drove it? How many hours until die 4412 reaches its sharpen interval? Which customer has the most past-due lines? Each question touches several tables in the ERP: sales order lines or releases, inventory by lot, job and operation status, labor and scrap transactions. Someone who knows the schema must write the query, which is why the answer usually takes a day. **Bullets:** - Open releases at risk by ship date, with inventory and WIP - Coil on hand by width, gauge, alloy and lot against next week's jobs - Scrap rate by press, die and part number for the last 30 days - Quoted versus actual cost for the last ten closed jobs on a part - Past-due purchase orders for coil and outside processing #### How a Grounded Answer Is Produced **Body:** The assistant translates the question into a query on the ERP database or API, runs it read-only, and returns the rows with a plain explanation. It should display the query or the source tables so an analyst can verify it, and it should refuse to guess when a field is ambiguous, for example whether scrap quantity is stored in pieces or pounds. It must respect the ERP's roles so a supervisor sees only what the role allows. Hallucinated numbers are the main risk, so the design returns data rows and computes totals in code rather than asking a model to add up figures. #### Where This Beats a Dashboard and Where It Does Not **Body:** Dashboards are better for fixed daily numbers everyone watches, such as on-time delivery and OEE. A question layer is better for the long tail: one-off questions that arise during a customer call or a Monday meeting and are not worth a custom report. It also helps people who do not know where data lives. It is not a replacement for scheduling, MRP or quality systems, and it does not fix missing transactions. If the real problem is that operators do not report scrap, an assistant will simply report that scrap is low. #### Privacy, Deployment and Write-Back **Body:** Customer prints, volumes and pricing are confidential, so many manufacturers prefer to keep AI inside their own network. Open-weight models such as Llama, Qwen or Mistral class models can run on local GPUs using vLLM or Ollama, with retrieval over ERP data and documents. Start read-only. If you later want the assistant to create a purchase requisition or update a promise date, require explicit human approval for every write. Netray builds this for NetSuite, Infor SyteLine and LN and other ERPs through connectors, and you should insist on audit logs for every question and action. #### When It Is Not Worth It **Body:** If your ERP already has an easy report writer and a person who builds reports within hours, an AI layer adds little. If your data is full of gaps or duplicate part numbers, clean that first. If you have only a few users and stable questions, a handful of saved reports will serve better. The case is strongest in plants with many part numbers, several shifts, and managers who regularly wait for IT to answer simple questions. Pilot with ten real questions and compare answers against an analyst before rolling out. **Cta:** Give Netray ten questions your managers ask each week, and we will show how a private, read-only assistant answers them from your ERP with the query visible. **Faq:** #### Can I ask my ERP questions in plain English? **Answer:** Yes, with a natural-language layer that converts questions into read-only queries on your ERP data, shows the query, and returns the rows. Quality depends on data cleanliness and a good mapping of your schema, and answers should be verifiable against the source. Start with a pilot set of real questions. #### Is AI over our ERP safe for confidential customer data? **Answer:** It can be, if designed properly: local model hosting, read-only access, ERP role enforcement, query logging and no data sent to external APIs. Review your customer contracts and any export-control constraints, and have IT approve the architecture before connecting production data. #### Will AI replace our ERP reports and dashboards? **Answer:** No. Fixed dashboards remain the best way to monitor daily KPIs like on-time delivery and scrap. A question layer complements them by answering ad hoc questions that are not worth a custom report. It also depends on the same underlying ERP transactions being accurate. ## Related Slugs - on-prem-ai-for-syteline - syteline-kpi-manufacturing-metrics - syteline-dashboard-development-guide - private-ai-data-governance-framework ## Search terms ask ERP questions AI, natural language query ERP manufacturing, AI for stamping plant manager, chat with ERP data, AI reporting for manufacturers, private AI ERP stamping --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json