# AI for MachineMetrics: Connect CNC Machine Data to ERP Jobs, Costs and Answers > Source: https://netray.co/erp-ai/machinemetrics-ai-integration > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: AI for ERP ## Short answer AI for MachineMetrics joins the machine data it collects (cycle times, part counts, downtime reasons, feed and spindle signals) with ERP jobs, routings and costs, so operations teams can ask why a job ran long or which machines lose the most time and get a grounded, cited answer. MachineMetrics data is exported through its API into a store you control, and the AI layer can run on-prem. ## Kind platform ## Erp Family mfg-systems ## Erp Products - MachineMetrics ## Industries - Machining - Aerospace - Automotive - Medical Devices ## Region Global ## Persona Operations Manager ## Eyebrow MachineMetrics + ERP + private AI ## Intro MachineMetrics is a machine monitoring platform for discrete manufacturers. Edge devices read controllers over protocols such as MTConnect, Fanuc FOCAS, OPC UA and Modbus, and the platform records cycle times, part counts, machine states, operator-entered downtime reasons and, depending on the controller, signals such as spindle load and alarms. That makes it excellent at the question what is the machine doing right now and why was it stopped. It is much less able to answer the question a plant manager actually asks: did this job make money? That answer needs the ERP's job, routing, standard hours, material and labour cost, and the machine data side by side. MachineMetrics provides dashboards, OEE views, downtime analysis and an API, and many shops are well served by those alone. The case for a separate AI layer is the cross-system question, the open-ended investigation, and the plants whose security policy limits what machine data can sit in a vendor cloud next to ERP cost data. This page explains how to bring MachineMetrics data and ERP data together for natural-language analysis, what the data actually contains, and the honest limits of what an AI can infer from machine signals. ## Pain Points ### Machine time and job cost live in different systems **Detail:** The ERP knows planned hours and cost per work order operation; MachineMetrics knows actual cycle, setup and idle time per machine. Comparing them is usually a manual spreadsheet exercise per job. ### Downtime reasons are inconsistent **Detail:** Operators select reason codes under pressure, so the same cause appears under several codes and a large share ends up as unclassified. Pareto charts look precise but hide the quality of the underlying entries. ### Standards in the ERP are stale **Detail:** Routing run times were set at quoting and rarely updated. Actual cycle data would correct them, but nobody has time to compare thousands of operations by hand. ### Insight stops at the dashboard **Detail:** A dashboard shows that Machine 14 had low utilisation last week. It does not explain whether that was a changeover, a material shortage visible in the ERP, or a maintenance event. ### Cloud data sharing needs review **Detail:** Some aerospace and defense customers restrict where part, program and process data may be stored, which complicates joining machine data with controlled ERP records in a public AI tool. ## Use Cases ### Job actual versus standard **Description:** Ask why a job overran and get actual cycle, setup and downtime from machine data next to the ERP routing standard and quoted hours. **Erp Objects:** ERP work orders, routing operations, standard run and setup hours; machine cycle and state data **Outcome:** Job reviews become a few minutes of reading rather than hours of data pulling, and repeated overruns point to bad standards. ### Routing standard correction proposals **Description:** Compare observed median cycle times with ERP standards and list operations where the gap is consistent, as a proposal for a planner to review. **Erp Objects:** Routing master, operation run times, observed cycle time distributions **Outcome:** Quotes and schedules improve gradually as standards move toward reality; every change stays a human decision. ### Downtime Pareto with context **Description:** Rank downtime by reason, then cross-check against ERP shortages, maintenance orders and changeover counts to suggest probable causes. **Erp Objects:** Downtime events and reasons, ERP purchase order due dates, maintenance work orders **Outcome:** Shifts focus from top-line OEE to the few causes that repeat, with evidence you can inspect. ### Capacity and promise date checks **Description:** Use observed machine availability and cycle rates to sanity-check scheduled load and promise dates in the ERP. **Erp Objects:** ERP finite or infinite schedule, work centre calendars, observed utilisation **Outcome:** Fewer promise dates built on nameplate capacity; accuracy depends on how consistently machines are monitored. ### Unclassified downtime cleanup **Description:** Group similar free-text notes and unclassified stops, then suggest consolidation of reason codes for the continuous improvement team. **Erp Objects:** Downtime reason code list, operator notes, shift and machine attributes **Outcome:** Cleaner reason codes over time, so the next Pareto is more trustworthy. ### Tool and program change correlation **Description:** Look for scrap or cycle changes following a program revision, tool change or new material lot recorded in the ERP. **Erp Objects:** Part program revision records, tool life data, ERP lot receipts and quality inspections **Outcome:** Faster root-cause leads; correlation is presented as a lead to investigate, not proof. ### Shift handover summaries **Description:** Draft a summary of what ran, what stopped, and which jobs are at risk, for the next shift lead to confirm. **Erp Objects:** Machine state history, open work orders, due dates **Outcome:** Handover notes take minutes to review instead of being written from scratch. ## Architecture **Overview:** Machine data is pulled through the MachineMetrics API into a store in your environment, aligned to ERP jobs by machine, work centre and work order identifiers, and queried by a locally hosted model through a curated semantic layer. **Layers:** #### Machine data ingest **Detail:** Scheduled and incremental pulls of cycles, states, downtime events and part counts via the vendor API into a time-series or relational store you control. #### ERP alignment **Detail:** Mapping of machine IDs to ERP work centres and of job or part-program identifiers to work order operations, with exceptions listed where mapping is missing. #### Semantic layer **Detail:** Named metrics such as run time, setup time, idle time and first-article scrap defined once, so every question uses the same definition. #### Model serving **Detail:** An open-weight model on your GPUs or private cloud, using tool calls to query the store and ERP APIs rather than answering from memory. #### Controls **Detail:** Read-only access, query display, role scoping by plant, and human approval for any change to ERP routing data. ## Deployment Options ### Hybrid: vendor platform plus on-prem AI **Fit:** Shops happy with MachineMetrics cloud but unwilling to send ERP cost data out **Detail:** Machine data is mirrored to your environment; the model and ERP data stay on-prem. ### Private cloud analytics **Fit:** Multi-plant manufacturers with a central data team **Detail:** A central store collects machine and ERP data from each plant, with per-plant access scopes. ### Air-gapped analysis copy **Fit:** Programs with strict data handling **Detail:** Periodic exports are moved into an isolated environment for analysis, accepting some latency in exchange for isolation. ## Compliance ### Part, program and process data in the analysis store can be confined to infrastructure the customer controls; the customer's export program governs access. **Framework:** ITAR / EAR ### If cost and drawing-related data is controlled unclassified information, keeping the AI layer inside the enclave avoids adding an external processor. **Framework:** CMMC / NIST SP 800-171 ### Machine and process evidence becomes queryable for audits, while the quality system remains the record. **Framework:** AS9100 and IATF 16949 ### Review MachineMetrics data ownership and API terms; the integration uses only documented interfaces. **Framework:** Vendor terms ## Integration Notes - Use the documented MachineMetrics API for data export and confirm rate limits and data retention windows before planning backfills. - Agree on a single machine identifier shared between MachineMetrics, the ERP work centre and maintenance records. - Capture job context by having operators or the ERP shop-floor integration tag the active work order, otherwise cycles cannot be tied to costs. - Store raw events and derived metrics separately so definitions of OEE or utilisation can be changed without reloading data. - Join to the ERP using its APIs for work orders, routings and standards; avoid writing to ERP tables directly. - Control-level data such as spindle load depends on what each controller exposes, so list per-machine signal coverage early. ## Product Fit ### Custom build **Why:** The value is in the join between machine data and ERP jobs, which needs a bespoke semantic layer for your machines, jobs and cost model. ### ERPray **Why:** For the ERP side of the question, ERPray can answer job, routing and cost questions on supported ERPs and be paired with the machine data store. ### DataRay **Why:** If machine data lands in a database or file share alongside quality records, DataRay can chat across those sources on-prem. ## Engagement ### Discovery **Duration:** 1 to 2 weeks **Deliverables:** - Questions that mix machine and ERP data - Machine to work centre mapping audit - API access and retention review ### Data foundation **Duration:** 2 to 4 weeks **Deliverables:** - Ingest pipeline into your store - Defined metrics (run, setup, idle) - Job context tagging approach ### Pilot **Duration:** 3 to 5 weeks **Deliverables:** - Job actual versus standard Q&A for one cell - Test set with verified answers - Feedback from planners ### Rollout **Duration:** 3 to 6 weeks **Deliverables:** - More machines and plants - Standards review workflow - Monitoring and runbook ## Buyer Questions - How will machine cycles be tied to ERP work orders, and who maintains that mapping? - What share of our downtime is currently unclassified? - Which machines expose which signals, and which are only on/off? - Where will machine data and ERP cost data be joined, and who can see it? - Can the answer show the metric definition and the underlying rows? - How are changes to ERP routing standards approved? - What does MachineMetrics already do that we should not rebuild? ## Faq ### Does MachineMetrics have its own AI? **Answer:** MachineMetrics provides analytics, OEE and downtime views and continues to add features, so check the current product first. Where a separate AI layer helps is the cross-system question, such as comparing machine actuals with ERP cost and standards, or where policy requires the combined data to stay on infrastructure you control. ### Can AI tell why a machine stopped? **Answer:** Only to the extent the data supports it. Controller alarms, operator reason codes and ERP context such as shortages can suggest probable causes, but the AI should present these as leads with evidence. It cannot see problems that no signal or person recorded. ### Do we need to replace MachineMetrics dashboards? **Answer:** No. Dashboards are the right tool for live monitoring on the floor. The AI layer is for investigation, cross-system questions and summaries, and sits beside them, using the same underlying machine data through the API. ### Which ERPs does this work with? **Answer:** Any ERP with a usable API or reporting interface. We have documented patterns for Infor SyteLine, Epicor Kinetic and Prophet 21, NetSuite, SAP and others. The mapping of work orders, routings and work centres is where most of the effort goes, not the ERP brand. ### Can it update ERP routing times automatically? **Answer:** We recommend against automatic updates. The AI can propose changes with supporting cycle data, and a planner or process engineer approves them. Write-back, if used, goes through the ERP's supported APIs with an audit trail. ### What about machines with no controller data? **Answer:** Older machines may expose only on/off or a signal from a current sensor. Questions about cycle time and utilisation can still work, but diagnostics will be shallower. We list per-machine signal coverage during discovery so expectations are realistic. ### How long before we get useful answers? **Answer:** Usually a few weeks after data is flowing and machine IDs are mapped to work centres. The slowest part is typically job context, because cycles cannot be tied to cost unless operators or the shop-floor integration tag the active work order consistently. ## Search terms machinemetrics ai, machinemetrics erp integration, machine monitoring ai, cnc downtime analysis ai, job actual vs standard ai, machinemetrics api integration, oee ai analysis, machine data and erp, cnc machine monitoring erp, private ai machine data, routing standards from cycle time, machinemetrics alternative analytics --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json