Paper-based quality checks catch defects after hundreds or thousands of parts have been made. Automated SPC data collection from gauges and inspection stations catches drift in real time — before scrap and rework pile up. Ford's Rouge Electric Vehicle Center, GM's Factory ZERO, and Stellantis's retooling of Jefferson North are rewriting what it means to build cars in Detroit. Tier 1 and Tier 2 suppliers along the I-94 corridor face a brutal reality: retool for EV drivetrains and battery modules, or lose contracts to greenfield competitors. The shift from internal combustion to electric has compressed product development cycles from years to months, and legacy Plex and QAD installations weren't designed for that pace.
Detroit suppliers who digitized scheduling and traceability before the EV transition hit are winning new battery-pack contracts; those still running paper-based PPAP are getting passed over.
Direct connection to digital calipers, micrometers, CMMs, and vision systems. Measurement data flows automatically — no manual recording.
Control charts (X-bar/R, X-bar/S, individuals) updated in real time as measurements are captured. Automatic rule violation detection (Western Electric, Nelson rules).
Immediate alerts when measurements exceed control limits or specification limits. Escalation to quality engineers and optional machine-stop integration.
Automatic Cp, Cpk, Pp, Ppk calculation by part, feature, machine, and time period. Track capability trends and identify processes drifting toward their limits.
Link quality data to production orders, material lots, machines, operators, and tooling. When a quality issue appears, trace it back to its source immediately.
First-pass yield, scrap rates, defect Pareto, and capability reports. Exportable for customer quality requirements, PPAP submissions, and audit documentation.
Review your current inspection plans, measurement methods, and data flow. Identify where automated data collection delivers the most value.
Connect digital gauges, CMMs, and inspection equipment to the data collection system. Validate data accuracy against known standards.
Configure control charts, spec limits, sampling plans, and alerting rules for each measurement point. Match your quality plan requirements.
Train inspection operators on the new data collection workflow. The goal: faster than paper with zero transcription effort.
Use real-time quality data to drive capability improvement initiatives. Track the impact of process changes against SPC baselines.
Quality Data Collection for Detroit automotive operations - configured around local workflows, data ownership, and implementation governance.
Quality Data Collection for Detroit aerospace & defense operations - configured around local workflows, data ownership, and implementation governance.
Quality Data Collection for Detroit robotics & automation operations - configured around local workflows, data ownership, and implementation governance.
Quality Data Collection for Detroit steel & metals operations - configured around local workflows, data ownership, and implementation governance.
Quality Data Collection for Detroit financial services operations - configured around local workflows, data ownership, and implementation governance.
Quality Data Collection for Detroit healthcare & medical operations - configured around local workflows, data ownership, and implementation governance.
Most digital gauges with USB, RS-232, or Bluetooth output — Mitutoyo, Starrett, Mahr, Fowler, Keyence, and others. CMMs from Zeiss, Hexagon, Mitutoyo. Vision systems from Keyence, Cognex, and similar. If it outputs digital data, we can likely connect it.
It complements your QMS by providing the real-time data collection layer that most QMS platforms lack. We integrate with quality management systems for CAPA, NCR, and document control workflows.
Yes. Automated SPC, real-time control charts, capability analysis, and full traceability support IATF 16949 requirements. The system provides the objective evidence auditors look for.
Configurable sampling plans — 100% inspection, first/last piece, every Nth part, time-based intervals, or statistical sampling. Different plans for different parts, features, and risk levels.
Most manufacturers are still running workflows that require a person to touch every exception, every order, every routing decision. AI agents eliminate that bottleneck — not by replacing your people, but by handling the work that was always below their pay grade.
Most manufacturers forecast demand with spreadsheets, gut feel, and last year's numbers adjusted by 5%. ML models trained on your actual order history, seasonality patterns, and market signals replace guesswork with predictions your planning team can act on.
Odoo Maintenance captures work orders, failure reasons, repair times, and equipment history. We build AI models on top of that data to identify failure patterns and recommend maintenance windows before breakdowns occur — no new hardware, no IoT infrastructure required.
Odoo Quality captures inspection results, non-conformances, scrap reasons, and lot traceability across every production order. We build AI models on top of that data to surface defect patterns, predict quality risk, and trigger alerts before scrap accumulates — no cameras, no hardware.
Most manufacturers price by cost-plus formula or by whatever the sales rep negotiated last time. AI pricing models factor in material costs, competitive positioning, customer segment, order size, inventory position, and market conditions — governed by business rules so every price stays within approved boundaries.
When an order hits your system, someone decides which warehouse ships it — usually based on habit, proximity, or whoever answered the phone. AI order routing makes that decision in real time, optimizing across inventory availability, shipping cost, delivery speed, and warehouse workload.
Manufacturers still process thousands of POs, invoices, RFQs, spec sheets, and BOLs manually — reading PDFs, retyping data into the ERP, and fixing the errors that come with it. Document intelligence extracts structured data from unstructured documents automatically, with validation rules that catch errors before they enter your systems.
Your dealers call or email to check stock before placing orders because they can't see what's available. We give them live ATP visibility across all your warehouses — available, allocated, in-transit, and expected replenishment dates — straight from your ERP and WMS.
We govern cloud migration in phases — every dependency mapped, every workload sequenced, every cutover window defined. Zero-downtime migration for manufacturers who can't afford an outage.
Most manufacturing AI projects die in the pilot phase. We deploy AI that integrates into your actual workflows -- demand forecasting, predictive maintenance, pricing optimization, and intelligent routing -- governed by operational data contracts.
Your demand planning process runs on last year\u2019s sales adjusted by a gut-feel percentage. ML models trained on your actual order history, seasonal patterns, and market signals produce forecasts that are measurably more accurate \u2014 and they improve automatically as more data accumulates.
Your legacy system holds critical data that modern applications need -- but it has no APIs, no webhooks, and no modern integration points. We build a REST/GraphQL API layer on top of your legacy system so new applications can access data without touching the core.
Metrotechs starts with the operating questions: which records are trusted, which workflows are manual, which systems own each decision, and where AI can safely improve throughput.
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