Most manufacturers calculate OEE monthly from estimates and spreadsheets. We connect directly to your machines to deliver real-time availability, performance, and quality metrics — automatically, accurately, continuously. 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.
OEE data captured directly from machine signals — cycle times, run/idle status, part counts, and reject counts. No operator data entry required.
Real-time uptime/downtime with categorized reason codes. Operators tag downtime from a touchscreen or tablet — takes seconds, not minutes.
Actual cycle time vs. ideal cycle time, automatically. Detect speed losses, micro-stops, and slow cycles that don't trigger downtime alerts but kill throughput.
Reject counts from machine sensors or operator input. First-pass yield and scrap rates integrated into the OEE calculation in real time.
OEE by machine, cell, line, shift, product, and plant. Drill from plant-level summary to individual machine performance in one click.
Configurable alerts for OEE drops, extended downtime, and performance thresholds. Escalation chains that notify the right people at the right time.
Understand your current OEE measurement method, data sources, and reporting cadence. Identify the biggest gaps between current metrics and reality.
Connect target machines and validate the data signals needed for OEE — cycle complete, run/idle, part count, and reject indicators.
Configure OEE dashboards, reason code trees, shift schedules, and ideal cycle times for each machine/product combination.
Train operators on downtime tagging, reject entry, and dashboard use. The system must be easier than the clipboard it replaces.
Use real-time OEE data to drive Pareto-based improvement initiatives. Track the impact of changes against historical OEE baselines.
Real-Time OEE for Detroit automotive operations - configured around local workflows, data ownership, and implementation governance.
Real-Time OEE for Detroit aerospace & defense operations - configured around local workflows, data ownership, and implementation governance.
Real-Time OEE for Detroit robotics & automation operations - configured around local workflows, data ownership, and implementation governance.
Real-Time OEE for Detroit steel & metals operations - configured around local workflows, data ownership, and implementation governance.
Real-Time OEE for Detroit financial services operations - configured around local workflows, data ownership, and implementation governance.
Real-Time OEE for Detroit healthcare & medical operations - configured around local workflows, data ownership, and implementation governance.
Automated OEE from machine data is typically 5-15 points lower than manually reported OEE — not because performance is worse, but because manual methods systematically miss micro-stops, speed losses, and short downtimes. Accurate data drives better decisions.
Yes. The system captures downtime events automatically from machine signals, but operators categorize the reason via a touchscreen or tablet at the machine. This hybrid approach gives you both accuracy and context.
Most manufacturers see 10-20% OEE improvement within 6 months of deploying real-time monitoring. The improvement comes from visibility — when you can see losses in real time, you act on them faster.
It can complement or replace parts of your MES depending on your needs. For many mid-size manufacturers, real-time OEE plus production tracking covers what they actually use an MES for, at a fraction of the cost.
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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