Production managers shouldn\u2019t discover yesterday\u2019s bottleneck in today\u2019s shift report. We build production analytics that track OEE, cycle times, scrap rates, and throughput in real time \u2014 so problems are identified when they can still be fixed, not documented after the fact. 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.
Availability, performance, and quality tracked in real time for every production line and work center. OEE calculated automatically from machine data, MES, or operator input \u2014 not end-of-shift paperwork.
Actual cycle times measured against standard times by machine, product, and operator. Identify variation patterns, slow-running jobs, and setup time opportunities.
Scrap rates by product, machine, shift, and defect type. Connect scrap events to upstream process parameters to identify root causes, not just symptoms.
Automated detection of production bottlenecks based on throughput data, queue lengths, and utilization rates. See where production is constrained and quantify the capacity impact.
Compare planned production schedule against actual completions in real time. Identify jobs that are behind schedule while there\u2019s still time to recover.
Performance metrics by shift, crew, and operator. Identify training needs, best-practice patterns, and staffing optimization opportunities \u2014 with data, not opinions.
Inventory production data sources \u2014 MES, PLCs, paper logs, ERP work orders. Identify what\u2019s measured, what\u2019s missing, and what\u2019s measured but not used.
Define OEE calculations, cycle time standards, scrap categories, and performance benchmarks. Align operations and production leadership on the definitions.
Build the data pipeline from production systems to analytics dashboards. Real-time for OEE and throughput, near-real-time for quality and scheduling metrics.
Deploy dashboards on shop-floor displays, supervisor tablets, and management desktops. Train production teams on using the data for shift management and continuous improvement.
Production Analytics for Detroit automotive operations - configured around local workflows, data ownership, and implementation governance.
Production Analytics for Detroit aerospace & defense operations - configured around local workflows, data ownership, and implementation governance.
Production Analytics for Detroit robotics & automation operations - configured around local workflows, data ownership, and implementation governance.
Production Analytics for Detroit steel & metals operations - configured around local workflows, data ownership, and implementation governance.
Production Analytics for Detroit financial services operations - configured around local workflows, data ownership, and implementation governance.
Production Analytics for Detroit healthcare & medical operations - configured around local workflows, data ownership, and implementation governance.
No. We can build analytics from ERP work order data, PLC signals, operator input tablets, or a combination. An MES provides the richest data, but useful production analytics can be built from whatever data sources you have today.
For manual operations without machine data, we deploy operator input stations \u2014 tablets or terminals at work centers where operators log starts, stops, counts, and scrap. Simple input, structured data.
Yes. We integrate with common MES platforms -- Plex, IQMS (DELMIAworks), and custom shop-floor systems. The analytics layer sits on top of whatever production data collection you already have.
Manufacturers typically see 5\u201315% OEE improvement in the first year from visibility alone \u2014 before any process changes. The biggest gains come from reducing unplanned downtime and setup time, both of which become visible immediately with real-time tracking.
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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