We connect equipment, PLCs, and sensors to your data backbone — real-time OEE, predictive maintenance signals, and production tracking from the shop floor into your systems. Charlotte's manufacturing identity is splitting in two directions at once. Legacy energy suppliers built around Duke Energy's grid infrastructure are retooling for EV components, while Siemens Energy and ABB push turbine and switchgear production along the I-85 corridor toward full digital thread adoption. The result is a workforce fluent in heavy electrical assembly but largely unfamiliar with connected factory operations.
Charlotte's Tier 1 automotive suppliers are being forced into digital compliance by OEM mandates from BMW Spartanburg and the incoming VinFast plant, but most are still running disconnected Epicor and SAP instances with no real-time shop floor visibility.
Connect CNCs, PLCs, sensors, and legacy equipment to your network using industrial IoT gateways. Support for OPC-UA, MQTT, Modbus, MTConnect, and serial protocols.
Automatic OEE calculation from machine data — availability, performance, and quality. Dashboard visibility by machine, cell, line, and plant. No manual data entry.
Vibration, temperature, and cycle-time anomaly detection that predicts failures before they happen. Maintenance alerts integrated with your CMMS or work order system.
Automated job tracking from ERP production orders to machine-level execution. Real-time WIP visibility, cycle counting, and throughput reporting.
Track energy consumption by machine, line, and shift. Identify waste, optimize scheduling for off-peak rates, and support sustainability reporting.
Automated SPC data collection from in-process gauges and inspection stations. Real-time control charts and out-of-spec alerts.
Inventory all shop-floor equipment, identify connectivity options (PLC, sensor, protocol), and define the data points that matter most.
Design the IoT architecture — edge gateways, network infrastructure, data platform, and integration with ERP/MES. Security and scalability built in.
Deploy on 2-3 machines to validate connectivity, data quality, and dashboard accuracy. Prove the value before scaling.
Roll out to all target equipment. Integrate with ERP production orders, maintenance systems, and quality management.
Tune alerting thresholds, build predictive models, and expand data collection based on operational learnings.
IoT & Industry 4.0 for Charlotte automotive operations - configured around local workflows, data ownership, and implementation governance.
IoT & Industry 4.0 for Charlotte energy infrastructure operations - configured around local workflows, data ownership, and implementation governance.
IoT & Industry 4.0 for Charlotte aerospace & defense operations - configured around local workflows, data ownership, and implementation governance.
IoT & Industry 4.0 for Charlotte food & beverage operations - configured around local workflows, data ownership, and implementation governance.
IoT & Industry 4.0 for Charlotte financial services operations - configured around local workflows, data ownership, and implementation governance.
IoT & Industry 4.0 for Charlotte healthcare & medical operations - configured around local workflows, data ownership, and implementation governance.
Yes. We use retrofit sensors for vibration, current, temperature, and cycle detection on legacy equipment. Even a 30-year-old press can report OEE data with the right sensor package.
AWS IoT, Azure IoT Hub, and purpose-built manufacturing platforms like MachineMetrics, Tulip, and Ignition (Inductive Automation). Platform selection depends on your scale, existing infrastructure, and integration requirements.
We design IoT networks with segmentation from your IT network, encrypted communications, device authentication, and firmware update management. Security is architected in from day one, not bolted on.
Typical results: 10-20% OEE improvement, 25-50% reduction in unplanned downtime, and 15-30% reduction in maintenance costs. Most pilots pay for themselves within 6 months through downtime reduction alone.
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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