Your equipment generates valuable data every cycle. Industrial IoT gateways bridge the gap between shop-floor protocols and your digital systems, turning silent machines into connected data sources. 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 via OPC-UA, MQTT, Modbus TCP/RTU, MTConnect, EtherNet/IP, PROFINET, and serial protocols. One gateway architecture that handles your entire equipment mix.
Retrofit sensors for current, vibration, temperature, and cycle detection on equipment without PLCs. Even 30-year-old machines become data sources.
Industrial-grade edge gateways that buffer, filter, and forward machine data. Local processing for time-critical alerts, cloud forwarding for analytics.
Normalize disparate machine data into a unified data model. Consistent naming, units, and timestamps regardless of equipment brand or protocol.
Segmented IoT network isolated from IT systems. Encrypted communications, device authentication, and firmware management built in from day one.
Push machine data to your ERP, MES, CMMS, or data lake via REST APIs, message queues, or direct database writes. Real-time or batched based on your architecture.
Catalog all shop-floor equipment — make, model, controller type, available protocols, and current connectivity. Identify quick wins and challenging retrofits.
Design the IoT network — edge gateways, network topology, security zones, and data flow to downstream systems. Size for current equipment and future expansion.
Connect 2-3 machines representing different protocol types. Validate data quality, latency, and reliability before scaling.
Roll out gateways and sensors to all target equipment. Commission each connection with data validation against known machine behavior.
Ongoing monitoring of gateway health, connection status, and data quality. Alerts for disconnections, anomalies, and firmware updates.
Equipment Connectivity for Charlotte automotive operations - configured around local workflows, data ownership, and implementation governance.
Equipment Connectivity for Charlotte energy infrastructure operations - configured around local workflows, data ownership, and implementation governance.
Equipment Connectivity for Charlotte aerospace & defense operations - configured around local workflows, data ownership, and implementation governance.
Equipment Connectivity for Charlotte food & beverage operations - configured around local workflows, data ownership, and implementation governance.
Equipment Connectivity for Charlotte financial services operations - configured around local workflows, data ownership, and implementation governance.
Equipment Connectivity for Charlotte healthcare & medical operations - configured around local workflows, data ownership, and implementation governance.
Yes. We retrofit external sensors — current transformers, accelerometers, thermocouples, and proximity switches — to capture cycle data, run/idle status, and operating conditions from any machine.
We design segmented networks where IoT traffic is isolated from your corporate IT. Edge gateways communicate outbound only, device authentication is enforced, and all data is encrypted in transit.
Typically 10-50 machines per gateway depending on data frequency and protocol type. We size the architecture during the design phase to balance cost, latency, and redundancy.
Not always. We use industrial Wi-Fi, cellular (4G/5G), and LoRaWAN for machines that are difficult to cable. The right connectivity method depends on data volume, latency requirements, and plant layout.
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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