Energy is your second or third largest operating cost, but most manufacturers can't tell you which machines consume the most, which shifts waste the most, or how production scheduling affects utility bills. We make energy visible and actionable. Los Angeles produces more manufactured goods than any metro in the United States, but the narrative is dominated by entertainment and tech. Northrop Grumman's B-21 Raider program in Palmdale, SpaceX's Hawthorne rocket production, and Boeing's El Segundo satellite operations make the South Bay the densest aerospace corridor in the world. Below that defense-prime layer sits City of Industry — a municipality that is literally nothing but factories — where thousands of small and mid-market manufacturers produce everything from food packaging to precision machined parts under ITAR restrictions they barely understand.
LA's manufacturing base is so fragmented across 12,000+ firms that no single initiative reaches critical mass — digital transformation here happens company by company, with almost no regional coordination or shared infrastructure.
Sub-meters on individual machines or circuits that capture real-time power consumption. kWh, kW demand, power factor, and voltage quality by machine.
Energy consumption broken down by shift, product, and operator. Identify which production runs are energy-efficient and which are not.
Automatic detection of machines running in idle — drawing power without producing. Alerts for equipment left on during breaks, weekends, and between jobs.
Monitor and manage peak demand to avoid demand charge spikes. Load-shedding recommendations and scheduling optimization for time-of-use rate structures.
Energy per unit, carbon equivalent per product, and plant-level intensity metrics. Data ready for ESG reporting, customer requirements, and regulatory compliance.
Allocate energy costs to production orders, cost centers, and products. Accurate energy costing for pricing decisions and profitability analysis.
Assess current energy consumption patterns, utility rate structures, and metering infrastructure. Identify the highest-impact monitoring targets.
Install sub-meters, CTs (current transformers), and power monitors on target equipment and circuits. Non-invasive installation with no production impact.
Configure energy dashboards with real-time consumption, historical trending, and cost analysis. Set up alerts for demand spikes and idle equipment.
Analyze data to identify scheduling optimizations, idle reduction opportunities, and demand charge mitigation strategies. Quantify savings potential.
Continuous monitoring with anomaly detection for equipment degradation, air leaks, and efficiency losses. Monthly energy reviews tied to production metrics.
Energy Monitoring for Los Angeles aerospace & defense operations - configured around local workflows, data ownership, and implementation governance.
Energy Monitoring for Los Angeles food & beverage operations - configured around local workflows, data ownership, and implementation governance.
Energy Monitoring for Los Angeles textiles & apparel operations - configured around local workflows, data ownership, and implementation governance.
Energy Monitoring for Los Angeles electronics operations - configured around local workflows, data ownership, and implementation governance.
Energy Monitoring for Los Angeles technology & software operations - configured around local workflows, data ownership, and implementation governance.
Energy Monitoring for Los Angeles financial services operations - configured around local workflows, data ownership, and implementation governance.
Typical savings range from 10-25% of energy costs. The biggest wins come from idle reduction (5-15%), demand charge management (3-10%), and scheduling optimization (2-5%). ROI on metering hardware is usually under 12 months.
We design the metering plan and work with licensed electricians for installation. Sub-metering uses clamp-on CTs that install without disconnecting equipment — no downtime required.
Yes. We provide energy-per-unit, carbon-equivalent-per-product, and scope 1/2 emissions data at the granularity your reporting requires. Data feeds directly into your sustainability reporting framework.
Yes. We integrate with BMS, SCADA, and utility meter systems. Machine-level energy data combined with HVAC and lighting gives you a complete picture of facility energy consumption.
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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