Edge AI Energy Monitoring: Real-Time Optimization Strategies That Cut Enterprise Bills by 40%

The Edge AI Revolution in Energy

Enterprise energy management is undergoing a fundamental shift from cloud-centralized to edge-distributed intelligence. While AI deployment in energy has accelerated dramatically, the next frontier is Edge AI—deploying artificial intelligence directly at the point of data generation for real-time decision-making without cloud latency.

The compelling economics: Enterprises deploying Edge AI energy monitoring report 40% average cost reductions, sub-second response times, and 90% reduction in cloud bandwidth costs. These aren't theoretical benefits—they're measurable outcomes from organizations that recognized a fundamental truth: energy optimization decisions made in real-time are exponentially more valuable than those made minutes or hours later.

Traditional cloud-based energy monitoring creates a critical gap: sensors → cloud → analysis → action takes 30-300 seconds. Edge AI collapses this to milliseconds by processing data locally, enabling immediate response to wasteful consumption patterns, demand spikes, and grid events before they impact operations or costs.

Why Real-Time Matters: The Millisecond Advantage

Energy consumption follows millisecond-level patterns invisible to traditional monitoring systems. Understanding why real-time processing delivers exponential value requires examining three critical dimensions:

1. Demand Charge Mitigation: The 15-Minute Window

Most commercial electricity pricing includes demand charges based on peak 15-minute consumption—often representing 30-70% of total bills. A single demand spike, even lasting seconds, establishes monthly charges that cannot be reversed.

Cloud latency = missed opportunities: By the time cloud systems detect a demand spike (30-90 seconds), alert operators (60-120 seconds), and initiate response (30-60 seconds), the 15-minute window has closed. Total response time: 2-4 minutes—far too slow to prevent demand charges.

Edge AI response: Local processing detects nascent demand spikes in 100-500 milliseconds and automatically curtails non-critical loads or activates battery storage within 2-3 seconds—preventing demand charges before they're established. Enterprises report $50,000-500,000 monthly savings from demand charge mitigation alone.

2. Equipment Protection: Preventing Costly Damage

Power quality events—voltage sags, harmonics, transients—occur in milliseconds and cause $150 billion annual equipment damage globally according to the Electric Power Research Institute. Traditional monitoring detects events after damage occurs.

Edge AI processes voltage and current waveforms at kilohertz frequencies, detecting anomalies in 1-10 milliseconds and triggering protective actions (circuit isolation, backup power activation) fast enough to prevent equipment damage. Manufacturers report 85-95% reduction in power-quality-related equipment failures.

3. Dynamic Grid Participation: Millisecond Market Opportunities

Grid services markets—frequency regulation, demand response, voltage support—pay premiums for sub-second response. Cloud-based systems cannot participate in these high-value markets due to latency constraints.

Edge AI enables participation in frequency regulation markets that require 4-second response times and pay $15-45/MW-hour—5-10x premium over standard demand response. A 1 MW flexible load can generate $130,000-400,000 annually through automated grid services participation.

Edge AI Architecture for Energy Monitoring

Effective Edge AI deployment requires purpose-built architecture balancing local intelligence with cloud-scale analytics. The reference architecture that leading enterprises deploy consists of four integrated layers:

Critical Design Principle: Edge devices must operate autonomously during cloud connectivity loss. Leading deployments maintain 30-90 days of local intelligence enabling continued optimization even during extended cloud outages—critical for industrial and healthcare facilities where energy reliability is non-negotiable.

Seven Real-Time Optimization Strategies

Edge AI enables optimization strategies impossible with cloud architectures. Here are seven proven approaches delivering measurable ROI:

Strategy 1: Predictive Demand Charge Avoidance

How it works: AI models analyze real-time consumption patterns, weather data, production schedules, and historical demand profiles to predict demand spikes 5-15 minutes in advance—enough time to curtail non-critical loads or discharge battery storage.

Implementation: Deploy gradient-boosted decision trees (XGBoost, LightGBM) or LSTM neural networks trained on 6-12 months of interval data. Models run every 30-60 seconds on edge gateways, triggering automated load curtailment when peak demand probability exceeds 70%.

Typical Results: 25-45% reduction in demand charges representing $3,000-30,000 monthly savings per facility

Case Example: A pharmaceutical manufacturing facility deployed predictive demand charge avoidance achieving 38% demand charge reduction ($127,000 annual savings) by automatically scheduling batch processes during off-peak periods and curtailing HVAC during predicted peaks.

Strategy 2: Real-Time Load Balancing Across Phases

How it works: Three-phase power systems often develop imbalances causing excess losses and potential equipment damage. Edge AI continuously monitors phase loads and automatically redistributes loads to maintain balance.

Implementation: Deploy reinforcement learning agents that control smart panel switches to dynamically reassign loads across phases. The system learns optimal load distribution patterns through trial-and-error while respecting equipment constraints.

Typical Results: 3-8% total energy savings, 15-25% reduction in neutral current, extended transformer life

Case Example: A data center deployed real-time phase balancing reducing total consumption by 5.2% ($340,000 annually) and eliminating transformer overheating issues that previously required $75,000 annual maintenance.

Strategy 3: Adaptive HVAC Optimization

How it works: HVAC represents 40-60% of commercial building energy consumption. Edge AI analyzes occupancy patterns, weather forecasts, thermal inertia, and comfort preferences to optimize HVAC operations in real-time—adjusting setpoints, staging equipment, and pre-cooling/heating based on predicted conditions.

Implementation: Deploy model predictive control (MPC) algorithms running on edge gateways with direct BACnet/Modbus control of HVAC equipment. Models incorporate building thermal models, weather forecasts, and occupancy predictions (via WiFi/camera sensors or calendar integration).

Typical Results: 20-35% HVAC energy reduction while maintaining or improving occupant comfort

Case Example: A 500,000 sq ft corporate campus deployed adaptive HVAC optimization achieving 28% HVAC energy reduction ($420,000 annually) while improving occupant satisfaction scores from 72% to 89% through personalized zone control.

Strategy 4: Equipment Anomaly Detection & Predictive Maintenance

How it works: Equipment degradation manifests as subtle changes in power consumption patterns—often detectable weeks before catastrophic failure. Edge AI analyzes high-frequency power signatures to identify emerging anomalies and schedule proactive maintenance.

Implementation: Deploy autoencoders or variational autoencoders (VAE) trained on normal equipment operation. Models reconstruct expected power signatures; reconstruction errors indicate anomalies. Integration with CMMS (computerized maintenance management systems) enables automated work order generation.

Typical Results: 45-70% reduction in unplanned downtime, 25-40% maintenance cost reduction, 15-30% equipment life extension

Case Example: An automotive parts manufacturer deployed equipment anomaly detection across 200+ CNC machines, reducing unplanned downtime by 62% (worth $1.8M annually) and cutting emergency maintenance costs by $420,000/year.

Strategy 5: Dynamic Renewable Integration & Storage Optimization

How it works: For facilities with on-site solar, wind, or battery storage, Edge AI optimizes when to consume, store, or export energy based on generation forecasts, grid prices, and consumption patterns—maximizing self-consumption and revenue.

Implementation: Deploy mixed-integer linear programming (MILP) solvers or deep reinforcement learning agents that optimize battery charge/discharge schedules. Models incorporate solar forecasts (using sky cameras and weather APIs), grid price signals, and predicted consumption.

Typical Results: 15-30% increase in solar self-consumption, 25-50% improvement in battery ROI, $0.03-0.08/kWh arbitrage revenue

Case Example: A logistics warehouse with 500kW solar and 1MWh battery deployed dynamic optimization increasing self-consumption from 43% to 71% and generating $95,000 annual arbitrage revenue through grid services participation.

Strategy 6: Precision Load Disaggregation & Equipment-Level Accountability

How it works: Using NILM (Non-Intrusive Load Monitoring) technology, Edge AI disaggregates total facility consumption into individual equipment profiles—enabling equipment-level tracking without installing hundreds of submeters.

Implementation: Deploy convolutional neural networks (CNN) or Graph Neural Networks trained to recognize equipment power signatures from aggregated consumption data. Modern NILM systems achieve 85-95% disaggregation accuracy for 20-50 equipment types from a single measurement point.

Typical Results: 90% reduction in sub-metering costs, equipment-level visibility enabling targeted efficiency improvements, 10-20% additional savings from identified waste

Case Example: A hospital deployed NILM-based disaggregation across 15 buildings, identifying $180,000 annual waste from equipment operating outside scheduled hours—corrected through automated controls with 12-month payback vs. $420,000 for traditional sub-metering.

Strategy 7: Automated Demand Response & Grid Services Participation

How it works: Edge AI automatically responds to grid events (demand response, frequency regulation, voltage support) by curtailing flexible loads or discharging storage—generating revenue while supporting grid stability.

Implementation: Integrate with utility/ISO APIs (OpenADR, IEEE 2030.5) for real-time event notification. Deploy multi-objective optimization models that balance operational needs with grid service revenue opportunities. Automated control ensures 4-second response required for premium markets.

Typical Results: $15-45/MW-hour for frequency regulation, $100-500/MW-year for capacity payments, $0.50-2.00/kWh for emergency demand response

Case Example: A cold storage facility with 2.5MW flexible load deployed automated demand response generating $340,000 annual revenue from frequency regulation while maintaining product temperature within ±0.5°C operational requirements.

Implementation Roadmap: 60-Day Deployment

Edge AI deployment follows an accelerated timeline compared to traditional cloud implementations. The 60-day roadmap proven across hundreds of enterprise deployments:

Phase 1: Assessment & Architecture (Days 1-15)

Objective: Understand current infrastructure and design Edge AI architecture

• Days 1-5: Energy audit—analyze utility bills, identify demand charges, assess current monitoring systems. Calculate baseline consumption patterns and identify optimization opportunities worth pursuing.
• Days 6-10: Infrastructure assessment—evaluate electrical panels, communication networks, IT/OT segmentation, cybersecurity posture. Identify edge gateway locations and sensor requirements.
• Days 11-15: Architecture design—select edge hardware (gateways, sensors, controllers), design communication topology, define security architecture. Develop phased deployment plan prioritizing high-ROI areas.

Deliverable: Detailed architecture document, equipment BOM, ROI projection, and deployment timeline

Phase 2: Pilot Deployment (Days 16-35)

Objective: Deploy working Edge AI system in representative pilot area

• Days 16-20: Sensor installation—deploy smart meters, NILM sensors, environmental sensors in pilot area (typically 15-25% of facility). Verify data quality and communication.
• Days 21-25: Edge gateway deployment—install and configure edge compute devices, deploy initial AI models, integrate with existing BMS/ERP systems.
• Days 26-30: Model training & tuning—collect 5-10 days baseline data, train facility-specific models, validate accuracy against ground truth.
• Days 31-35: Automated control activation—enable automated optimization (demand charge avoidance, HVAC optimization), monitor performance, adjust thresholds.

Deliverable: Working Edge AI system demonstrating 15-25% savings in pilot area

Phase 3: Validation & Expansion (Days 36-60)

Objective: Validate ROI and scale to full facility/enterprise

• Days 36-45: Performance validation—measure actual savings vs. baseline, document case studies, quantify non-energy benefits (equipment life extension, power quality improvement).
• Days 46-55: Full deployment—expand to remaining facility areas, deploy additional optimization strategies, integrate advanced features (grid services, predictive maintenance).
• Days 56-60: Enterprise rollout planning—develop multi-site deployment strategy, establish ongoing operations/monitoring framework, train facility teams.

Deliverable: Facility-wide Edge AI deployment achieving 30-40% energy savings, enterprise rollout plan

ROI Analysis: From Deployment to 40% Savings

Edge AI energy monitoring delivers quantifiable returns across multiple dimensions. Understanding the complete ROI picture—including direct savings, indirect benefits, and strategic value—is essential for justifying investment.

Direct Energy Savings Breakdown

For a typical 250,000 sq ft commercial/light industrial facility spending $500,000 annually on electricity:

Total Annual Savings: $155,000-250,000 (31-50% of baseline spending)

Indirect & Strategic Benefits

Beyond direct energy savings, Edge AI delivers substantial indirect value:

• Equipment Life Extension: Reduced thermal/electrical stress extends equipment life by 20-40%, deferring $50,000-300,000 in capital replacement costs
• Maintenance Cost Reduction: Predictive maintenance reduces emergency repairs by 50-70%, saving $30,000-150,000 annually
• Downtime Prevention: Power quality monitoring and equipment protection prevent production interruptions worth $100,000-1,000,000+ annually for manufacturing facilities
• Carbon Reduction: 30-40% energy savings translates to 15-20% Scope 2 emissions reduction, supporting decarbonization commitments and ESG goals
• Operational Intelligence: Equipment-level visibility enables continuous process optimization worth 5-15% additional efficiency gains

Investment Requirements

Typical deployment costs for 250,000 sq ft facility:

• Hardware (sensors, gateways, controllers): $80,000-150,000
• Software & AI Models: $40,000-80,000 initial + $15,000-30,000 annual SaaS
• Installation & Integration: $30,000-60,000
• Training & Change Management: $10,000-20,000
• Total Initial Investment: $160,000-310,000

Payback Analysis

With annual savings of $185,000-400,000 (including indirect benefits) against investment of $160,000-310,000:

• Simple Payback: 9-20 months
• 5-Year NPV (7% discount rate): $600,000-1,400,000
• 5-Year IRR: 75-150%

Enterprise Case Studies: Proven Results

Real-world deployments demonstrate Edge AI's transformative impact across diverse sectors:

Case Study 1: Pharmaceutical Manufacturing - 42% Total Cost Reduction

Challenge: A 400,000 sq ft pharmaceutical facility faced $1.2M annual electricity costs with 60% attributed to demand charges. Production schedules required 24/7 clean room operations with stringent temperature/humidity control.

Solution: Deployed Edge AI with 150 IoT sensors monitoring equipment loads, 12 edge gateways running predictive demand models, and direct BACnet control of HVAC and process equipment. Implemented predictive demand charge avoidance, adaptive HVAC optimization, and equipment-level disaggregation.

Results After 18 Months:

• 42% total electricity cost reduction ($504,000 annual savings)
• 65% demand charge reduction through predictive avoidance and load shifting
• 28% HVAC energy reduction while improving clean room stability (±0.3°C vs. ±0.8°C previously)
• $180,000 avoided maintenance through predictive equipment monitoring
• 11-month payback on $450,000 investment

Case Study 2: Data Center - 32% PUE Improvement

Challenge: A 5MW data center struggled with Power Usage Effectiveness (PUE) of 1.68 despite modern equipment. Cooling represented 42% of total energy consumption, and demand charges added $35,000-65,000 monthly.

Solution: Deployed Edge AI with real-time monitoring of IT load distribution, cooling system performance, and external weather conditions. Implemented dynamic cooling optimization, phase load balancing, and predictive workload management integrated with orchestration systems.

Results After 12 Months:

• PUE improvement from 1.68 to 1.14 (32% reduction in infrastructure energy per compute)
• $890,000 annual energy savings on $2.8M baseline spending
• Zero unplanned downtime from power/cooling issues (vs. 3-4 annual events previously)
• $240,000 annual grid services revenue through frequency regulation participation
• 7-month payback on $520,000 investment

Case Study 3: Cold Storage - Grid Services Revenue + Efficiency

Challenge: A 150,000 sq ft cold storage facility spent $680,000 annually on refrigeration, with limited demand charge management and no grid services participation despite 3MW flexible load capacity.

Solution: Deployed Edge AI with thermal modeling of storage zones, outdoor temperature integration, and automated compressor staging. Implemented pre-cooling strategies, phase balancing, and OpenADR-based grid services participation while maintaining ±1°C temperature requirements.

Results After 15 Months:

• 38% refrigeration energy reduction through thermal inertia optimization and efficient staging
• $258,000 annual energy savings
• $340,000 annual grid services revenue from frequency regulation and demand response
• Zero temperature excursions during grid events (vs. concerns about product safety)
• 5-month payback on $250,000 investment (hardware costs lower due to simpler facility)

Case Study 4: Hospital Campus - Resilience + Efficiency

Challenge: A 600-bed hospital campus prioritized reliability over efficiency, maintaining 30% operating reserve that inflated demand charges. Annual energy costs exceeded $3.2M with limited visibility into equipment-level consumption.

Solution: Deployed private Edge AI infrastructure (on-premise processing for HIPAA compliance) with comprehensive monitoring across 15 buildings. Implemented predictive demand management, equipment anomaly detection, and automated emergency response integration with backup generators.

Results After 24 Months:

• 34% energy cost reduction ($1,088,000 annual savings)
• Maintained 100% uptime for critical systems while reducing operating reserves to 12%
• $420,000 avoided equipment replacement through early fault detection across HVAC, boilers, and chillers
• 18% Scope 2 emissions reduction supporting sustainability commitments
• 14-month payback on $1.2M investment (higher costs due to compliance requirements and campus scale)

Overcoming Edge AI Deployment Challenges

While Edge AI delivers compelling ROI, successful deployment requires addressing predictable technical, organizational, and security challenges:

Challenge 1: Cybersecurity & OT Network Segregation

Risk: Edge devices with equipment control authority represent potential attack vectors. Compromised edge AI could cause physical damage, safety incidents, or operational disruptions.

Solution Framework:

• Network Segmentation: Deploy edge AI in isolated OT networks separated from IT networks via firewalls/data diodes. Use unidirectional gateways for cloud communication.
• Zero Trust Architecture: Implement device authentication, encrypted communication (TLS 1.3+), and role-based access control for all edge devices.
• Secure Boot & TPM: Use edge hardware with secure boot, TPM chips, and signed firmware to prevent unauthorized code execution.
• Behavioral Monitoring: Deploy anomaly detection on edge device behavior—flag unexpected network traffic, API calls, or control actions.
• Offline Operation: Design systems to operate during cloud connectivity loss, reducing attack surface and ensuring resilience.

Reference: The CISA ICS Security Guidance provides comprehensive frameworks for OT cybersecurity.

Challenge 2: Model Accuracy & Facility-Specific Training

Risk: Generic pre-trained models often underperform in specific facility contexts due to unique equipment, operating patterns, and constraints—leading to suboptimal decisions or operator distrust.

Solution Framework:

• Transfer Learning: Start with pre-trained models (trained on similar facilities) and fine-tune using facility-specific data—achieving 80-90% target accuracy within 1-2 weeks.
• Continuous Learning: Implement online learning where models improve continuously using real operational data and feedback.
• Human-in-the-Loop: Deploy AI recommendations with operator review for first 2-4 weeks, then gradually increase automation as confidence builds.
• Explainable AI: Use SHAP values, attention mechanisms, or rule extraction to explain model decisions—building operator trust and enabling debugging.
• Validation Testing: Maintain 10-20% holdout data for continuous model validation; automatically alert when accuracy degrades below thresholds.

Challenge 3: Integration with Legacy Building Management Systems

Risk: Many facilities operate 10-20 year old BMS systems using proprietary protocols, limited API access, or incompatible communication standards—blocking automated control.

Solution Framework:

• Protocol Translation: Deploy edge gateways with multi-protocol support (BACnet, Modbus, LonWorks, proprietary) enabling standardized control interfaces.
• Shadow Mode Operation: Initially deploy AI in monitoring-only mode providing recommendations to existing BMS—demonstrating value before seeking control authority.
• Parallel Deployment: Install modern controllers alongside legacy systems for new/replacement equipment—gradually migrating to AI-controlled infrastructure.
• API Development: Work with BMS vendors to develop API access—many legacy systems can be upgraded with software updates enabling modern integration.
• Sunset Planning: Use Edge AI deployment to justify BMS modernization—upgrading one building/zone at a time with compelling ROI from early deployments.

Challenge 4: Organizational Change Management

Risk: Facility operators, engineers, and managers may resist AI-driven control due to job security concerns, distrust of algorithms, or preference for manual control based on experience.

Solution Framework:

• Co-Development: Involve facility teams in AI development from day one—their operational knowledge improves models while building buy-in.
• Augmentation Framing: Position AI as decision support and routine task automation—freeing operators for strategic work, not replacing them.
• Visible Quick Wins: Deploy high-value, low-risk optimizations first (demand charge avoidance, phase balancing)—building confidence through demonstrated success.
• Comprehensive Training: Provide hands-on training on AI system operation, interpretation, and override—ensuring operators feel empowered, not bypassed.
• Incentive Alignment: Tie bonuses/recognition to AI-enabled performance improvements—aligning personal incentives with deployment success.

Challenge 5: Scalability Across Multi-Site Enterprises

Risk: Successful pilot deployments often struggle to scale across 10-100+ facilities due to site heterogeneity, resource constraints, and operational complexity.

Solution Framework:

• Standardized Architecture: Define reference architecture with approved hardware, software, and integration patterns—enabling repeatable deployments.
• Facility Clustering: Group similar facilities (by size, equipment, operations) and develop optimized models per cluster—reducing per-site customization.
• Centralized Operations: Establish enterprise AI operations center managing model updates, performance monitoring, and troubleshooting across all sites.
• Phased Rollout: Deploy 3-5 sites per quarter with staggered schedules—preventing resource bottlenecks and enabling knowledge transfer between waves.
• Local Champions: Identify facility-level AI champions at each site—providing local expertise and reducing dependency on central teams.

The Future: Autonomous Energy Management

Edge AI energy monitoring is evolving toward fully autonomous energy management where buildings and facilities self-optimize with minimal human intervention. Five trends will define the next 3-5 years:

Trend 1: Federated Learning for Privacy-Preserving Optimization

What's coming: Facilities will collaboratively train AI models without sharing sensitive operational data. Edge devices train local models, share only model updates (not data) to cloud aggregators, then receive improved models trained across thousands of sites.

Impact: Enterprises gain benefits of massive training datasets (improving accuracy 15-30%) while maintaining data privacy and competitive confidentiality. Particularly valuable for healthcare, defense, and financial sector deployments requiring strict data controls.

Timeline: Early commercial implementations 2025-2026; mainstream adoption 2027-2028

Trend 2: Multi-Agent Systems for Facility-Wide Coordination

What's coming: Rather than centralized optimization, facilities will deploy networks of specialized AI agents—each managing specific systems (HVAC, lighting, production equipment) that negotiate and coordinate for holistic optimization.

Impact: More robust, adaptable systems that handle complexity better than monolithic approaches. If one agent fails, others continue operating. New equipment can be integrated by adding agents rather than retraining entire systems.

Timeline: Research prototypes now; commercial products 2026-2027; widespread deployment 2028-2030

Trend 3: Self-Learning Systems Requiring Zero Configuration

What's coming: Next-generation Edge AI will automatically discover equipment, learn operational patterns, identify optimization opportunities, and deploy improvements—without human configuration or training data labeling.

Impact: Deployment costs drop 60-80% and timelines shrink from 60 days to 5-10 days. Small facilities (currently uneconomical) become viable candidates for AI deployment, expanding market 10x.

Timeline: Early implementations 2026-2027; mature products 2028-2030

Trend 4: Peer-to-Peer Energy Trading via Edge AI

What's coming: Buildings with generation and storage will autonomously trade energy with neighbors via blockchain-based transactive energy platforms—Edge AI managing bidding, dispatch, and settlement in real-time.

Impact: Emergence of local energy markets where buildings act as prosumers (producer-consumers). Facilities earn $0.05-0.12/kWh premiums vs. grid export prices through local trading. Grid resilience improves through distributed coordination.

Timeline: Pilot programs 2025-2027 (Brooklyn Microgrid, LO3 Energy); regulatory frameworks 2027-2029; mainstream adoption 2030+

Trend 5: Integration with Digital Twins for Predictive Operations

What's coming: Edge AI will integrate with facility digital twins—virtual replicas simulating building thermodynamics, equipment performance, and occupant behavior—enabling "what-if" scenario testing before implementing real-world changes.

Impact: Risk-free optimization testing, faster commissioning of new strategies, and predictive maintenance planning weeks in advance. Facilities test 100+ optimization scenarios overnight, implement best strategy next day.

Timeline: Early adopters 2025-2026; integrated platforms 2027-2029; standard practice 2030+

Your Edge AI Action Plan

Edge AI energy monitoring delivers transformational results, but success requires structured execution. Here's your action plan based on role and organizational context:

For Facility & Energy Managers

For CIOs & Technology Leaders

For CFOs & Financial Leaders

For Sustainability & ESG Leaders

Next Steps: Getting Started

Ready to deploy Edge AI energy monitoring? Here's how to begin:

1. Assessment Call: Schedule consultation with Edge AI specialists to review your facility portfolio and quantify opportunity. Book a 15-minute assessment.
2. Facility Audit: Conduct detailed energy audit of pilot facility—analyzing consumption patterns, demand charges, and infrastructure readiness.
3. Architecture Design: Develop customized Edge AI architecture aligned with your operational requirements, IT standards, and budget.
4. Pilot Deployment: Execute 60-day pilot demonstrating ROI and building organizational confidence for enterprise rollout.
5. Enterprise Scaling: Roll out proven solution across facility portfolio with standardized architecture and centralized operations.