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Lewis Services Achieves 90% Efficiency Gains

With ProArch’s Dataware Data Platform

Lewis Services

About Lewis

Lewis Services is one of North America’s largest vegetation management companies. With over 4,000 staff members and 200 customers across the country, Lewis plays a critical role in the US energy infrastructure.

 

Solutions Used

 

 

Situation: Painful Invoice Processes & Time-Consuming Timesheets

Without a centralized system, Lewis’s payroll processing and customer invoicing were challenging, slow, and manual. To put it in perspective, the company processed 86,000 spreadsheets for payroll each year. This required nearly 45 full-time employees to complete, and the General Foreman had to enter the same data four times.

“The general foremen were being pulled in so many directions that it was causing inefficiencies in operations,” says Huntley Hedrick, VP of IT at Lewis.

Weekends of time-consuming work made for unhappy employees, payroll delays, slow revenue realization, and poor customer experiences.

 

Solution: Data Platform & Custom Application

Using ProArch’s Dataware Data Platform, the team integrated the HR and ERP systems into a data warehouse and then developed a custom application called “Tiempo.”

The data modeling in the background simultaneously calculates payroll and invoices based on customized rules by location, job type, pay rate, employee, and start/end time. 

 

Results: A Game-Changer All Around

  • Lewis has experienced a 90% reduction in the overall effort with a clear, streamlined process for time entry, timesheet creation, and approval.
  • Advanced reporting capabilities using data from different systems.
  • Before, Lewis experienced a high volume of invoice adjustments. Now, it is a 3% adjustment rate.
  • Customers have a better experience, and Lewis can realize revenue faster.
  • Lewis owns the tool so they can avoid future costly and complex ERP upgrades and customization.

For the general foremen, Tiempo has been transformational. Hedrick added, “What I’ve observed with the rollout of Tiempo has been greater than anything that I’ve ever seen in my career with how accepted it’s been in such a short amount of time.”

Laying the Groundwork for AI-Powered Predictive Maintenance in Power Generation

About

A large-scale power generation facility in Dover, New York, faced significant challenges with its reactive approach to equipment monitoring. Operational data from AVEVA PI was manually exported for offline analysis, creating delays between data collection and insight and limiting real-time visibility into asset health.

Services

Challenge
  • Operational data exported manually from AVEVA PI for offline analysis
  • Delays between data collection and actionable insight
  • No real-time visibility into asset health
  • Limited scalability across assets
  • Early detection of equipment issues was nearly impossible

The organization aimed to move from reactive, spreadsheet-based analysis to proactive, AI-driven predictive insights to improve operational efficiency, optimize energy output, and reduce unplanned downtime.

Solution

As a trusted technology partner, ProArch proposed an AI Proof of Value (PoV) to demonstrate how a modern, real-time data platform could unlock predictive maintenance capabilities.

Building on deep domain knowledge of the environment, ProArch executed a 2-week ImpactNOW Proof of Value, leveraging Microsoft Fabric Real-Time Intelligence to process sensor data instantly instead of relying on offline methods.

Building a Real-Time Data and AI Foundation

  • Monitored 3 critical pumps and 4 associated sensors, selected based on operational importance and failure risk
  • Replaced manual data exports with direct ingestion from AVEVA PI into Microsoft Fabric
  • Implemented real-time anomaly detection for pressure, vibration, and flow deviations
  • Built dashboards comparing live pump performance against manufacturer performance curves
  • Calibrated performance curves using historical operating data specific to plant conditions
  • Validated AI models by cross-referencing detected anomalies with historical maintenance logs

Technical Foundation

  • Leveraged historical and real-time data from AVEVA PI
  • Implemented a scalable Microsoft Fabric architecture using Azure Event Hub, Container Apps, and Lakehouse
  • Demonstrated how integrated AI can identify patterns, forecast performance, and support proactive maintenance decisions
Result

Early Results & Measurable Operational Impact

The Proof of Value successfully demonstrated how shifting from offline analysis to Microsoft Fabric Real-Time Intelligence delivers immediate operational value.

  • Validated Accuracy: Historical back-testing correctly identified known past anomalies, validating the AI models.
  • Real-Time Operational Visibility: Delivered a live view of pump health against manufacturer benchmarks—eliminating manual analysis and delayed insights.
  • Scalable Foundation: Established an architecture capable of expanding from 3 pumps to the entire facility.

Positioning the Plant for Predictive Maintenance at Scale

This initial phase created the foundation for a unified, AI-enabled data platform capable of supporting predictive maintenance at scale.

Working with ProArch, the organization is now positioned to realize their long-term strategic goals:

  • 50% reduction in unplanned downtime through predictive alerts.
  • 25% reduction in maintenance costs by moving from schedule-based to condition-based maintenance.
  • A clear path to expanding AI-driven insights across assets and systems
Read the full story

Industry

  • Power Generation

Challenge

  • Manual AVEVA PI data exports led to delayed insights, no real-time visibility into asset health, and limited scalability.

Solution

  • A 2-week ImpactNOW AI Proof of Value using Microsoft Fabric Real-Time Intelligence to enable real-time data processing and anomaly detection.

Result

  • Real-time operational visibility, validated AI accuracy through historical back-testing, and a scalable foundation for predictive maintenance at scale.

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