How a Digital Transformation Strategy Turned a Long-Standing Power Concern into Documented Evidence
The Challenge
For years, a manufacturing facility suspected that incoming power quality was contributing to equipment instability and unplanned downtime. Although personnel knew voltage problems existed, the facility lacked the historical data needed to quantify the issue, identify when it occurred, or demonstrate that incoming power was outside acceptable limits. At the same time, one of the facility’s machines was experiencing repeated breakdowns. The equipment OEM had been unable to establish a definitive root cause. Two of the most significant areas of concern were the environmental conditions inside the control cabinet and the quality of the incoming power.
The Digital Transformation Strategy
As part of its broader digital transformation strategy, the facility recognized that operational data needed to become a managed business asset. The strategy followed a practical progression.
Identify. Standardize. Connect. Contextualize. Visualize. Act.
The goal was not to collect data for the sake of collecting it or to create more dashboards. The goal was to establish a trusted operational data foundation that could transform machine and process signals into actionable information. Deploying the Canary Historian was a key part of that strategy. It gave the facility the ability to capture high-resolution industrial data over time, retain the original values, and make the information available for troubleshooting, analysis, and operational decision-making.
The Solution
As part of the facility’s NCC installations, VeriSafe power-monitoring modules were included in the equipment specification. GPA configured the modules, integrated them with the PLC, and historized the voltage data in Canary. Over a two-week monitoring period, Canary continuously recorded incoming voltage conditions. This allowed the facility to compare voltage behavior with machine events, environmental conditions inside the control cabinet, and the utility provider’s contractual power-quality requirements. The analysis showed voltage swings exceeding the utility provider’s contractual tolerance of ±5%. A condition that had previously been based on observations and assumptions was now supported by documented, time-stamped historical evidence.
Turning Data into Actionable Information
The value of the implementation was not simply that voltage data was collected. The value came from giving the data context and connecting it to a business and operational problem. The facility could now answer important questions:
When did the voltage excursions occur?
How far did the voltage move outside the acceptable tolerance?
How long did each excursion last?
Which equipment was operating or experiencing problems at the time?
Were voltage excursions occurring before or during machine faults?
Were the conditions isolated events or part of a recurring pattern?
By answering these questions, the historian transformed raw voltage readings into information that site leadership, maintenance personnel, engineers, equipment vendors, and the utility provider could use.
The Result
The facility moved from suspecting a power-quality problem to having the evidence needed to act. Because the voltage data was stored and trended in Canary, site leadership can now demonstrate when power-quality excursions occurred and how far voltage moved outside the required tolerance. The facility can take this documented evidence to the utility provider and begin pursuing corrective action. The implementation also improved the equipment troubleshooting process. Instead of relying primarily on isolated observations, OEM opinions, or tribal knowledge, the facility can compare machine failures with historical operating, environmental, and power conditions. This does not automatically prove that every equipment failure was caused by incoming power. It does, however, provide the objective information needed to investigate the relationship, eliminate uncertainty, and make better-informed decisions.
Future Opportunity: From Reactive Analysis to Proactive Response
The next phase can build upon this digital foundation by using Canary to detect abnormal power conditions before they contribute to additional equipment problems. Power-quality and statistical rules could be established to identify conditions such as:
Voltage exceeding established tolerances
Sustained undervoltage or overvoltage
Repeated voltage fluctuations within a defined period Increasing frequency or severity of power-quality events
Abnormal power behavior preceding equipment faults
When these conditions are detected, automated notifications could alert site leadership, maintenance, or engineering personnel. This would allow the organization to investigate and respond before more machines are affected. The facility’s digital transformation journey can therefore continue to mature:
Capture the data.
Retain the history.
Add operational context.
Identify meaningful conditions.
Notify the appropriate people.
Enable timely corrective action.
Measure whether the action produced the intended result.
Business Impact
This project demonstrates that digital transformation does not begin with artificial intelligence or another dashboard. It begins by creating a trusted data foundation and connecting that data to a real operational problem. By combining VeriSafe power monitoring, PLC integration, and Canary Historian, the facility gained:
Objective evidence of incoming power-quality problems
Better information for equipment root-cause investigations
Documentation to support discussions with the utility provider
Greater visibility into conditions affecting machine reliability
A foundation for proactive monitoring and automated notification
Reduced dependence on assumptions, spreadsheets, and tribal knowledge
A repeatable model for turning other operational data into actionable information
The Conclusion
The power-quality issue became visible because the facility had already committed to a broader digital transformation strategy. That strategy created the infrastructure and discipline needed to capture operational data, retain its history, provide context, and turn it into information people could act upon. The result was more than a voltage trend. It was a practical demonstration of what digital transformation should accomplish: turning trusted operational data into better decisions and measurable action.
