Transforming Data Into Maintenance Insights

Today’s supermarket, restaurant and convenience store operators have an abundance of data at their fingertips. Most utilize facility management systems and controls to monitor refrigeration, HVAC, lighting and energy management. These platforms give them ability to respond to alarms that could impact customer comfort and food quality. But alarms are only the “tip of the iceberg” when it comes to this data’s potential usefulness. In a recent E360 article, we discussed how operators can transform this data into maintenance insights.

While many companies spend their time tracking, prioritizing and responding to alarms that need immediate attention, owners and operators have relatively limited visibility into overall operational status. But with deeper analytics of available data, operators can look “beneath the hood” of key systems and gain access to insights that could impact them in the future — insights that could potentially transform maintenance activities from a primarily reactive approach to a more condition-based, analytics-driven model.

The difference between “urgent” and “important”

One way to visualize the role of operational analytics in maintenance activities is by prioritizing maintenance events according to their urgency or importance. Maintenance events and operational decisions can be divided into four basic categories:

• • Don’t roll a truck (no action required)
  • Roll a truck soon (plan to take action)
  • Roll a truck now (take action now)
  • Take steps to improve (address at next scheduled maintenance)

Using the iceberg analogy, urgent issues represent events that you will need to respond to immediately — those that lie above the surface. Below the surface, you’ll find issues where analytics platforms can help operators make maintenance decisions based on their potential business impacts. Analytics can help identify issues that, while not urgent, are highly important — and may have otherwise gone unnoticed.

These insights often reveal areas of improvement that could either be addressed during scheduled service intervals or when the equipment or system condition indicates the need to address a potential issue. Armed with this knowledge, operators can receive advance notice of certain performance issues that may soon impact them.

Drive performance across the enterprise

The role of analytics within a maintenance framework can be extrapolated across an enterprise to maximize its potential. Drawing from a combination of equipment sensors and control system data, performance analytics can provide store operators and enterprise managers deeper insights for:

• • Real-time and historic operating conditions in their facilities and systems
  • Pressure, temperature and energy data to compare to established benchmarks
  • Enterprise- and store-level dashboards and prioritized notifications

For example, analytics allows for display case performance analysis based on temperature sensor data. Data may detect an anomaly in case temperature deviations that, while still within safe ranges, could indicate a larger performance issue. Instead of being notified with an urgent alarm, operators have advance notice to investigate issues at their discretion — and even preempt a potentially larger issue.

Enterprise operational dashboards can also be configured to display these insights and provide managers with visual snapshots of urgent and pending issues across their store networks — even enabling investigation into specific assets in their respective facilities.

If you’re ready to see what lies below the surface of your operational data and realize the true potential of analytics, contact Emerson to speak to one of our enterprise data analytics experts.