Unplanned outages, equipment running inefficiently, and downtime for repairs can cause billions of dollars a year of added costs in manufacturing and other asset- intensive industries. Asset reliability management is the process of ensuring that critical equipment, machinery, and infrastructure is working as intended and is available when needed.

Over the past decade, many enterprises have focused on developing data-driven strategies to predict and prevent failures. From the C-Suite to the plant floor, organizations have invested heavily in digital transformation. However, industry research found that many teams still spend more than 80 percent of their efforts on gathering and organizing data—as opposed to analyzing and applying insights from it.

This level of effort is unsustainable and stands in the way of realizing the potential of your reliability and predictive maintenance investments. But new asset reliability solutions make it easier to bridge the data gap, uncover insights, and eliminate surprises.

Barriers to Digital Transformation

Why is data capture and analysis for asset reliability so much work?

There are several compounding factors. First, older equipment isn’t connected to IoT systems and the cost of sensorization and networking these assets is high, meaning many teams still rely on manual condition monitoring. At the same time, many industries are facing labor shortages, aging workforces, and high turnover; they need their maintenance team focused on more skilled and engaging work rather than data capture. And finally, many areas in industrial facilities are unsafe for people while operational.

The result is that data capture is a dull and dangerous job that increasingly isn’t getting done. Predictive maintenance requires data at scale, and the bottom line is you don’t have that data.

Automating Through the Data Gap

Getting through this data gap requires a new way to think about automation. Frequently, the focus is processing data efficiently—using AI to generate insights for asset reliability management, for example. But this doesn’t solve the underlying bottleneck. So how do you effectively automate data collection without breaking the bank or overtaxing your vital teams?

Today, agile mobile robots enable dynamic sensing—automating industrial inspection rounds and gathering  asset data when and where it’s needed. This approach helps alleviate the underlying challenges of data capture.

  • Real-Time Data on Legacy Equipment: Robots ensure repeatable and standardized data capture—performing the same inspections consistently on your schedule—making it easier to predict issues without extensive IoT installations.
  • Augmented Workforce: Employees would rather focus on high-value tasks. Using robots for industrial inspections frees employees up to perform analysis, conduct repairs, and solve problems.
  • Safe Conditions: Existing infrastructure may contain dangerous environments—active work cells, areas with high voltage, deafening noise, and other occupational hazards. Robots are able to enter these hard-to-reach and unsafe areas of the facility without waiting for off-hours or routine shutdowns.

With Spot, we built an asset reliability solution that takes the guesswork out of predictive maintenance. First, Spot has the advanced mobility and autonomy to reliably navigate your facility. Second, Spot is equipped with sensors for visual, thermal, and acoustic inspections to capture the relevant asset data. Orbit, our software solution, enables fleet management, teleoperation, and data review, as well as easy integration to existing EAM software tools. And finally, our expert support and service teams make implementing and scaling with Spot simple.

Unblocking Asset Reliability KPIs

With over 1,000 robots deployed, our customers are unblocking their data pipeline and converting insights into action. To date, customers have inspected over 337,000 assets and started moving the needle on their asset reliability KPI metrics.

With Spot and dynamic sensing, you can detect issues before they escalate—improving mean time between failures. You can monitor known issues to better plan future maintenance—extending mean time between repairs. And you can increase the overall lifespan of your assets with data-driven predictive and proactive maintenance. 

What can you accomplish with better data? Watch our on-demand webinar and start empowering your asset reliability management program with better data today.