Uptake Recognized for Industry-Leading Asset Maintenance Analytics

Verdantix recently published its “Smart Innovators: Maintenance Analytics for Heavy Industries” report, which recognized Uptake for comprehensive capabilities in five areas: fault detection and diagnostics, asset failure prediction, lifecycle costing, risk scoring and management, and demand forecasting.

Each of these areas of analytics helps maintenance and reliability teams to better understand, forecast, and report performance on their critical equipment.

1. Fault Detection & Diagnostics gives asset managers data-backed visibility into critical equipment like pumps and motors so that they can monitor performance. Using fault detection and diagnostics, maintenance and reliability teams can set rules or thresholds to proactively take corrective action on impending asset issues.

2. Asset Failure Prediction uses past failures on critical equipment to calculate when a failure will occur. Through the combination of data, subject matter expertise, and industry content, asset managers taking advantage of predictive insights on asset failure are able to prioritize maintenance based on the likelihood and timing of failure.

3. Lifecycle Costing allows asset managers to see and model costs on their critical equipment, offering them opportunities to re-evaluate and reduce operational, maintenance, and procurement costs without an impact on reliability.

4. Risk Scoring & Management provides maintenance and reliability teams with insight into the probability and scope of critical equipment failure. In view of this risk, industrial sites can manage their workers and processes to ensure safe, compliant, and productive operations.

5. Demand Forecasting readies asset and procurement managers in the likely event of failure to ensure that they have the necessary spare parts or equipment. The preparation smoothes over potential disruptions in industrial operations and minimizes downtime.

More broadly, Verdantix defines asset maintenance analytics software as: "Software tools and services used to explore and gain insights into data related with assets and workers, with the aim of informing decisions to optimize maintenance, determine asset strategy, and drive cost savings related to the upkeep of firms’ industrial facilities."

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For process-intensive industries like energy, utilities, manufacturing, and mining, maintenance analytics enable teams across the organization to make smarter, more sustainable, and productive business decisions on their critical assets.

Growing Need for Maintenance Analytics

In its survey of managers in maintenance, engineering, and operations at asset-intensive companies, the report from Verdantix found that investment in maintenance analytics would increase over the coming years. One of the reasons asset managers cited was the need to move from reactive to proactive maintenance. Maintenance guidelines from original equipment manufacturers (OEMs) deliver a general overview for asset performance management, but often imprecisely identify the reasons for failure, and only after a breakdown has occurred.

Also prompting this adoption of maintenance analytics and industrial intelligence are changes in the industrial workforce. Challenges with recruitment and retention in the heavy industries and skill shortages have placed a premium on proactive maintenance. By empowering maintenance and operations teams with industrial intelligence before issues arise, shorthanded staff can focus their attention on those critical asset issues that matter most.

The pandemic only reinforced that need. Remote solutions have provided companies with cost-effective asset monitoring and management as they faced personnel shortages and social distancing measures. Telework and distributed teams re-upped the need for data-savvy maintenance teams to build out asset performance management and intelligence strategies, with remote monitoring coming at the expense of customary preventative maintenance intervals.

Data Management: First Stepping-Stone or Stumbling Block for Maintenance Analytics?

Along with the trends in talent and digital skills that asset-intensive industries are coming up against today, a persistent challenge – and cornerstone – of digital transformation initiatives for maintenance analytics is data management. Many times though, data sits in disparate and proprietary systems, nonstandard and incorrect forms, sometimes even on individual spreadsheets and personal computers.

Beyond the core collectors and traditional users of that data, typically automation, control, and database historian administrators, non-experts looking to use operational technology (OT) data can leave better decisions and money on the table.

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When unified, data sits in an organization’s cloud environment, open to enterprise use for maintenance analytics, as well as reporting, monitoring, and application development. This data can then be leveraged as industrial intelligence to support business and environmental, social, and corporate governance (ESG) initiatives.

AI-Driven Asset Maintenance Analytics

With data at their disposal, asset-intensive companies can unearth great value. Industrial intelligence, including asset maintenance analytics, is helping teams across the heavy industries to make their routine decisions with data-backed certainty and avoid the disruption of asset failures.

That way, they can take care of the small things before they become bigger things. Industrial intelligence enables companies to make asset performance management even more manageable.

Put your data to work for maintenance analytics.

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