Balancing operational costs and business efficiency continues to be a significant challenge for enterprises everywhere. In the event of equipment failure, the resulting revenue and productivity losses pose dramatic consequences. To maintain modern flexibility, organizations must move beyond a break-fix maintenance strategy—but how?
In DBTA’s latest webinar, Stop Fixing, Start Predicting: Mastering Predictive Maintenance with IoT Data Analytics and AI, experts from OpenText explained how to implement a predictive maintenance program—shaped by the latest advancements in AI, machine learning (ML), and data analytics—to ensure that business operations remain on, available, and drive successful outcomes.
Phil Schwarz, industry strategist for energy and resources, OpenText, explained that “as we move to a world of autonomous decisions, we are inspired to reimagine what information can really do.” OpenText’s information management software works to achieve exactly that, a reimagined approach to extracting value from information.
This is particularly relevant to the world of reactive maintenance—or an equipment maintenance strategy where maintenance is only performed once an asset has broken down, according to IBM. The challenges associated with reactive maintenance are plentiful, ranging from unplanned downtime costs to high maintenance costs, inefficient resource allocation, and lost productivity.
Ultimately, there is a better way, noted Schwarz, with predictive and prescriptive maintenance—or a process that combines data about hardware, software, and service components in order to determine the maintenance requirements for mechanical assets, according to OpenText's definition.
Through remote IoT and sensor data monitoring powered by advanced analytics and ML, enterprises can predict potential failures within a system to prevent downtime. Prescriptive maintenance then prescribes preventative measures and recommended next actions to avoid issues.
“Overall, predictive maintenance offers a 10-fold ROI, transforming maintenance from a reactive burden into a strategic advantage,” explained Schwarz. “It keeps operations running smoothly—whether in factories, hospitals, or power plants—resulting in happier employees, satisfied customers, and better shareholder returns.”
Implementing a predictive and prescriptive maintenance strategy can result in:
- 30-50% reduced downtime
- 20-30% cost savings
- 20-40% improved asset lifespan
- 25-30% enhanced labor efficiency
- 70% fewer breakdowns
- 10x ROI
“You might be thinking, this sounds too good to be true, and if…[not], why doesn’t everyone do this?” posed Virgil Dodson, product marketing director, analytics cloud, OpenText. “While the promise of predictive and prescriptive maintenance is lucrative, the path to getting there is challenging.”
This path consists of collecting millions of data points from sensors in real time that must not only be amassed, but also made sense of—without overwhelming your IT infrastructure or your staff, according to Dodson. From data integration to scalability issues, latency, model accuracy, inadequate infrastructure resources, and security and data privacy concerns, predictive and prescriptive maintenance—though beneficial—is difficult to achieve.
Enter OpenText, offering a portfolio of solutions for predictive and prescriptive maintenance that minimizes enterprise disruption by anticipating failures before they happen. Through a combination of the OpenText Analytics Database (Vertica), OpenText Data Discovery (Magellan Data Discovery), and OpenText Intelligence (Magellan BI and Reporting) solutions, OpenText delivers to enterprises:
- Powerful ML capabilities with over 650 functions operating faster than traditional databases
- A complete picture of maintenance operations with a robust solution for big data, data preparation and enrichment, analytics, and enterprise reporting
- Visual ad-hoc analytics and data discovery with drag-drop ease that empowers employees
- Better understanding of the equipment and root cause analysis of downtimes
Schwarz, Dodson, and Ryan Sickles, lead product manager, core analytics database, OpenText, then engaged in a roundtable discussion exploring the various nuances of predictive and prescriptive maintenance.
For the full, in-depth webinar, featuring customer examples, a Q&A, and more, you can view an archived version of the webinar here.