Industrial firms have long relied on technology to maintain operational assets. The shift to EAM technology has advanced the wide-ranging attempt to maximize production efficiency by proactively monitoring asset condition and performance. New research provides evidence of this reality by pointing to the manufacturing industry as holding the largest share of the EAM market in the months before the COVID-19 pandemic hit.
In a year predominantly characterized by uncertainty, industry-wide interest in EAM tools persisted — only this time, the appeal of smart technology has been tied to a growing need for lucrative asset maintenance plans.
Organizations pursuing greater operational growth are turning to next-generation EAM systems as they look to firmly integrate predictive maintenance into their plans of action. The burgeoning interest in advanced systems prompts EAM providers to expand their product offerings in order to facilitate the posterity of superior — and most of all, predictive! — asset management.
The Ample Rewards of Digital Transformation Abound
There is one thing that firms trying to maintain optimal asset performance can’t contest today: the chances to perpetuate efficient asset management are considerably higher once investing in frontier technology. This shift to digital asset management is primarily driven by the imminent “changes in maintenance philosophy,” industry experts say.
With asset managers finding the need to predict downtime events more imperative, many of them reject the idea of traditional asset maintenance and take advantage of the rising demand for cutting-edge EAM systems. The advances in EAM technology are projected to remove the divergence between preventive and predictive asset maintenance practices, encouraging industry players to wisely keep mechanical assets running while eliminating cost pressures.
For now, EAM providers are rallying firms to act on the need for predictive asset management by proposing innovations that stimulate an industry-wide digital transformation. The rich rewards of digital transformation within the EAM market are raising, from an expert view, many possibilities:
“There is a movement toward smart EAM solutions that offer real-time or near-real-time visibility into asset condition, health, and insight into potential problems. […] These next-generation EAM solutions support organizations’ desire to move from preventive to predictive to prescriptive maintenance. [They are] delivering added value with expanded connectivity and interoperability to intelligent sensors, IoT, and mobility devices.”
The outlook for the shift to advanced EAM tools is stable. Many investment programs are presently underway as firms look to advance towards continuous production. Following the digital transformation of asset-intensive organizations, industry players benefit from greater access to data-driven insights into asset performance, many of which result in operational achievements. Early adopters of digital asset management already report having experienced a stronger market position as they rapidly improved time to market, reached new client segments, and enhanced the customer experience.
Technology is, then, a lifeline for asset managers seeking to maximize investments in customer experience. A study finds improved client engagement as taking the central stage in a company’s efforts to undergo a digital transformation—and also a key benefit that results from carrying out this transformation. When EAM systems act on predictive data, the end result will only make customer assistance more proactive and efficient. This is why perhaps the most prominent benefit of EAM developments is the newfound attention for asset maintenance practices that methodically promote predictive inspections.
As Industry Leaders Center on Predictive Asset Maintenance, They Unlock Greater Data Value
Many of today’s industrial asset managers are putting greater confidence in the promise of predictive maintenance. This promise often arises out of data—which is touted by industry experts as the fuel of any predictive maintenance engine. An argument made to support this idea comes out in Deloitte’s position paper on predictive maintenance:
“The more information is available on events to be predicted, the better predictions become.”
Moreover, timely data gathered, integrated, and dispersed through EAM technology can spur predictive analyses that offer immediate visibility into the firm’s assets. Along with these benefits come many others. An asset maintenance plan that is powered by predictive data provides remarkable value, galvanizing industry players to:
- Collect real-time operational information and share it across the firm
- Analyze timely information to predict equipment breakdowns, reduce unplanned shutdowns, and improve worker safety procedures
- Decrease production losses while maintaining quality levels at all times
- Enrich OEE calculations and improve the score
- Maximize asset investments while briskly reducing maintenance costs
What predictive data constantly offers is, in essence, an opportunity to pay closer attention to the firm’s industrial assets—an offer inherently similar to the one made by EAM technology, which prompts industry leaders to listen to, and learn from, their assets via predictive analytics. Once the EAM application is paired with predictive analytics, firms can act on the resulting insights set to refine asset management.
A New Asset Management Reality Is Coming to the Fore
As firms are rushing to optimize the use of their industrial assets, the lack of predictive maintenance is becoming a pressing issue — and sometimes, this difficulty is compounded by legacy equipment that is primarily reactive.
Today, run-to-failure maintenance and outdated systems present an enormous liability; without having a forewarning of impending breakdowns, reactive responses will only compromise operations for asset-intensive firms.
The pandemic-generated turmoil sparked significant strides in the transition towards advanced maintenance programs and subsequently, accelerated digital transformation. All business sectors have recently witnessed a surge in the use of predictive models — which were deemed critical by experts in the fight against COVID-19. In the manufacturing industry, there is at least one rational argument in favor of this shift: after events in 2020, asset management standards are changing permanently.
To avoid falling down a slippery slope while sustaining asset health, firms have to evolve their operating models to continuously capitalize on asset intelligence derived from predictive maintenance practices and supported by technological advancements in EAM systems.