Understanding Predictive and Preventive Maintenance for Heavy Asset Optimization
Heavy production assets are ubiquitous across numerous industries, from residential construction to mining. Keeping this mission-critical equipment up and running is among the top priorities for modern industrial organizations. It is why the average firm devotes almost 10 percent of its facilities budget to maintenance activities, according to researchers from Plant Engineering.
Virtually all of these entities leverage either predictive or preventive maintenance methodologies, both of which materialized in recent years due to widespread enterprise digitization. But how exactly are businesses deploying these strategies to ensure heavy production assets maintain peak performance? Here is an industry-agnostic look into the state of predictive and preventive maintenance best practices for heavy asset optimization:
Unpacking the preventive approach
An estimated 80 percent of maintenance teams employ preventive maintenance techniques, per survey data from Advanced Technology Services and Plan Engineering. The proactive approach centers on one operational goal: reducing production downtime. There are several associated best practices that guide preventive maintenance implementation and management.
Relinquishing the reactive approach to asset maintenance is the most impactful of these exercises. Unfortunately, it is also the hardest to adopt, Modern Materials Handling reported. Maintenance specialists that have traditionally listened for the cacophony of mechanical collapse and responded in turn must change their mindsets and instead focus on implementing piecemeal adjustments to mitigate the wear and tear that causes asset failure.
Making this cultural switch is no easy task – neither is reassessing all production and maintenance policies and procedures, and drafting new ones to comport with key performance indicators and company goals. However, four-fifths of maintenance teams have managed to execute these and the other preventive best practices on the way to transformation, including groups responsible for overseeing heavy assets.
Industrial organizations that excel at heavy machinery maintenance and effectively address small mechanical errors before they devolve into downtime-causing kinks focus on developing and sustaining routine asset optimization schedules, according to Reliable Plant. Through consistent check-ups and slight tweaks, maintenance teams responsible for bulky equipment can ensure these key production tools are always in good condition and ready to perform. Usage monitoring is also key, as heavy assets that are misused, either intentionally or unintentionally, typically break down the fastest.
How does usage monitoring unfold within an actual production workflow? A construction company preparing to excavate a new worksite might assess the climate and the soil to determine which backhoe digging attachment is not only best suited for the task at hand, but also the least likely to cause mechanical stress. These and other techniques make preventive maintenance possible, even with heavy assets in play.
Unpacking the predictive approach
When investments in heavy machinery began climbing dramatically more than a half decade ago, equipment manufacturers advised industrial companies to adopt asset tracking solutions to ensure that the multimillion-dollar tools they were deploying were actually required, The Wall Street Journal reported. At the same time, an innovative approach to maintenance, which also happened to be based on data analytics, was emerging.
This methodology, called predictive maintenance, would allow organizations to harness the power of advanced information technology to monitor mission-critical machinery in real-time, calculate the potential for future downtime and make improvements to avoid shutdown. In the years since this strategy materialized, many businesses have embraced it. In fact, 47 percent of industrial firms attest to deploying predictive maintenance processes and tools, per Advanced Technology Services and Plan Engineering.
Perhaps the most well-publicized and successful predictive maintenance programs have unfolded within organizations leveraging larger production assets. For example, construction equipment giant Caterpillar was among the first asset producers to manufacture products meant for use in predictive maintenance workflows, Digitalist Magazine reported. The company began building bulldozers, dump trucks, excavators and other common equipment with pre-installed wireless sensors designed to transmit usable performance insights to maintenance leaders. Caterpillar customers have seen significant efficiency gains and cost reductions as a consequence of this forward-thinking equipment and the accompanying software.
Harley-Davidson is another early adopter that propelled predictive maintenance to new heights. Starting back in 2010, the automotive giant began outfitting the decades-old equipment in its York, Pennsylvania plant with wireless sensors configured to monitor mechanical operations and measure physical variables such as component vibration to assess asset performance, The Journal reported. These tools and the predictive maintenance program they facilitated led to drastic shop floor improvement, as Harley-Davidson watched production throughput times and costs decrease.
While impressive, these outcomes merely represent the initial stages of the predictive maintenance approach, according to PricewaterhouseCoopers. The rise of deployable enterprise artificial intelligence technology is expected to have an immense impact on this strategy, lending industrial firms the ability to monitor more data points across larger pools of equipment, including heavy assets.
That said, there is ample ground to cover before AI-propelled predictive maintenance workflows become industry standard. Most organizations inhabit the second position on the predictive maturity scale, per PwC, and therefore still depend on instrument inspections and other manual means. However, a significant number have entered stage three and now leverage real-time condition monitoring tools. Far fewer are in stage four, where expansive data-driven workflows capable of handling massive amounts of asset information are the norm.
In any case, predictive maintenance holds immense potential for teams assigned to heavy assets and, as the survey data from Advanced Technology Services and Plan Engineering indicates, a good number of the teams overseeing large machinery today have at least embraced some processes and tools associated with the strategy.
Implementing preventive and predictive maintenance strategies centered on heavy equipment can seem like an overwhelming task, especially for smaller industrial firms or those with particularly intense production demands. For businesses in these positions, external assistance could be all but necessary. USC Consulting Group has been helping organizations optimize their maintenance operations for decades, leveraging proven techniques and tools that accelerate change and lay the foundation for sustainable growth.
Connect with us today to learn more about our work and how we can help your company reduce heavy asset-related downtime.