Peering into the future--a new approach to predictive maintenance

Despite the hype around machine learning and IoT, value propositions have been elusive, the one big exception being predictive maintenance.

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Burt Hurlock, Azima DLI AzimaDli BurtHurlock SQ2CEO, chats with us about predictive maintenance, the role domain experts play with big data and the importance of benchmarking. Take a look…

Smart Industry: What is your approach to predictive maintenance?

Burt: Our approach is to offer decision support to site, regional and global industrial-enterprise managers. Site-level maintenance teams performing mechanical break-fix tasks receive accurate and timely predictions to plan repairs and eliminate unplanned events; and site, regional and enterprise managers tasked with managing maintenance budgets and capital expenditures receive benchmarking reports that correlate maintenance activities with avoided costs and overall machine health to ensure the highest-possible production efficiencies and asset-life expectancy. Azima DLI reaches a number of industrial-enterprise audiences by capturing data at massive scale from a number of condition-monitoring technologies, aggregating those data from across the plant (and across plants throughout the enterprise) and using that data both locally and regionally to standardize demonstrated best practices across the enterprise. 

Smart Industry: What advantages does this approach offer?

Burt: The Azima DLI approach to PdM varies from traditional approaches in two important ways:

First, we are the only PdM service company to have built a fully integrated, end-to-end solution that incorporates data collectors, a cloud-based support network, an expert diagnostic system and the world’s largest and most experienced team of machine-health analysts. That means we can support customers who want to run programs themselves, but want the comfort of secure data back-up and access to diagnostic experts if required. We can also support customers seeking a fully outsourced, turnkey PdM solution, as well as customers who mix and match in-house with outsourced programs depending on the site.

Second, Azima DLI hosts machine-test data in perpetuity, which means the company retains an unmatched store of machine and site-performance history that is the basis for valuable industry-performance benchmarking information. Benchmarking reveals opportunities to draw on the experience and practices of comparable sites to achieve the best possible yield on maintenance spending and capital expenditures. So while PdM has always been invaluable as a site-level tool, it now has the potential to impact performance across the enterprise.

Smart Industry: How is digital transformation affecting the world of predictive maintenance?

Burt: Digital transformation is the catalyst for unleashing all the benefits of visibility that are already commonplace to other markets, like retail and banking and media. Today’s informed consumer would never buy without comparing options, performance and pricing online. Industry is proving much slower to adopt, mainly because the full implications of digital transformation at the plant are poorly understood.

Despite the hype around machine learning and IoT, value propositions have been elusive, the one big exception being predictive maintenance. PdM has always been effective in the hands of sophisticated users, but very difficult to scale because sophisticated users were rare and their influence constrained to within the four plant walls. Cloud-based solutions change everything by making the successes of sophisticated users visible and replicable.

Our business is increasingly concentrated among sophisticated global/industrial production operations that have recognized the potential for leveraging best practices exposed by the digital transformation.

Smart Industry: How important is it for an enterprise to benchmark itself against peers/competitors regarding maintenance? How do these challenges spur growth?

Burt: Benchmarking drives competition in every other market—why should it not fuel the competitiveness of industry? Automotive companies compete head-on for ratings from well-known independent-ratings agencies, as do television shows for viewers (also tracked by ratings agencies), and restaurants for guidebook star ratings. Even the fitness industry has embraced personal benchmarking with step- and calorie-counting devices and apps. Benchmarks are a ubiquitous method of measuring relative performance, and with digital transformation, benchmarking will become important to industry as well, in at least three important ways:

  • Self-benchmarking: The impact of unplanned events that lead to down time, unanticipated costs and lost production is easily measured. If the event can be predicted and the costs avoided, what organization would say “No”? Organizations that commit to rigorous internal benchmarking discover surprising opportunities to standardize on the practices of their most-skilled managers and operators.
  • Competitor benchmarking: Nobody likes to be compared to a competitor and found wanting, but if the alternative is staying uninformed about of how competitors are winning in the marketplace, companies that play to win would rather know. Digital transformation will bring competitive benchmarking to industry, and companies committed to leadership will embrace it. PdM is rife with opportunities to move from average to top-quartile performance—from data-collection strategies to response times, from maintenance-planning strategies to tactics for avoiding costs. The potential for improving yield on production assets is material and the gains achieved relatively easily.
  • OEM benchmarking: Benchmarking applications are built on compiling data that originates at the individual asset level. If enough data exists to self-benchmark and benchmark against competitors, then enough data exists to compare the individual performance of like assets. OEM benchmarking presents opportunities to improve the performance of installed assets, and to be smarter about investing in new assets.

Smart Industry: What role does big data play in the smart factory?

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