Predictive maintenance & quality solutions have that Cassandra-esque insight we all wish we had: knowing now what's very likely going to happen in the future. PMQ solutions provide organizations the advance intelligence to identify and act on, if necessary, issues well before they occur. This ability, of course, has wide sweeping ramifications starting with containing runaway costs.
In this first of two posts as part of the AnalyticsZone's newly launched PMQ Blog series, Greg Milwid from the Predictive Maintenance and Quality solutions at IBM, discusses PMQ's pervasive impact on "many other asset-intensive industries" besides just the manufacturing sector as well as specifically diving into asset failure use cases.
Read Greg's post now:
Predictive analytics is all about identifying an event before it may occur and Predictive Maintenance does just that. By analyzing machine sensor data, Predictive Maintenance helps organizations identify potential asset failures before they may occur and take preventative action. The ability to predict and prevent a potential breakdown has a widespread impact on the business, ranging from cost reduction and optimization to improving customer satisfaction.
We typically think of Predictive Maintenance being relevant to the manufacturing industry, yet there is a groundswell of interest from many other asset-intensive industries. The cost of unscheduled maintenance is driving significant tangible value to adopters of predictive analytics, whether they are trying to prevent unplanned plant and equipment outages, optimize planned maintenance, or reduce scrap and manufacturing defects.
Some sectors are even seeing new horizons opening up to them as a result of these predictive capabilities. This springs from their efforts to expand revenue opportunities through new business in equipment servicing. We are seeing this approach being explored as manufacturers optimize their businesses around maintaining equipment as varied as aircraft and their engines, mining equipment, farm and construction equipment, and commercial trucks and automobiles.
IBM’s approach to Predictive Maintenance and Quality offers customers two primary use cases: “Asset Failure” and “Quality.” I'll cover asset failure in this post and quality in the next one.
The costs for unplanned asset outages are astronomical. Haul trucks used in mining for instance, can have a cost impact of up to $1.8 million per day to be out of services, while giant excavators (mining) stoppages have an even higher impact of up to $5 million per day. Compare that to a 10 million tons of steel producer downtime impact of up to $7.3 million per day.
Equipment providers are also looking to reduce their planned maintenance impacts. In the oil industry, for instance, maintenance represent 20% of cash OPEX for oil production license (PL) companies. At the same time, for ten largest NA PL companies, a 5% improvement in maintenance yields $1.6 billion annually.
In the Pharmaceutical industry, manufacturers of nutritional products are looking to implement Predictive Maintenance in their manufacturing operations and manufacturers of Image Diagnostic equipment are assessing whether this technology can lower their customers’ maintenance costs. Conservative estimates indicate that such projects tend to recoup the investment within a year.
New opportunities are also opening up as the Internet of Things becomes a reality. Vehicle manufacturers are looking to see how they can exploit the emerging technology to provide safer and more reliable driving experiences for their customers by monitoring critical aspects of performance in real-time, while appliance vendors see opportunity in preventing frustrating outages for their customer base.
PMQ solutions are a game-changer positively impacting industries to great effect. To learn more about these solutions and how they're helping businesses become smarter enterprises here's two events you won't want to miss:
1) Please join us on June 10th in Indianapolis. IN for “A Taste of Analytics: Discover the Business Value of Predictive Maintenance” live event; and,
2) Attend our June 25th webinar, which will cover two Predictive Maintenance case studies – Daimler and IBM Bromont.
I invite you to keep following this blog as colleagues and guest writers dig deeper into various industry applications of Predictive Maintenance and Quality and show you why Predictive Analytics is creating such interest in the asset intensive industries.
In the meantime, if you have any questions about PMQ, please feel free to ask in the comments section.
Next post in the AnalyticsZone's PMQ Blog Series: "PMQ & Quality."