When we think of personalization of service through analytics, the first thought that likely comes to mind is retail applications or consumer goods.
But what about industrial equipment, especially heavy equipment? The way we service big machines is undergoing a renaissance, thanks to the same technology we use to enhance the consumer experience.
Machines Have Personalities
When I was in college, I worked summers at a company that made industrial photographic supplies. Because this was the pre-digital age, photography for magazines and newspapers was complex. The company made giant cameras — so big you could walk into them — as well as rolls of film as big as an area rug and photographic chemicals that came in 50-pound containers.
Like anyone who has spent time around industrial equipment, I realized that each machine had its own personality. The machines had quirks that the operators and service technicians learned over time.
And recently, while watching a keynote address from SAPPHIRE NOW, SAP's user conference in Orlando, Fla., I was reminded of those lessons I learned about machines such a long time ago.
A representative from John Deere, the well-known manufacturer of everything from tractors and engines to construction, forestry and turf care equipment, was talking about new ways to support big machines in the field. The problems he described were very similar to those I saw in the chemical plant where I toiled away summers many years ago.
Service and support for big machines is complicated. Industrial equipment is a complex arrangement of mechanical parts, electronics and computer technology assembled into intricate systems.
How do you understand the unique personality of these big machines so that they can be serviced better? The answer lies in the intersection of analytics and the Internet of Things.
By feeding sensor data from individual machines into real time analytics, you can identify the unique needs of each machine before problems get serious. And if you use predictive analytics, problems may be averted altogether.
In some cases, the service technician may show up before things break or the customer is even aware of a looming problem. Analysis of machine sensor data can also lead service personnel to better understand the unique wear patterns of parts within a machine and create more precise maintenance schedules.
Personalizing service extends to parts in the machine. This type of service easily translates into less planned and, especially, unplanned downtime. Reducing downtime is major concern for manufacturers, farmers and construction companies.
Less downtime means more on-time deliveries, higher customer satisfaction, reduced costs and higher revenues.
The same technology that marketing professionals are using to better understand customers and deliver a personalized experience can be applied to large scale industrial machinery.
Analytics enables a personalized service experience not only for the manufacturing company but for each individual machine — or part of it — on the factory floor, farm or building site. It’s a technology that helps turn the equipment vendor into the manufacturer’s partner in success.