Predictive Maintenance: Will Railroads Assist Printer Repair Service Customers?

Sphygmomanometer for the topic about Printer Repair Service

In the past, customers requesting printer repair and service sometimes experienced frustrating delays waiting for repairers to obtain replacement components. Now dramatic modifications in the way repair firms deliver customer service promise to reduce critical unscheduled “down” time. This improvement in First Time Fix Rate statistics owes much to Machine Learning.

The Importance of First-Time Fix Rates

Repair companies in many industries pay close attention to a technician’s First Time Fix Rates (“FTFR”) statistics. This rating relates to the ability to solve a problem during the first visit to the customer’s site. Faster repairs please most customers by reducing inconvenience and delays associated with the provision of services.

Partly for this reason, today many printer manufacturers offering repair services encourage customers to utilize diagnostic repair software programs and remote technical assistance as initial steps in correcting problems. If firms must dispatch technicians to the site, these preliminary protocols frequently offer assistance in terms of identifying components that sustained damage or heavy wear.

Lessons From The Railroad Industry

In recent years, the development of sophisticated Machine Learning technology has assisted many railroads in actually predicting the need for repair services. Switching from a scheduled maintenance and repair services model to a predictive maintenance model enhances efficiency to an even greater extent. Experts utilized machine learning protocols to actually forecast when sudden equipment failures would occur.

Some other industries have recently embraced this model (including some manufacturing companies). Using Machine Learning to accurately gauge the need to send a repairer to a client’s site may eventually enhance the productivity of printer repair companies, as well. The variability and diversity of printer models (compared with railroad equipment) may challenge implementing this process rapidly, however. Machine Learning requires extensive repetitive data collection and processing in order to generate accurate predictions.

Ongoing Improvements

Safety concerns frequently compel transportation industries to subject original equipment manufacturer parts to extensive performance testing. Yet office products don’t always obtain such detailed preliminary study before reaching the marketplace. As the power of computers increases, possibly engineers will discover ways to address this issue.

Already, improvements in FTFR statistics suggest the repair industry maintains a strong interest in improving efficiency. This result promotes enhanced customer service. Consumers ultimately benefit when printer repair firms help them avoid preventable “down” time!

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