Aquant Introduces ‘Uptime as a Service’ to Combat Field Service Industry’s $53bn Yearly Loss

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A higher first-time fix rate can save companies millions of dollars per year.

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Research reveals that service companies in the US suffer from a $53 billion loss per year as a result of unnecessary repeat visits by their field service technicians. This has prompted one innovative company to take action. Created by field service industry experts, aims to reduce the number of repeat visits, leading to shorter product downtime and reduce cost for service companies across the US.

It has been reported that 1 in 4 visits from field service technicians results in a repeat visit. Each repeat visit costs the companies hundreds of dollars, due to lack of relevant parts, incorrect diagnostics on the initial customer engagement, or lack of knowledge of the problem that requires fixing. While many businesses are working to build predictive maintenance solutions, Aquant has taken a different approach. In addition to fault prediction, Aquant aims to solve the first-time fix problem that is responsible for 80% of products’ downtime. This unique approach makes them real pioneers in their field.

After spending years in the field service industry, Aquant's CEO, Shahar Chen, knows exactly how service companies struggle to increase their products’ uptime and improve their first-time-fix rate:

“Once one takes a deep dive into the facts, it is staggered to learn that repeat visits cost the American economy $53 billion a year. In today’s economy, this problem is set to grow even larger due to shift to service-based business models. Understanding that the need for first-time-fix solutions was one that was rarely met, we made it our mission to fix this. By using artificial intelligence and machine learning with a clear vision to eliminate product downtime, we quickly realized that what we are providing is really “Uptime as a Service,” potentially saving our customers millions of dollars every year."

The unique technology driving Aquant’s solution, continually monitors equipment in industries such as automotive, office equipment, HVAC, aviation, industrial equipment, healthcare and manufacturing. It provides real-time data on sub-optimal performance, leveraging artificial intelligence and machine learning to use a three- fold solution to predict faults more accurately, diagnose issues and recommend corrective actions. These practices all aim to enable smarter, data-driven, optimized decisions throughout the customer service process and increase product uptime.

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Shahar Chen
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