Staying competitive is always top of mind for manufacturers. With the ever-increasing supply of goods from overseas producers coupled with the rising costs of domestic production, manufacturers need to utilize any advantage they can. In manufacturing, one of the best investments is reducing machine breakdowns. Studies have consistently demonstrated that downtime is one of the leading costs incurred by manufacturers across the world. Machine breakdowns don’t just halt the production of goods, they also add to labor costs in the form of outside repair services and the cost of an underutilized workforce.
The average manufacturer suffers from nearly 1,000 hours of downtime per year, as a direct result of breakdowns. In the automotive industry, this can add up to tens of thousands of dollars per minute . Even if this isn’t reflective of all industries, it’s clear that unplanned downtime can seriously impact any business’s bottom line.
IoT Enabled Devices Are Everywhere
The potential value for IoT (Internet of Things) enabled devices can’t be overstated. These smart tools allow for seamless communication between machines, offsite monitoring, data storage, and quicker mobile access for technicians and operators. While the mainstream applications in cars and home appliances are certainly making waves, the area of real groundbreaking innovation is on the industrial market. By 2020, we can expect to see over 20 billion active IoT (Internet of Things) devices worldwide. This surge in the number of sensors communicating means more sources to collect data for analysis, and a wider range of functionality. As for the return on investment, 72% of manufacturers questioned in a recent survey cited IoT equipment as responsible for increasing productivity.
Cloud Computing Has Made Data Analytics Lightning Fast
The advent of cloud computing has shortened the distance between the machine and the operator. The rapid growth of this 400-billion-dollar industry has democratized information storage. Networked machines provide data that can be tracked and stored in real-time allowing technicians to respond faster with more accurate tools at their disposal. What’s more, programmers are able to utilize the information available to trend machine health and reveal new insights into operating conditions. Everything from vibration analysis, temperature readouts, and ultrasonic data can be collected, compiled and saved for easier access and utilization. Collecting the data in cloud platforms provides an even greater range of use beyond the manufacturing floor. Some companies have even begun implementing machine health insight into applications aimed at improving supply chain management.
Machine Learning is Growing Exponentially
Machine learning programs bring countless opportunities for labor management, streamlined maintenance, and supply chain management processes. These smart applications take in data, observe trends and “learn” from the information–as the name suggests. For manufacturers, the potential is limitless. One of the biggest areas of cost-benefit comes from developments in vibration analysis. Artificial intelligence systems provide technicians with knowledge into minor changes in vibration data and can accurately predict when a machine is heading towards failure. This early insight gives technicians the chance to make necessary operational changes, conduct planned maintenance, and save tens of thousands of dollars a year.
About VibePro
At VibePro, it is our mission to provide the best predictive tools on a single iPad platform with services to monitor nearly any asset. We strive to bring our customers the portability, connectivity, and affordability offered by the latest available technologies. Our product family combines wireless portable and online vibration data collection and analysis, balancing, shaft alignment, thermography, and ultrasound into an affordable and completely scalable solution on one simple to use platform. VibePro’s predictive technology apps feature many additions that have come directly from customers. Click here to learn more.