Work stoppages cost companies money. It’s no secret, no great mystery. The longer the stoppage and the bigger the company, the greater the revenue loss and operating cost respectively. Fortunately, this is a reality that most companies do not have to worry about. For the manufacturing and energy sectors, however, this is not a rare occurrence.
Due to the nature of manufacturing and the oil and gas industry, it is dependent on heavy machinery that are scattered across distances and often in remote locations. So in order to maintain these machines, there needs to be engineers making regularly scheduled trips or staying on-site. That’s quite the investment in manhours and headcount!
Even then, the machines will break down over time. There are just too many internal and external influences for engineers to keep an eye on. In this digital age, the energy industry is largely still running on manual power. When there are solutions powered by artificial intelligence available to be incorporated into the process to make the business more efficient, they should be considered. Our advances in technology has led to a digital transformation wave where AI takes on menial tasks and provides advanced analytics, leaving humans to operate at a higher and strategic level.
Predictive maintenance is an AI- and machine-learning-powered solution that will do just that for manufacturing and the oil and gas industry. It takes in historical data to predict which parts of a machine will fail at what time. With proper sensors installed that provides accurate and relevant data points, the prediction engine will operate with precision and prevent work stoppages.
Using the predictive maintenance solution, businesses will know when to schedule specific parts to be replaced, be alerted to degradations due to faulty parts or installations, if any parts are being cluttered and need to be cleaned, etc. The solution can even be adapted to prevent oil spills by predicting tank overflows and when to schedule replacements!
Unfortunately, it is one thing to install predictive maintenance and another entirely to make full use of it. Just like how it wouldn’t be wise to purchase a Tesla if one doesn’t have a driver’s license, charging port, and lives in northern Canada without a garage, businesses have to be ready to adopt predictive maintenance. How well equipped and ready a business is for adoption depends on many factors. But the journey to implementing a fully functional predictive maintenance solution is well worth the time, effort, and money if it prevents the annual machine breakdowns and work stoppages, don’t you think?