It never fails: Things tend to fall apart, literally and figuratively, at the worst possible moments. Maybe you're running late for an important meeting when your car battery breathes its last, or you're printing out a critical document for a presentation when the printer runs out of ink.

Nothing lasts forever. The machines we count on to perform their functions flawlessly every day will inevitably fail. What happens when a problem like the battery in a single car or the ink in your office printer grows to the scale of a problem in manufacturing? How valuable would it be if you could pinpoint those moments exactly and intervene to keep things running smoothly?

Prediction Over Prevention

That bone-dry printer didn't come out of nowhere. In fact, you probably can't even recall the number of times you saw it flash "Toner Low" in silent protest of your neglect. Still, it picks the most inconvenient moment to go from toner merely "low" to "not enough for a single sheet."

Enter predictive maintenance.

Whereas preventative maintenance establishes an estimate of an outcome based on previous performance, predictive maintenance utilizes a constant stream of data to calculate outcomes in real time.

Predictive maintenance can be applied to many industries, but it's especially groundbreaking in manufacturing. Sundeep Sanghavi, co-founder of DataRPM, a Progress company, gives a broad overview of areas in which manufacturers can expect to benefit: "Factories that elect to embrace predictive maintenance will see an undeniably positive ripple effect of benefits. Not only will they stop wasting time, money, and effort on a highly inefficient approach to maintenance, but they'll also gain valuable insights into other issues within their operations they never knew existed."

Sanghavi elaborates, "Predictive maintenance can help companies save $630 billion by 2025. Automated predictive analytics maintenance is the clear winning strategy for tomorrow's successful manufacturers. The sooner you implement it into your business, the better off you'll be."

While predictive maintenance is an obvious choice for manufacturing, this technology can be used to monitor anything that utilizes data to draw conclusions. To go about incorporating it into your operation, follow these steps.

1. Determine goals for the data.

What surprises would you like to get rid of? Figure out which metrics are indicative of a looming problem, and use them to alert your team before the problem occurs.

Maybe you'd like to eliminate downtime in your manufacturing process caused by overheated engines. If an engine was slowly burning oil, for example, implementing predictive maintenance might involve installing hardware and software that monitor its use, check temperature and oil levels, and calculate how long it can operate with the remaining fluids. The predictive maintenance solution could then inform technicians before that engine becomes a major problem, thereby saving you time and money.

Predictive maintenance is about more than knowing when things are just about to go off the rails. It's about automatically alerting the right people and technology to intervene at the right time

2. Find the right data to test.

Correlations between data and the problems you're trying to solve won't always be as straightforward as in the overheating engine example. One of the most difficult (and critical) parts of executing a predictive maintenance strategy is figuring out what data needs to be monitored and putting in the time to collect it.

For your data to be useful, you need to collect the right information for a while before you begin to use it to draw conclusions. Otherwise you might base an expensive initiative on anecdotal evidence and end up doing more harm than good.

3. Ensure the data stream continues to flow.

Once your predictive maintenance program is off the ground, keep it running by ensuring your data is updated constantly. Data is the fuel for your whole program, and if it's not up-to-date and comprehensive, then it's not going to help you successfully detect and avoid problems.

Buying behaviors evolve, manufacturers update models, and new technologies can give you a closer look at just about every part of your business. Collect and analyze that relevant data and incorporate it into your predictive maintenance program to maintain the program's effectiveness.

Predictive maintenance isn't a silver bullet. Constantly monitoring equipment or key metrics can be expensive, and there's a learning curve for your employees when working with the technology. Predictive maintenance also doesn't apply to every product or process -- for instance, you probably don't need a home dryer monitoring system and email alerts to tell you when to clean out your lint trap.

But for any business leader or enterprise executive looking to streamline production, eliminate costly setbacks, and improve performance, leveraging the insights offered by predictive maintenance could be just the ticket.