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Sundeep Sanghavi

In today’s manufacturing world, preventative maintenance is the standard method of ensuring that machines remain in working order and factories stay up and running. Equipment is routinely checked by technicians who hopefully catch impending breakdowns in their early stages, thus preventing full-on machine failure and costly downtime from occurring.

However, this strategy — one that is utilized by roughly 85 percent of manufacturers — is grossly inefficient. Labor costs are unnecessarily inflated, as machines that end up having no issues whatsoever are manually checked by humans. And further, there’s no telling what will occur between scheduled inspections. A machine that shows no signs of breaking down during routine maintenance may begin doing so the very next day — and at that time, it could be weeks before the technician returns to conduct another inspection.

This preventative approach to maintenance is costing manufacturers way too much money, and it’s putting them at risk of catastrophic machine failure. But fortunately, there’s a better way — and it has potential to both improve how factories conduct maintenance and revolutionize the manufacturing industry as a whole.

Stop Preventing and Start Predicting

The Internet of Things is sweeping across the entire industrial world, and manufacturers are poised to reap many benefits from leveraging this trend and embracing predictive maintenance.

IoT-driven sensors can be attached to factory equipment to record and compile millions of data points around the clock. Then, thanks to the power of cognitive data science, these metrics are automatically analyzed in real time.

From a maintenance standpoint, technicians receive notification at the very moment a sensor detects an issue, and they can respond swiftly and accordingly. Thus, manufacturers have the ability to predictively maintain their equipment, as maintenance is boiled down to a proactive, efficient, automated process where manual intervention only occurs on an as-needed basis.

But that’s just the beginning of the benefits predictive maintenance can provide to manufacturers. With the enormous amount of data available from IoT devices and sensors today, factories find themselves with a golden opportunity to transition into becoming “smart” entities that operationalize data into their day-to-day decision-making.

Here are four big-picture operational issues that predictive maintenance can illuminate for manufacturers:

No.1 - A Kink in the Supply Chain

Inventory discrepancies are a total nightmare for manufacturers. While managing short-term inventory demand can be quite straightforward, forecasting for the future is where things get tricky. It’s highly difficult to balance an array of suppliers, vendors, and inventory, especially when dealing with seasonal factors. We’ve reached a point where using data to traverse this landscape is no longer an option; it’s mandatory.

According to a recent Deloitte survey, “demand forecasting” is currently one of the biggest imperatives for manufacturers, but only about half say they use predictive maintenance to help manage their supply chains.

Predictive maintenance don’t just allow manufacturers to evaluate deep archives of historical data; they also allow them to intertwine data from external influencing factors to identify patterns and make informed decisions.

No. 2 - Subpar Quality Control and Efficiency

Ensuring a consistently high-quality product is imperative in manufacturing. Every product that fails the quality test is wasted materials and energy, and if machine output isn’t closely monitored, it can spell the end of business.

Adapting advanced, data-driven workflows to Six Sigma and DMAIC processes makes the overall workflow more customer-driven. Doing this won’t just improve product quality; it will also increase production speed while minimizing waste.

According to GE, a 1 percent increase in efficiency can help companies save billions of dollars over the next 15 years, while another study estimates that output increases by 25 percent when predictive maintenance is used. Manufacturers don’t have to blindly trust that their equipment will continue functioning and performing at the same level when they can see exactly how it’s been performing today, last week, last year, and throughout its lifespan.

No. 3 - A Lackluster Business Model

Due to the massive amounts of suppliers, customers, machines, and pricing constraints involved in the modern manufacturing process, it’s nearly impossible to optimize production workflows without the use of predictive analytics. With complete, real-time visibility into machine performance, manufacturers are better prepared to optimize their operation’s time and labor.

This data can be used to identify and streamline which products are driving lower margins, optimize specific products for positioning in a competitive landscape, and amplify sales by managing levers. It can empower manufacturers to connect missing dots for uniting everyday production metrics to financial metrics.

With this power, manufacturers can optimize machine performance for higher efficiency, greater throughput, and higher yield. Also, visibility into predictive analytics allows them to plan better for labor and run their floors more efficiently by knowing when to scale up and down in advance.

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.

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.

Sundeep Sanghavi is co-founder of DataRPM.

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