When most people think about big data and analytics in the business world, their minds immediately jump to ecommerce businesses, websites, and social media. But the truth is, big data is changing things offline as well. Just look at manufacturing.
4 Ways Big Data Analytics is Changing Manufacturing
The manufacturing space has always been highly competitive, but things have become even more heated in recent years. Innovative technologies have increased production capacities for larger companies, which has left smaller organizations questioning how they can continue to grow and compete on an even playing field.
“Most manufacturers have already made the most obvious changes to streamline their operations, using traditional methods to eke as much productivity out of their supply chains and plants as possible,” Valerio Dilda writes for McKinsey. “To do even more with less in a slow-growth and uncertain environment, however, companies must look for new ways to boost the productivity and profitability of their operations.”
The answer is big data analytics.
Manufacturers and companies in process industries generate massive amounts of data, yet very few put this intelligence to use. Over the years, most manufacturers have lagged behind in their IT capabilities and it has cost them.
“However, thanks to cheaper computational power and rapidly advancing analytics opportunities, process manufacturers can put that data to work, gathering information from multiple data sources and taking advantage of machine learning models and visualization platforms to uncover new ways to optimize their processes from the sourcing of raw materials to the sale of their finished products,” Dilda continues.
While each use case will differ, it’s helpful to understand the big picture of how big data analytics can be used in manufacturing. Let’s take a look at some of the top areas:
1. Predictive Maintenance
One of the biggest early wins for big data analytics in manufacturing has been the role of predictive maintenance. With all of the various sensors and connected devices included in today’s advanced equipment, it’s possible for manufacturers to use algorithms to uncover complications before they arise and fix minor issues before they become costlier problems.
Predictive maintenance has the potential to save manufacturers millions of dollars over the course of a year, prolonging the life of equipment and ensuring efficient operations. And thanks to growth in big data platforms, it’s becoming easier and more cost-effective to gather these insights.
2. Performance Analyses
It’s easy to assume that everything is functioning properly, but there’s a huge difference between operating at 80 percent capacity versus 95 percent capacity. Big data allows businesses to analyze performance and make changes based on desired levels of output.
Take industrial manipulators as an example. Up until recently, many businesses have used one-size-fits-all products, but this is changing. When customers come to Dalmec, one of the leaders in this space, they’ve reached a point where they realize cookie-cutter solutions don’t work. Using data and analytics, they can purchase one-of-a-kind manipulators that have been specially engineered for specific functions. This results in far greater productivity and performance.
3. Decrease in Downtime
Few things are costlier to a manufacturer than downtime. In some industries, it can cost thousands of dollars per minute and millions of dollars per year. With the right systems in place – systems that are powered by big data – these organizations can greatly reduce downtime and ensure maximum productivity.
In addition to bolstering the bottom line, a decrease in downtime improves operational efficiency, reduces stress, strengthens brand loyalty, and allows for innovation and creativity.
4. Improved Strategic Decision Making
At the end of the day, big data analytics helps manufacturers with their strategic decision making. In other words, there’s less guessing and more execution.
When it comes to empowering your organization, you have a variety of big data analytics tools to choose from, including: data cleanup tools, profiling tools, data mining tools, data mapping tools, data analysis platforms, data visualization resources, data monitoring solutions, and more. Learning how to combine the right tools for the right outcome is an important step in this process.
Changes Are Coming
The notion that big data analytics is changing manufacturing is both exciting and intimidating. For companies that have been collecting data for years but have failed to use it, the notion that they’ll need to finally start leveraging it is a bit frightening. But once you get past the initial challenges, you’ll realize there’s tremendous opportunity for growth and advancement.
Big data analytics are changing manufacturing for the better. The only question is, will you be coming along for the ride?
