- April 11, 2016
By Rick Delgado, Freelance Technology Writer
When it comes to big data analytics, manufacturing companies have discovered numerous use cases and applications, all of which bring notable benefits in a highly competitive marketplace.
By Rick Delgado, Freelance Technology Writer and Commentator
The advantages of using big data analytics in business are undeniable by this point. In just a few years, a wide variety of businesses have made significant strides in adopting big data solutions intended to reach a number of different goals. Businesses from all types of industries have seen how much big data can help them. This is true of healthcare, finance, marketing, and even sports. Not to be overlooked, the manufacturing industry is hopping on the big data bandwagon as well. When it comes to big data analytics, manufacturing companies have discovered numerous use cases and applications, all of which bring notable benefits in a highly competitive marketplace. For those manufacturing businesses that are still wondering what big data can do for them, the following applications can prove useful in determining how best to pursue their own big data strategies.
Reducing Waste and Energy Costs
Much of the hype surrounding big data revolves around the ways in which it can increase a manufacturer’s profits. This can be done in a number of ways, but one of the most common is reducing a manufacturing company’s waste and energy costs. According to research done by McKinsey & Company, one chemical manufacturer in Europe used an advanced form of analytics to look for deeper insights into the manufacturing process. Looking at various factors like temperatures, carbon dioxide flow, and coolant pressures, the company was able to determine which factors had a noticeable impact on overall yield. Based upon these findings, the company was able to reduce material waste by an impressive 20 percent, while also cutting energy costs by as much as 15 percent.
Increasing Accuracy and Yield
Another way manufacturers can use big data to increase product is by improving their accuracy during the process while also producing more. This can be seen in the example of a biopharmaceutical company that took this route with big data analytics. In the case of this type of business, dozens of factors play a role in the manufacturing process. Big data makes analyzing these factors much easier. In this company’s case, the factors most associated with yield variation were identified. This lead to a much lower error rate (a crucial factor in pharmaceuticals) and allowed the company to produce more. In just one example, the yield for one of the company’s vaccines increased by 50 percent, leading to millions more in profit.
Improving Quality Assurance
Every manufacturing company knows the importance of churning out a high quality product. For this reason, many manufacturers have turned to big data to help them improve their quality assurance. One such company is Intel. The computer chip manufacturer uses predictive analytics to ensure the products their making are of the best quality. Before big data, each chip would have to go through thousands of tests to see if it held up to Intel’s standard. With big data, the number of tests could be reduced dramatically, putting the focus on certain tests to maintain high quality. With millions saved, Intel looks to be using big data in many other aspects as well.
Speeding Up Assembly
Part of the key to manufacturing more products is to simply make the whole process quicker. With big data, manufacturers have been able to segment their production to identify which parts of the process go the fastest. Knowing which products are faster and easier to produce can help companies know where to focus their efforts, perhaps even concentrating solely on those products for maximum production. It helps for companies to know where they are most efficient, with the added possibility of working on those areas that need the most improvement.
Big data has many applications in the manufacturing industry. As manufacturing companies begin to adopt big data technologies and work with converged infrastructure vendors, they’ll be better positioned to take advantage of all that big data analytics has to offer. It may not always be the easiest strategy to pursue, but anyone who has traveled down that path can say with certainty is has been worth it.
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