In addition, IDCMI predicts that big data initiatives will remain one of the top priorities for the manufacturing sector in the foreseeable future.
However, when compared to other industries, manufacturers seem to be on the trailing edge of this development rather than the leading edge. That’s not surprising given the huge IT and factory-infrastructure investments that can be required to tap into the business-transformative power of seemingly infinite loads of data about manufacturing functions.
There are some manufacturers, such as Caterpillar, who are taking the plunge and leading the way. One example is Caterpillar. It’s “Cat Connect” network, for example, connects Caterpillar’s customers to dealers. “It monitors everything our machines are doing, so engineers can see whether it has a high exhaust gas temperature or is running low on oil—all the things the engineer needs to know to run that machine in his fleet more efficiently,” Chairman and CEO Doug Oberhelman told Chief Executive’s Smart Manufacturing Summit audience in May.
ADDRESSING THE CHALLENGES
The difficulty of tapping into big data is compounded in some processing environments where extreme variability is a fact of life even after lean techniques have been applied, McKinsey & Co. consultants say. So companies in these industries need a more granular approach to diagnosing and correcting process flaws—an approach provided by advanced analytics.
Operations managers can take deep dives into historical process data, identify patterns and relationships among discrete process steps and inputs, and then optimize the factors that prove to have the greatest effect on yield, according to McKinsey.
For example, one big pharma company is using advanced analytics to significantly increase its yield in vaccine production while incurring no additional capital expenditures, by crunching data about its manufacturing processes and how they impacted yield—and then making targeted process changes.
General Motors’ current recall crisis has had the automaker pressing for ways it can predict potential safety problems and patterns earlier. One promising avenue could be to tap into the massive diagnostic data that already exists in GM’s OnStar system—its digital and connected infotainment platform that has been gathering information on the function of connected vehicles for 18 years.
Subscribers who receive OnStar’s monthly diagnostic emails about their own vehicles see a few dozen of these measurements, but GM has them all, The New York Times noted. The company could analyze that data, looking across thousands or even millions of vehicles in search of safety problems. For now, however, GM remains tight-lipped about any big-data applications it may be making to engineering and manufacturing challenges with the OnStar data.
As manufacturers think about big data, they should focus more heavily on the analysis end than the capturing end, as capturing and storing data is the easy part. Finding valuable and usable insight in a timely fashion and then putting it to good use is the key to achieving increased revenue streams.