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.