Question

please describe a situation from a six sigma manufacturing environment where the better use of data analytics would have significantly helped?

Answer 1

Example- Intel, in 2012, reported that it saved $3M by using Big Data for preventative analysis on a single microprocessor chip production line. Extending the process to more chip lines they estimate a savings of over $30 million over the next few years along with the application of Six Sigma in their production lines.

Lean Six Sigma quality measures are now accompanied by advanced data mining techniques to detect anomalies, outliers, errors and eradicate them by using Prescriptive and Predictive analytics to know-how of the right methods to proceed for applying Six Sigma defect removals. This saves a huge fixed production costs at the beginning of the production itself and then saves variable costs for the company during Operation and Maintenance of machines. This benefits bottom line and profit margins of the company and secures it.

Advanced analytics with multivariate statistical techniques, evolutionary algorithms and machine learning algorithms programmed is used to estimate a function or perform supervised learning in Intel’s production line, for various products during their production schedule. Since, their microprocessor is heart line for most of the global PCs/ Laptops/ Mobiles, there is huge number of devices using them, which Intel collects information and use it as Big Data. Hence, these processors need to be error free for a reliable operation. Sig Sigma is used to Measure, Analyze, Improve and Control the manufacturing process for defect minimization. This even reduces possibility of calling back/ reverse logistics for any defected microprocessors from customers. Hence, again a good brand building option and saves replacement or defected products cost for Intel.