LEAN
Lastly , there has to be a clear use case on which to apply the analyses to in the first place . If a problem requires an army of PhDs or the problem is not that large , then think again . Predictive analytics isn ’ t the answer to every prayer .
So , what kind of manufacturing problems are placed to benefit from predictive analytics ? Well , there ’ s root cause analysis for resolving defects to lower COPQ , predictive maintenance
Being able to see and play around with data is useful , but if say an engineer wants to know what his COPQ is likely to be over the next few months and the factors which are driving the forecast , this is predictive analytics and service on products and plant , hidden bottlenecks in processes , speeding up launch , lowering supply chain costs by understanding behaviours , improving the design of products by understanding how customers will use them better , product pricing … the list goes on .
For engineers , it ’ s a case of replacing ‘ rear-view mirror analytics ’ using statistical tools , with forward looking analytics which enable results to be understood and most importantly proven – perhaps ‘ practical predictive analytics ’.
So now we ’ ve debunked the topic , what about the technical hurdles ? In the 2014 TDWI survey , the top three predictive analytics challenges across all sectors were : lack of skilled personnel ; lack of understanding of predictive analytics technology ; and an inability to assemble the necessary data .
Fortunately there are predictive analytics companies appearing who address these challenges by automating the data integration exercise , not needing to clean data ( in fact positively refusing to clean data lest it remove early warning signals ), dealing with sketchy data ,
16 October 2015