HEADLINE
ctive analytics come or is it just the big data vendors trying to persuade us ?
trying to persuade us ? Let ’ s answer this by debunking the myths of manufacturing predictive analytics .
Firstly it is important to start by saying that ‘ visualisation ’ is not analytics despite what many vendors tell you . 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 .
Secondly , there are many challenges with implementing and operating the common predictive analytics tools already on the market . Firstly it is too easy to produce a predictive model which is either naively incorrect to start with ( because it requires a data scientist to build the model and / or validate ) or soon becomes irrelevant ( because things change ). Secondly there is a mathematical limit to what predictions can be made with a specific amount of data - if a vendor tells you he can predict the future from a just few data points , then it ’ s too good to be true .
Thirdly , if the analytics can ’ t be practically implemented on the shop floor then it is useless .
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