OPERATIONAL EXCELLENCE
THE ISSUES SURROUNDING bottlenecks in manufacturing are well documented . From production delays , overstock , increased pressure from customers and beyond , the problems caused by a sudden , unforeseen limitation in capacity can be devastating for any business in the industry . With this in mind , Manufacturing Global speaks to former Yahoo data scientist and leading light at Progress DataRPM , Ruban Phukan , about the use of predictive maintenance models and how they can help establish a fail-proof environment .
Getting to the heart of the problem As a former member of the Yahoo data analysis team , Phukan is well versed in the complications surrounding the use of big data . “ I was part of the first data science team that was created at Yahoo many years back ,” he says . “ The big learning there was that data science is very hard to scale , manually , for large organisations . What we realised is that the only way to really solve the data science problem , in a manner that can add value to a business , is to automate the processes that go behind that data analysis .”
Moving on from Yahoo , Phukan started his own vertical search engine business , using machine learning to understand user behaviour , specifically dealing in predictive analytics . After selling it on he went into business with his current co-founders , which is where he picks up the story of ProgressDataRPM and how it is making strides towards , among many other things , tackling bottlenecks .
“ When we first got together we realised there was a huge amount of digital data that was being generated ,
42 April 2018