“ What I like to talk about is availability , especially availability to plan ,” says Brooks . “ You want to be sure that , when you put the equipment in place , it ' s going to work reliably for a certain length of time . Often in a refinery , it ' s called a turnaround , and they run them every four years . So they expect it to run for four years , then they can pull it apart , service it and repair it . That ' s the general expectation .”
Predictive maintenance tools can also help to increase reliability by avoiding downtime – something the industry has seen a lot of over the past few years . As they say in every industry , time is money . But in manufacturing , a little warning can help a great deal , especially for the supply chain .
“ The earlier you can do it , the better position you put them in . The superior products actually predict problems by examining multidimensional and temporal patterns in the sensor signals coming through , in ways that humans can ’ t .”
Manufacturers try to recognise those patterns in machine learning , so businesses can understand early and take action .
During his time at AspenTech – which included the height of the COVID-19 pandemic – Brooks has learned some powerful lessons , including about how he works best .
“ My favourite thing , and the thing that ' s been best for me , is to be in front of people , to talk about and understand their problems , and make sure that that gets fed back into the product . You have to know them intimately . You have to know their work process in order to make the right products . Otherwise , you ' re just throwing technology at people and that doesn ' t work .” Whatever comes next for the manufacturing sector , Brooks will find something to learn . manufacturingdigital . com 81