10 min
S2 / E3 How data keeps manufacturing hyper-competitive - Rand Merchant Bank (rmb.co.za) Data Analytics with Matthew Bernath
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- Business
Joining us today on this episode of the podcast series is Mike Grant - Chief Technical Officer at DataProphet, engineer and machine learning specialist. DataProphet is not a dashboarding company. There’s no added value outside of a beautiful looking dashboard where you have to take your production team to look at the dashboard; interpret the graphics and end up having 2 different people looking at the same graph and coming to 2 different conclusions. AI takes out that interpretation component and analyses the data a bit further to a point of action; formulating a plan as to what you should do next. A manufacturing line is a super complicated amalgamation of machines; you do something upstream and it can affect the next process. It’s very hard to say — ‘make this small correction here because you don’t yet have a problem, but if you don’t fix it, you’re going to have a very costly mistake’. It’s that guidance that really affects bottom-line profitability — if you can get it right, you can transform the manufacturing company into something that is hyper competitive.
Mike believes to undertake the AI journey, you need to start with a value proposition; make sure that whatever comes out of the AI system has a tangible ROI. This means operating at a price point where it’s affordable to the manufacturing firm, and the value it adds to the business warrants the expenditure. For example, if scrap is reduced by 40%, what does that mean to my bottom line? Among other things, it means an immediate cost improvement, additional production capacity, lower energy consumption and less CO² emissions. When it comes to having enough data, you don’t need an historical record. It’s like the saying — ‘The best time to plant a tree was 20 years ago; the next best time to plant a tree is now’. So, if you don’t have it, just start collecting it.
As far as the concern around AI replacing jobs, Mike feels this isn’t the case at all. There’s a massive absence of ‘experts’ around the world, so implementing AI is actually creating this input to drive a production system – upskilling existing machine operators to perform better. At the end of the day, it’s effectively going to augment our current processes and make businesses more competitive on the global stage.
See omnystudio.com/listener for privacy information.
Joining us today on this episode of the podcast series is Mike Grant - Chief Technical Officer at DataProphet, engineer and machine learning specialist. DataProphet is not a dashboarding company. There’s no added value outside of a beautiful looking dashboard where you have to take your production team to look at the dashboard; interpret the graphics and end up having 2 different people looking at the same graph and coming to 2 different conclusions. AI takes out that interpretation component and analyses the data a bit further to a point of action; formulating a plan as to what you should do next. A manufacturing line is a super complicated amalgamation of machines; you do something upstream and it can affect the next process. It’s very hard to say — ‘make this small correction here because you don’t yet have a problem, but if you don’t fix it, you’re going to have a very costly mistake’. It’s that guidance that really affects bottom-line profitability — if you can get it right, you can transform the manufacturing company into something that is hyper competitive.
Mike believes to undertake the AI journey, you need to start with a value proposition; make sure that whatever comes out of the AI system has a tangible ROI. This means operating at a price point where it’s affordable to the manufacturing firm, and the value it adds to the business warrants the expenditure. For example, if scrap is reduced by 40%, what does that mean to my bottom line? Among other things, it means an immediate cost improvement, additional production capacity, lower energy consumption and less CO² emissions. When it comes to having enough data, you don’t need an historical record. It’s like the saying — ‘The best time to plant a tree was 20 years ago; the next best time to plant a tree is now’. So, if you don’t have it, just start collecting it.
As far as the concern around AI replacing jobs, Mike feels this isn’t the case at all. There’s a massive absence of ‘experts’ around the world, so implementing AI is actually creating this input to drive a production system – upskilling existing machine operators to perform better. At the end of the day, it’s effectively going to augment our current processes and make businesses more competitive on the global stage.
See omnystudio.com/listener for privacy information.
10 min