The Manufacturer Podcast – Technology Series, Episode 2: Industrial Data Summit

Posted on 6 May 2022 by Tom St John

In this second episode of series three of The Manufacturer Podcast, the team look back on last week's event - Industrial Data Summit 2022

Good day to you listener, we have the second episode of our Technology Series recorded and ready for you to listen to. Sit back and ponder, as we bring you a handful of interviews recorded at this year’s Industrial Data Summit, which took place on Thursday 28th April at Villa Park in Birmingham.

We hear from four keynote speakers from the event – Harry Minns, Systems and Data Controller at Williams Racing discusses how data management and analytics can drive performance, giving examples from Formula 1.

Jon Stammers, Theme Lead for Connectivity and AI within the AMRC’s Integrated Manufacturing Group, talks about data-centric manufacturing and some of the benefits but also the challenges that manufacturers can have moving to that approach.

Mircea Oprisan is Director of Advanced Analytics at Mars, he touches on an overreliance on technology when it comes to managing data. It’s critical, in his opinion to understand what the performance challenge is first before thinking about the data and technologies to implement.

And lastly, Marla Nelson Head of Data Driven Transformation and Culture at Jaguar Land Rover, spoke on the topic of organisational structure and buy-in for data strategy.

In between these interviews, Conference Production Manager Ashley Oulton joins Tom and Joe from the editorial team in the studio.

Listen here 

Interviews from Industrial Data Summit

Harry-Mins-Systems-Data-Controller-Williams-Racing

“Data and our scope of work is very challenging, because we change so quickly. We can’t do engineering changes as you would in a manufacturing space, because things could change from one minute to the next and already be on the spindle being built before we’ve had chance to load the data into the system. So, the speed at which we work is a challenge in order to keep our data accurate. And that makes it very hard to look forward into how we’re going to forecast and run MRP and standard manufacturing processes.

“This also links back to our vendors who are very niche in what they do. A lot of them wouldn’t be able to link into an ERP system using any modern technology; many are still operating out of workshops on an individual basis and only supply Formula One. Therefore, we don’t have the same leverage that a mass manufacturer would have, so we’ve got to be very careful how we treat our vendor data as well as our material data.”

Jon Stammers, AMRC

JON STAMMERS THEME LEAD FOR CONNECTIVITY AND AI AMRC

“Data centric manufacturing is about understanding why you are embarking on a data journey, and being mindful about the data you are trying to capture; not just capturing data for the sake of it.

“Quite often people think the first challenge is around what data to capture. Actually, that’s one of the last questions you should be asking. The first should be why you are doing it. What is your vision for your data centric approach in the future? There must be some fundamental business questions that you are trying to answer, otherwise you wouldn’t be looking towards your data in the first place.

“So, define your vision and work out why you want to start using more data. Don’t just start collect data and store it for a rainy day, assuming that at some point it will be valuable and you’ll be able to use it. There’s a misnomer that data is the new oil – oil has a value when it’s stored, data does not. If you’re not doing anything with data, then it has no value.”

Mircea Oprisan, Mars 

Mircea-Oprisan-Director-Advanced-Analytics at Mars

“If you look back at some of the technology developments over the last 10 to 15 years, it’s probably now fair to say that technology has finally caught up with the thinking.

Computer power is much more accepted and accessible and the cost of technology has gone down dramatically, which allows I believe, businesses to embrace that opportunity and thrive in unlocking performance that maybe wasn’t easily achievable historically.

“I think businesses have become very good at maximising performance in particular business silos. When it comes to manufacturing within the four walls of a plant, businesses achieve some good levels of performance across, for example, logistics and supply chain. But what that has exposed is, in essence, an area of inefficiency, which sits at the boundary between business silos, or functional silos. That’s where the big ticket items tend to sit nowadays.

“It’s about breaking the business silos and breaking the business processes in areas of collaboration. I think data and analytics is uniquely positioned to unlock that, because you’re not bound by operational construct or organisational designs. You can do that just with the help of data.”

Marla Nelson, Jaguar Land Rover

Marla-Nelson-Jaguar-Land-Rover

“Jaguar Land Rover is on a journey to transform and become more of a digital business. Digital is behind everything we do and we can’t do that without data. We’ve set up a brand new function called InDigital that drives how we support and achieve our strategic objectives across the business. Data is fundamental to that.

“It’s about making sure we can maximise the returns from our data. This is done by putting governance structures in place and equipping people with the right sort of skill sets and mindsets. Over time, we’re changing the culture, we’re making sure that decisions in the boardroom and across the business are driven by data. That means people have got to start collaborating more by sharing data, talking with partners more widely about how data is going to be used and exploring how can we maximise that.

I think if you’re not using data effectively in your business, you’re not going to be fit for the future. In order to be a long term, sustainable and successful business, you’re going to need data and people that can understand data, glean insights from data and do the clever analytical data science and data engineering behind all of that AI. You just won’t be here in five year or 10 years time if you don’t have that.”

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Listen back to the first episode of this series