Key takeaways from The Manufacturer’s Industrial Data Summit 2022By Bryan Difford on May 4, 2022
Recently a handful of the Peak manufacturing team attended the Industrial Data Summit (IDS).
Held at Villa Park in Birmingham, the IDS is the UK’s most senior gathering of manufacturing data professionals – created to ensure that data-minded manufacturers can come together for the fifth year to talk about the role of data and analytics in their businesses.
It was a fantastic event, with lots of great networking opportunities and some fascinating conversations with a wide range of manufacturing organizations, discussing their pain points, challenges and the ways intelligent data can help. Here, I’ve rounded up some of the main takeaways that the team took away from IDS 2022…
Having the correct skills
This was a recurring theme throughout the event, and was without doubt the single biggest issue seen across all the businesses that attended the summit. Even though the sector is currently struggling to combat shortages of semiconductors, raw materials (palladium, aluminium and nicol to name a few) – and is facing ongoing supply chain issues caused by COVID-19 and Brexit – finding the right people is still the hardest and most influential problem faced by manufacturers.
On this theme, a key learning was that manufacturers would rather bring in graduates that they can train up (knowing that they have a shelf life of around two years) rather than turning to outsourcing. There seemed to be a strong belief held by manufacturing leaders that outsourced partners don’t particularly care about outcomes!
The concept of procuring outcomes directly from a third party is still viewed as a risky approach to running projects in the manufacturing space. Unique skill sets, coupled with high levels of regulation and legislation, have forced the manufacturing community to rather control risk through having their people be internally employed.
When it comes to sustainability (both in terms of reporting on it and the reduction of carbon emissions), most manufacturers aren’t too sure where to start from a data perspective.
Currently, many are focusing on the easiest way of understanding their carbon footprint, which is to look at sustainability from a location perspective. This provides you with simple, easily understandable metrics like electricity, water, heating and cooling.
Other assets, however, can be much harder to understand and measure from a sustainable emissions standpoint. Not all manufacturers have an effective way of generating data around a specific asset (usually meaning a sensor is required) or their data may be very difficult to measure (like compressed air, for example.)
Even if manufacturers do manage to pull useful data from all of their assets, using it to positively impact sustainability is still a struggle; simply knowing the output of an asset doesn’t necessarily mean you can reduce its carbon footprint. Assets need to be managed intelligently based on their impact on the overall process which they form part of.
This approach creates two issues. One is that it’s not affordable to have sensors on every asset that generates some form of CO2 output (so how do you figure out which assets get sensors and which don’t?) and, secondly, just knowing the CO2 data doesn’t mean you can make the assets functions more sustainable. This comes from generating intelligent insights about the assets (like optimal usage times and the overall efficiency of the process.)
AI vs. BI
There’s still a common misconception amongst manufacturers that business intelligence (BI) and artificial intelligence (AI) are one in the same. The ability to add “uncertainty” into a model in order to predict what will happen in the future still seems to be something of a foreign concept to many. The distinction comes when interrogating the data.
BI is a great mechanism for visualization of data and looking for insights at a specific point in time. AI, on the other hand, is used to understand, with some certainty, what will happen in the future and how to react to changing circumstances. That, coupled with applying intelligence to your decisions, is what makes Decision Intelligence so powerful.
IDS attendees were very keen to look at ways of making their overall business more sustainable, rather than focusing their attention purely on manufacturing a more sustainable product. The costs and change management associated with taking on this type of project seemed too risky of an approach.
The easier approach is to look at your overall value chain and try to affect your carbon footprint by looking at areas where the business can optimise with little impact on operations.
Businesses are starting to ask questions around how to optimize inventory, stock and logistics to lower carbon emissions across the supply chain. Focusing on short term CO2 gains also allows business buy-in from executives because they will be realizing carbon reductions in small bite sized portions.
Because of this, manufacturers are looking for a roadmap of items to work on that would lead to reductions in CO2 emissions. This would create buy-in from the business, thanks to demonstrable, small, incremental results.
On the wish list for most attendees was a templatized approach to Scope 1 and Scope 2 manufacturing sustainability reporting. Some were interested in how they can focus their data teams around carbon emissions in particular. What inevitably ends up happening is that analysts get caught up running in all kinds of different siloed directions, and are never really focused enough to deliver sustainable outcomes.
By providing manufacturing teams with a centralised tool for all of their carbon controlled data, from which everyone can work from, would allow data teams within manufacturing organisations to become more efficient, using data from across the business to efficient carbon neutral practices across the business.
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