Overcoming innovation challenges in manufacturing with Google Cloud and Siemens

Smart factories, Industry 4.0, digital twins, personalized production, robotics - there are endless possibilities facing manufacturers when thinking about new digital capabilities. The appetite for adopting digital tools and doing things differently, particularly when thinking about improving efficiencies and resiliency, has only been amplified by the COVID-19 pandemic. 

However, manufacturers often operate in highly complex environments. And they are often faced with having to navigate intensely regulated landscapes, with safety being the number one priority. Not to mention, many large manufacturers have a long history in industry, which brings with it challenges around adopting new processes and thinking about different outcomes - as well as ageing systems and complex data structures. 

All of this often makes innovation very difficult. This was the topic of a recent webinar featuring both Siemens and Google Cloud, where leaders at the companies discussed how manufacturers can get innovation projects off the ground, so that they aren't just sidelined as ‘fun R&D'. 

Google Cloud's upcoming Digital Manufacturer Summit will also see Google executives and customers discussing how organizations in the industry are embracing cloud and data tools to boost productivity, reduce costs and improve quality. You can register for the event here and access all of diginomica's coverage in our dedicated events hub here.

Speaking during the recent webinar, Laura Merling, Managing Director of Digital Transformation at Google Cloud, explained that whilst large organizations are often not short of ideas, it tends to be the execution of the ‘innovation' that leads to failure. She said: 

One area that I always find interesting is expectations. I was working with a company on innovation and creating a new portfolio - and they said let's try to get $7 billion out of this new idea in five years. I was like if I do that I get to be CEO. Going from zero to $7 billion in five years is a pretty big ask. 

So it's setting the expectation of what the outcome is. Sometimes it's ‘hey I'm this big company, I should have this big outcome', when organizations need to adjust that based on understanding the current business models and the landscape and the flow of it. 

Merling added that process is another stumbling block. For example, in automotive or aerospace, the requirements around an organization that are tied to safety and regulation are very stringent. However, sometimes this mindset gets applied to everything, when it doesn't need to be. She said: 

A lot of the processes that are put in place are designed to protect everyone, but this particular piece of software I'm going to buy isn't going to touch a human, so I probably don't need to go through all the same rigour from a regulatory perspective. It's kind of stepping back and thinking about what it is that you're trying to do, and removing those processes.

Andrea Kollmorgen, Head of Connected eMobility at Siemens, said that when you're a company that is almost two centuries old, uncomfortable habits can be formed and these can be restrictive when it comes to adopting and executing on new ideas. Whilst Siemens should be proud of its legacy, she said, it shouldn't assume that what has come before will work going forward. Kollmorgen explained: 

I look at it as a bit of a cultural challenge, from a psychological perspective. I mean if we look at how we grow up, we do something good and we repeat it over and over again. That translates all the way up to turning in good results for that quarter - we're probably going to look at what we did in the last quarter, and we're gonna try and repeat it. 

You know we maximise the serotonin for ourselves and for our teams, and just repeat. Innovation, and especially corporate innovation, asks us to basically take a step back, open the aperture a bit, and rethink all of our assumptions that we've made. 

Making innovation a reality

Both Merling and Kollmorgen offered manufacturing organizations some practical advice about how to gain adoption for innovation projects internally. This advice focused on specific solution areas, as well as more broadly on company processes. 

For example, Merling said that whilst every manufacturer wants to be in Industry 4.0, or have a smart factory, what tends to happen is that these projects tend to happen in isolation across many different facilities. This means that if an organization has 130 manufacturing locations, each of them running pilots, how do you ever get the scale and momentum you desire? She said that archetypes and centres of excellence could prove useful: 

I partnered with a company that created archetypes. So they ended up with five different manufacturing archetypes across their five different facilities. And then the idea was to create a centre of excellence for each one of those archetypes. Set up one location to make it really successful, and use the team from that archetype of a manufacturing environment to then work with the other manufacturing locations that….were basically similar in nature. 

That way you also had almost a train the trainer, people that had done it, had achieved those outcomes, so you also had some resource accessibility.

Merling also explained that she's worked with organizations that have identified  between seven and 15 digital twins across the business, but solving for all those twins is difficult from a complexity perspective. The key, she added, is to think about it from the value to the end customer. Not just how it will help the business. Merling said: 

Usually where there's customer pain, there's some efficiency issues internally. 

Looking at aerospace, you could say I want my PLM system, tied to my MRO data, my manufacturing parent operations data, to my real time aircraft data...if I could create a view of that across a particular set of parts or components or particular craft. 

And the real reason for doing that is to ensure uptime for the airline - measuring it based on uptime or lag, reducing aircraft on ground. So it's really thinking, what's the end value which determines your data streams that you need?

Adoption is key

Whilst understanding outcomes and thinking through use cases is important, so is ensuring that you have a plan to bring customers and the business along with you. Merling said that large organizations tend to have a philosophy of ‘build it and they will come', because of a history of success. But this isn't always true and manufacturers need to really think through how to drive adoption, in order to ensure innovation projects are successful. 

Kollmorgen agreed and said that this has been a high priority in her role at Siemens, trying to ensure that she gets concrete adoption for her ‘day after tomorrow innovation' (which in Kollmorgen's work is autonomous vehicles). She explained: 

What's important in both that external and internal adoption plan is to make sure that both the customer and our businesses don't look at this corporate innovation topic as a playground or as just a far out R&D activity. I have the responsibility to tie this back to concrete customer value. 

I have to tie it back to existing Siemens structures, existing Siemens processes, or create them new. But you have to take the organization along with you and not be this external organism that suddenly comes back with a brand new idea and treats this as something that's...threatening. 

I don't say that from any disrespect to the existing business, because for me it's really building out of this core relationship that we have, especially in the manufacturing of auto, that leads into: how do we take digital technology, simulation technology, the dawn of autonomous vehicles, and turn that into a new value proposition for our customers and for Siemens?

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For more diginomica stories from Google Cloud's Digital Manufacturer Summit visit our event hub. The Google Cloud Digital Manufacturer Summit opens on June 22 2021 and registration is now open. 

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