Recently, I had the pleasure of hosting a roundtable discussion with senior executives from six leading global food manufacturers, on the topic of “Artificial Intelligence and Food Manufacturing – Opportunities, Challenges & Strategies”.

The panel took place at the Industry 4.0 Leaders Summit, and included senior executives from Nestle, Pepsico, Mondelez, Danone, Bimbo and Aryzta – so it was a really exciting opportunity to hear firsthand how leading food manufacturers are approaching such a hot topic.

The panelists all shared their own unique experiences of adopting and leveraging Industrial Artificial Intelligence to gain a competitive advantage. As you can imagine, there were a lot of interesting insights and recommendations, particularly for other manufacturing executives who are thinking about embarking on a similar journey.

In this post, I want to list some of the key takeaways (You can watch the full recording here.)

Artificial Intelligence is just a means to a goal – focus on the goal!

Our first panelist – Eugenio Alvarez, Associate Engineering Director at Mondelez – put his finger on a common mistake when it comes to adopting new manufacturing technologies, and suggested a simple way to avoid it:

“Many people consider AI and digital solutions is like a journey to get to, (but) for me it’s a journey to use – it’s a means and not a goal.

So we’ll always say first connect with the business and link the technology where the business needs it most.

This was a theme that almost all of our panelists emphasized. The successful adoption of exciting new technologies like Industrial AI relies on one thing: meeting your strategic business goals.

It sounds obvious, and of course most executives know this intuitively (nobody is out there simply searching for “cool technology” to buy.) But quite often when these kinds of “innovation” and “digitization” projects do hit a snag, it’s because somewhere along the line the technology simply isn’t right for the business problem they’re trying to solve.

Sometimes that’s because the technology doesn’t live up to the promise — but at least as often it’s actually because the business case wasn’t clear enough from the start. Once the business case is crystal clear and focused, it’s a lot easier to zero-in on the right technology.

As Lisa Gofna Rubin, Open Innovation Manager at Nestle-Osem put it:

“It’s not about testing a new technology – it’s about testing a technology that we know upfront will make a difference for the business.”

The only way to know that “upfront” is to go into the process with a crystal-clear understanding of the business needs you want to address.

Here at Seebo, we actually developed a simple methodology for matching a specific manufacturing challenge to the right Machine Learning / Artificial Intelligence technology. We call it the Industrial AI Quadrant, and you can learn more about it here.

Why manufacturing giants hook up with startups

Here at Seebo we’ve been privileged to work with and learn from some of the world’s largest manufacturers. In the food industry that includes the likes Pepsico, who have an entire organization (Pepsico Labs) dedicated to sourcing and working with startups to accelerate innovation.

Of course the immediate question is why? What does a giant like Pepsico have to gain from startups like Seebo?

Anna Farberov, Head of Global Venture and Innovation at Pepsico Labs, offered an insight that many other large manufacturers can surely relate to:

“We really didn’t have a choice, startups are driving the pace of innovation. To be innovative, to be really out there and to develop as a company, we need to be very good at working with startups.”

That recommendation did come with a word of caution, however, as Anna emphasised how — just as with selecting a technology — it’s important to carefully consider which startups to work with.

“There are many startups out there, many amazing technologies — but many of them are very early-stage. So they won’t necessarily have the traction and the experience. So how do you tap into the global ecosystem of startups and how do you make sure that you have access to the best of the best? You’re not wasting time on startups that will not succeed eventually or will not be able to deliver eventually?”

Her answer? Again, it comes down to focus:

“…Focus on the right business needs, focus on the right business teams, stay close to the problem and partner with the people who will be executing the solution, because it’s very easy to stay in your corporate ivory tower and that know how this is going to be implemented and this is how it’s very easy to fail.”

How to make an “excellent” production line even better

Technologies like Artificial Intelligence are often viewed as a solution to a painful business problem.

This is certainly the most common use case we see among our customers, across many industries. For example, a production line with particularly high losses, or a line with serious capacity issues. Of course, this is a perfect scenario in which Industrial AI solutions can help, by revealing the hidden causes of losses and process inefficiencies — as many of our panelists noted.

But AI can also be applied to production lines and factories that are already performing above average — or even exceptionally well — in order to take them to the next level of process efficiency (what we at Seebo often refer to as “process mastership”).

A number of our customers use the Seebo solution to do precisely that. Once their existing resources have been successfully employed to squeeze out as many process inefficiencies as possible, they use Seebo to eliminate the most stubborn inefficiencies and losses that still remain.

This is the case with Danone, whose Senior Process Manager, Rene Waalewijn, described his journey with Seebo — and offered three tips to other manufacturing executives on how to “think outside the box” and reach new heights of process efficiency.

“First of all reconsider your basic assumptions. By doing that you can take the process efficiency to the next level. Secondly, look at the market needs and requirements; ask yourself, is there a better, faster, cheaper way to deliver them while still providing, of course, a superior product to the markets? And then last but not least, work with a vendor that cares about those needs as much as you, don’t look only to technology but also look for a solution to a problem.”

Artificial Intelligence is already driving manufacturing losses reduction

Of course, one of the most significant takeaways from this roundtable was the fact that all of these major manufacturers are already using Industrial Artificial Intelligence. Each of these household names are adopting and incorporating AI in their day-to-day operations, to address some of their toughest business challenges.

And they’re succeeding. They’re reducing waste, improving quality levels and increasing yield and throughput – which is why these companies, along with many others, are investing increasingly more on Industrial AI technologies

For more fascinating insights, watch the full roundtable featuring thought-leaders from Nestle, Mondelez, Bimbo, Pepsico Labs, Danone and Aryzta: