Episode #1 – A simple methodology to select the right AI technology

There’s a lot of buzz around Industrial Artificial Intelligence, but the key to getting value out of AI is having the correct focus. Different types of AI technology are needed to solve different categories of manufacturing losses – so how can you know when the vendor you’re looking at is the right fit?

In this videocast we explore a simple yet effective methodology that’s helping some of the world’s leading manufacturing executives match the right AI to their business needs.


Get the baked goods manufacturer's guide to selecting the right Industrial AI solution

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Episode #2 – Turning Machine Learning algorithms into process experts?

Baked goods manufacturing is uniquely complex, and each production line has its own quirks and complexities. For that reason “generic” Machine Learning algorithms can’t provide long-term value: it effectively analyze the data, you need to understand the context behind it – buffers, loops, parallel processes, dynamic traceability, etc.

At Seebo, we’ve invented a novel way to embed process expertise into Machine Learning algorithms. Here’s how it works – and why it’s so critical when trying to reduce process-related production losses like quality, waste and yield.


Episode #3 – Why can’t you reduce quality/waste losses far enough?

Net weight overweight, size variability, colour inconsistencies, quality variables… the battle for production efficiency is relentless, and each production line has its own KPIs for losses reduction.

Why is it so hard to reach your KPIs? Why is it that, after a certain amount of success, there is always a level of waste, quality or yield losses that defies your best efforts?

We surveyed hundreds of manufacturers, including many of the world’s leading baked goods producers, to find the answer to this question.


Close the Complexity Gap to reduce quality & waste losses

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Episode #4 – Reducing waste (and quality & yield) losses with Automated Root Cause Analysis

Traditional methods of root-cause analysis are inherently limited – and they’re preventing your production line from reaching its efficiency potential.

In this video we demonstrate how Automated Root Cause Analysis reveals the hidden causes of production losses that are invisible to the human eye.


Episode #5 – The winning strategy for adopting AI at a baked goods manufacturer

There are different approaches to adopting AI or advanced analytics across a manufacturing organization. But there’s only one methodology that both minimizes the risk of failure, while maximizing your potential to succeed with such a game changing project.: the Lighthouse Strategy.

We’re so confident this method works, that we recommend it to all our customers. And it does work – every single time.


Barilla cut waste 37% with Industrial AI - in 4 months!

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Episode #6 – So you know you want AI – which production line gets it first?

In the previous video, we discussed why picking a specific line to start with is the best approach when rolling out Industrial Artificial Intelligence, rather than implementing organization-wide.

But which production line should you start with? One that’s already performing well, or a particularly low-performing line? Should you prioritize a data-rich line over one without any data, or is that less important than you think?

In this video, we give a definitive answer based on what we’ve seen work at dozens of baked goods manufacturing lines.