Root Cause Analysis in the Age of Industry 4.0

Every manufacturing process has its inefficiencies — and these inefficiencies often lead to losses that harm their bottom line.

These losses take many different forms: be it from yield, quality, waste, throughput, energy, emissions, factory downtime, or something else. Usually, the more complex the process, the more complex the problems tend to be — and that’s where root cause analysis (RCA) comes in.

Process experts or process engineers use root cause analysis to trace a particular loss to its cause, in order to eliminate it. Of course, while that sounds pretty straightforward, the reality is often quite different.

Why is it so hard to find the root cause?

Sometimes, it’s relatively simple to solve a particular problem – particularly when that problem stems from a single cause, or a factor that’s easy to spot. For example, if the temperature at a particular point in the line strays from the permitted range, then a process expert will usually be able to identify and solve the problem themselves fairly quickly.

But very often this is not the case. Sometimes, the problem is caused by a complex combination of factors, making the root cause more difficult to understand. This is particularly true if the cause is rooted in the interrelationships between numerous tags, and their place within the process. This can mean no single tag is behaving problematically, but that the problem is rooted in the process itself – for example the speed at which the raw material travels from Tag A to Tag B; or its temperature or concentration at a specific point within the process; or the pressure it is subjected to between point A and point B, and so on. The options are endless.

Then of course there is the fact that the problem identified is actually just a symptom of a more fundamental issue, and the root cause may actually be the second or third derivative. Perhaps the issue began further upstream in the production process, and only became noticeable later on.

In any of these scenarios, nothing is amiss to the naked eye. This is why most manufacturers simply come to accept a certain amount of production losses: they’ve applied their smartest, most talented process experts and advanced tools to the problem, and simply couldn’t see what was wrong. What else can they do?

Shortcomings of traditional root cause analysis

As mentioned above, the general approach currently used by many manufacturers when it comes to root cause analysis is to rely on on-site expert knowledge, aided by a range of analytics tools.

Experience and process expertise is of course invaluable.

The problem is, for many complex processes, it isn’t humanly possible to analyze all the combinations of all the data tags on a production line, all the time.

In our conversations with hundreds of manufacturing executives, including many from leading global brands, the same limitation was consistently highlighted: advanced analytics are excellent for validating existing theories, but are much less useful for discovering the hidden root causes of persistent production problems. That’s because, even with the most sophisticated such platforms, there is always an inevitable blind spot, as the process expert needs to select a handful of tags based on their own human intuition and biases.

It’s natural that even experts can be biased towards certain ideas. Even if the root cause of the problem is roughly identified, there may be inaccuracies in the definition of the problem, making it difficult to come up with an intelligent and lean solution.

Other disadvantages of manual root cause analysis include:

  • Often, most RCA information isn’t shared across manufacturing sites, as manual analysis doesn’t scale. This leaves factories of the same company – or even individual lines within the same factory – to repeat each other’s mistakes, leading to losses that could have been avoided.
  • Manual RCA is conducted on an ad-hoc basis. But as manufacturing processes are dynamic, the data is constantly changing, so the analysis can quickly become redundant.

The Power of Automated Root Cause Analysis

Automated root cause analysis harnesses the power of Machine Learning — a subfield of Artificial Intelligence that focuses on developing and researching algorithms that learn from data. The algorithms exist in the form of models which are trained with historical data in a way that allows them to make predictions and decisions based upon new data.

Thanks to significant advances in machine learning and Big Data analytics, root cause analysis can be performed using automated methods. These methods are unbiased and based purely upon historic and real-time data from the production floor, infused with process expertise (more on that later).

Just as importantly, they take the Sisyphean task of analyzing and interpreting data away from the people on the factory floor, thereby enabling them to focus on actually optimizing the processes and improving performance.

Download the complete white paper to learn how Automated Root Cause Analysis works, and the real impact it’s having on manufacturers just like you.


Watch: How to Embed Manufacturing Process Expertise into Artificial Intelligence

For Industrial Artificial Intelligence to have a real impact in solving production problems, the algorithms must have a deep understanding of your production process. Watch our latest Vlog below to learn how it's done - and why it's so important.

WATCH: Talking Smart Manufacturing -- The Predictive Quality & Yield Series: Vlog #2

Transcript:

[Eden] Hi everyone, it's Eden from Seebo. Welcome again to our V blog. Our subject for today is how to embed process expertise into artificial intelligence and why it's so important. Here with me again is the co-founder and COO of Seebo, Liran Akavia.

[Liran] Hello, Eden.

- Hi, Liran. Liran is an expert in the field of industrial artificial intelligence and reducing manufacturing losses, and has extensive experience working with the world's leading manufacturers. Liran, we all know that process expertise is important. This is the key to meaningful process analysis. Many manufacturing executives that speak with us are asking, "Can algorithms solve my process inefficiencies when there is no process expertise in the algorithm?"

- So it's a true concern. Honestly, algorithms without process understanding will have no value to the manufacturing team. Just algorithms to analyze a production line will provide insignificant results.

- Okay, Liran, I get that. So can process expertise be embedded into the algorithm?

- In short? Yes. And I'd like to show you something. In my opinion, it's very interesting. I'd like to speak a little bit about how the algorithm is thinking about the data that is coming from the process and what is the big difference that is made when the algorithm actually understands the process. Let me take you through a quick journey.

- Okay.

- So a traditional algorithm, let's say just for this example, we look at three temperature data sets or tags. This is temperature data number 532, and this is temperature data number 667, and temperature data number 780.

A normal algorithm will simply look at this data and will try to understand if it's within the value or a little bit about its behavior, and in most cases, will come back with no meaningful results. Now, let's take a look on how Process-Based Artificial Intelligence is looking at the data, or how algorithms that actually understand the process look at the same piece of data.

So just for this example, let's say that there are three assets, asset A, B and C, and now when we understand the assets, we also know the physical order of those temperature sets. So we know that 780 is the first one and 532 is the second one physically, like the material is going first through asset A and then through asset B. With that knowledge, we can suddenly make a lot of beautiful calculations. For example, we can calculate the difference in time and material flow between those two temperature sensors, and by using this data, we can calculate the delta T, in this example, how much the material got hotter or colder during the time that it went between 780 to 532.

Related Video: How to Select the Right Kind of AI to Solve Your Manufacturing Losses

With that, we can calculate the first derivative that tells us the beautiful story of how the temperature is increasing or decreasing. Now with that value, we can maybe realize that the problem is not the absolute temperature parameter. The problem is the speed of increase and speed of decrease. So this is just one example of why for the algorithm to understand the plant topology and the flow of material is so dramatically important.

- Okay, Liran, that sounds really interesting, but very difficult in theory. Every production line is unique, so do you need to reinvent the wheel every single time?

- So you're right again. This is extremely difficult to do that. And when we built Seebo, we were concerned exactly because of that.

On the one hand, we put for ourselves a clear goal to provide value to manufacturers reducing the process inefficiencies. On the other hand, we understood that algorithms with no process expertise within them will be meaningless. And on the other hand, we understood that every production line is different.

So you actually need a tool or technology that is able to take a flow diagram, a P&ID, a production description, and somehow inject it into the algorithm, and I'd like to quickly show you how it works and what makes the Seebo technology so unique in understanding the actual manufacturing process.

So the journey starts with the manufacturer's data. Usually, we look at process data, raw material data, quality data and sometimes other data sources, for example, the ambient temperature. Then we build a unified multi-source schema, so we actually take all the data sources and put them inside one database while they're synced and connected to each other, and then we do something that no one else does: we build a digital model of the process, technically taking the P&IDs and converting them into a digital appearance, and then using our proprietary tools, we automatically convert it to code and inject it into the core process algorithms.

Now, the core process algorithms has in them the data and the plant topology, the flow of material, and the algorithms can do incredible calculations, for example, calculate dynamic traceability, understand buffers or production loops or even parallel processes. We can suddenly clean the data in a meaningful way. We understand the batch and changeovers and many, many other things, and the result is being digested through a straightforward, ready-to-use interface for the production team.

So if I'm going back to your core question today, algorithms with no process expertise will probably provide poor results. Embedding process expertise is a difficult task and we took upon ourselves this task to solve it and make it scalable.

- Thank you, Liran, for those meaningful insights, and thank you everyone for watching us and for those incredible questions you are sending. See you again, next time.

To learn more about Process-Based Artificial Intelligence and how it can be used to reduce production losses, check out the Seebo website.


Chemical Process Control - Optimizing Manufacturing with Industrial IoT

Watch: What "Dramatic Impact" did Seebo Make for this Chemical Resins Manufacturer?

Watch the full interview here:

Chemical manufacturing can be a highly-complex business. It's a constant battle to fend-off production losses, particularly in the areas of quality and yield.

The nature of those losses differ from manufacturer to manufacturer -- and even between different factories and lines within the same company. But very often, these losses have one thing in common: they are caused by inefficiencies in the production process itself (as opposed to, say, problems with individual physical assets on the line).

Allnex, a leading resins manufacturer, were struggling with a particularly painful problem. Their production process was very harsh on their equipment, leading to significant periods of factory downtime, as teams needed to be mobilized to clean and restart the machinery. This posed a major challenge in a competitive marketplace, by hampering their ability to meet growing demand.

As Global Operational Excellent Manager Jim Martin explains: "The process is inefficient, and while we're running the equipment is constantly being attacked by the process. The product is growing in the marketplace because of its eco-friendliness, and we wanted to be able to respond to the market... and produce more, more quickly."

Removing AI skepticism, with clear-cut results

To root-out this problem once and for all, Allnex evaluated a number of Industrial Artificial Intelligence vendors, each of which promised to eliminate the causes of their losses -- and every one of which ultimately failed to deliver. It's a story many chemical manufacturers will find all-too familiar, and it led to a sense of skepticism about the practical usefulness of AI technology.

But when they turned to Seebo, that skepticism quickly dissipated. Jim and his team were particularly impressed by Seebo's "process-oriented" approach, in stark contrast to other AI and Machine Learning vendors.

"We had a number of companies that we looked at. Seebo was the only one that was process-oriented. Many of the others were data-oriented, and they did not understand the process," explained Jim.

"Significant financial success"

Jim describes the process as an "iterative" one, that over time has had a "dramatic impact".

"Using Seebo has put us on the path of significant financial success. It's like having a very skilled, very talented engineer watching the process 24/7."

"We no longer have to spend time monitoring and figuring out the interrelations (between tags) -- the Seebo solution is doing that for us," he continued. "It's made the engineers' life much simpler.

"It's eliminating process inefficiencies, it's improved quality, it's improved yield -- and I cant say enough about the collaborative relationship, and their desire to get meaningful results."

The following is a full transcript of the video testimonial above:

There's a lot of confusion and skepticism on AI and what AI means...

Using Seebo has put us on a path of significant financial success. It's eliminating processing inefficiencies, it's improved quality, it's improved yields. It's had a dramatic business impact.

I'm Jim Martin with Allnex, we're a global, leading resin manufacturer.

The product we create at Allnex is very unique in the marketplace. It offers flexible adhesives, non-yellowing sealants, water-based polyurethane dispersions.

The process is inefficient and while we're running, the equipment is constantly being attacked by the process.

The product is growing in the marketplace because of its eco-friendliness, and we want to be able to respond to the market and be able to produce more and more with the equipment.

When we looked at choosing an artificial intelligence partner, we had a number of companies we looked at. Seebo was the only one that was process-oriented, many of them were data-oriented, and they did not understand the process.

We chose Seebo because they were very focused on the process, very focused on business results, and we were confident they could deliver those results.

One of the things the tool does very well is, it's like having a very skilled, very talented engineer watching the process 24/7, watching it the same way every time.

Having that confidence that the process is being monitored correctly, allows us to look at, where else can we use the tool? Where else can we spend the time? We no longer have to spend time monitoring or figuring out  the interrelations -- the Seebo solution is doing that for us.

It's made the daily engineer's life much simpler. There were many interactions with the process, the engineer had to take once a day, once a week, and constantly making interventions. Those interventions have dropped down drastically.

It just gives us the ability to not dwell on the processing issues, but dwell on the processing opportunities.

It was refreshing to work with a vendor that had exactly the same interest that we had.

It's eliminating processing inefficiencies, it's improved quality, it's improved yields,  and I can't say enough about the collaborative relationship and their desire to get meaningful results.

Want to see how Seebo can drive quality and yield improvements for your business? Book a free personalized demo, and we'll show you!


Case Study: How a Baked Goods Manufacturer Reduced Quality & Waste Losses by up to 70%

Process-driven production losses -- particularly due to quality and waste -- are a challenge for any food manufacturer. Sometimes, the cause is fairly straightforward; but what do you do when your process experts can't find the root cause?

This is a common problem for manufacturers in all industries. But for one international manufacturer of breads, crackers and biscuits, shrugging off those losses wasn't an option.

One of their production lines in western Europe was suffering persistent losses in quality and waste, specifically:

  • Rejects due to net weight underweight
  • Burned/over-toasted bread
  • Size and shape variability issues

Their process experts were using an advanced self-serve analytics platform -- but still were unable to locate the root cause of these losses. To their puzzlement, in most cases where losses occurred, no clear process  inefficiencies were apparent, and the data tags they investigated appeared to be operating within the permitted ranges.

Predictive Quality & Yield to the Rescue

That changed shortly after they implemented the Seebo Predictive Quality and Yield Solution. Using the Seebo solution, the company’s process experts were able to identify the hidden causes of their production losses, and gain clear recommendations as to how to prevent those process inefficiencies. Furthermore, production teams received proactive alerts as soon as those inefficiencies were detected - enabling them to prevent losses before they occurred.

Related article: Predictive Quality - Why is it Critical for the Food Industry?

This breakthrough eventually translated into a major reduction of waste and quality issues -- up to 70% for some! Ultimately, the company was able to save hundreds of thousands of euros each year on that production line, by preventing some of their most damaging production losses.

Want to learn how they did it? Read the case study below:


Predictive quality analytics in manufacturing

Predictive Quality: Why is it Critical for the Food Production Industry?

The Case for Predictive Quality

Food quality has always been a critical factor in the food production process, requiring food manufacturers to abide by stringent quality regulations, inspections and statistical quality control methods.

The impact of poor food quality is severe, with direct bottom-line consequences. Poor product quality reduces production yield, can damage the company’s brand and reputation. More immediately, quality issues are one of the most common causes of production losses for food manufacturers, together with other, largely process-driven losses such as waste. These losses translate into a significant dent in yearly revenues.

Conversely, meeting high-quality standards can reduce manufacturing costs – internally and externally. Internal costs emerge from problems associated with the product before it is delivered, e.g. shortages, waste, and delays. External costs arise post delivery - through recalls, lawsuits and warranty costs - and constitute a major expense for the food industry, resulting in a staggering $7 billion loss annually.

Food manufacturing operations turn to processes and tools to sustain high-quality production in addressing regulatory compliance, risk prevention, and product traceability. While tools such as statistical quality control (SPC) have been used since the early 1920s to identify issues as early in the manufacturing process as possible, they deal with problems that have already occurred.

Predictive Quality is an emerging category of Industrial Artificial Intelligence solutions, that provide manufacturers with the means to significantly reduce process-driven losses in quality and waste, by pinpointing the root cause quickly and with a high degree of confidence, and preventing those losses before they next occur.

Predictive quality management in food industry

Defining the "Quality" in "Predictive Quality"

In the food industry in particular, "quality" can mean a number of things.

For example, there are laws governing food quality in many countries, as well as international regulations which are important regarding globalization and the increasingly complex food supply chains. These laws and regulations assure food safety and a minimum level of quality for the health and overall benefit of consumers.

While certainly a very important topic, this is not the quality challenge that Predictive Quality addresses. Rather, Predictive Quality tackles production losses caused by inefficiencies in the process itself (rather than due to individual assets for example).

In fact, Predictive Quality isn't limited to quality-related losses, but also addresses other common process-based production losses. In the case of the food industry that typically includes waste and yield. In other industries, Predictive Quality addresses anything from throughput, to emissions levels to energy consumption. The common thread is that these are all losses which - like the consistent quality and waste losses experienced in food manufacturing - are caused by process inefficiencies.

Industry 4.0 and Predictive Quality to the rescue

Fortunately, food factories can overcome many of these quality control hurdles with the use of Industry 4.0 technology, specifically predictive quality.

Predictive quality technology provides manufacturers with 3 key capabilities to optimize their processes, thereby minimizing losses due to quality issues, as well as other process-driven losses e.g. waste, yield, and so on.

  • Automated root cause analysis
    Predictive Quality provides the process engineer with the tools to reveal previously unknown root causes of quality issues in the production line, via automated root cause analysis.Most food production lines have certain recurring losses that can't seem to be traced to any specific visible cause. This is particularly true for more complex food production processes. That's because with so much complex data to analyze, process engineers and experts are largely left to follow their intuition in selecting which of the hundreds, even thousands, of data tags to investigate. Naturally there will always be a blind spot - and the more complex the inefficiency, the less likely even the most eagle-eyed process expert is to find it.By contrast, a predictive quality solution uses Artificial Intelligence to do what human beings can't: continuously analyze all the relevant data (ERP, MES, Quality systems, data lakes, etc) at scale - including complex interrelationships between the different data tags. This provides process experts with the ability to pinpoint the precise combination of factors that are causing a particular quality problem, no matter how complex.
  • Predictive recommendations 
    A key component of Predictive Quality is predictive recommendations. Predictive recommendations use the insights from root cause analysis to identify the optimal process setting, enabling manufacturing teams to minimize quality issues as much as possible.

    Through continuous, multivariate analysis of production data, predictive recommendations will provide the precise optimal range of values for any given combination of tags.

  • Proactive alerts for real-time action
    Of course, all of this intelligence must be translated into timely action to provide real value. Proactive alerts are delivered directly to production teams in real-time, as soon as a process inefficiency emerges - i.e. a problematic combination of tag behaviors, as identified by the predictive recommendations. This enables front-line manufacturing staff to act to prevent losses before they occur. Ideally, the alert should include as much actionable information - for example, not only identifying the problem, but also providing clear Standard Operating Procedures for addressing them.

Predictive quality analytics in manufacturing

Predictive Quality empowers quality teams to anticipate and proactively address quality problems before they arise - making sense of complex data patterns to determine areas of greatest quality risk and assign production floor resources before risk becomes reality.

The business gains of Predictive Quality are clear and compelling, providing food manufacturers with a competitive edge in an era where every last drop of efficiency counts.

Check out this case study for a detailed look at how one particular food manufacturer succeeded in drastically reducing quality and waste losses, using Predictive Quality:


Watch: How to Select the Right Kind of AI
to Solve Your Manufacturing Losses

Industrial Artificial Intelligence can hold the key to solving some of the most complex and painful manufacturing challenges. But every manufacturing process is unique – as are the challenges that come with them. And different types of manufacturing challenges require different types of AI to tackle them.

With all the noise in the market around “Industrial Artificial Intelligence”, “Industry 4.0”, “Smart Factories”, “Machine Learning” etc, it can be hard to figure out which AI technology is relevant to tackle your specific problems. In this mini-webinar — the first in our new Vlog series — we reveal a simple but effective methodology to help you make sense of it all.

WATCH: Talking Smart Manufacturing — The Predictive Quality & Yield Series: Vlog #1

Transcript:

[Eden] Hi everyone, my name is Eden. Welcome to the Seebo vBlog.

If you are in manufacturing, you’ve probably heard all the buzzwords: Industrial Artificial Intelligence, Smart Factories, Industry 4.0, Machine Learning. Today I would like to talk about a question we frequently hear
from executives in manufacturing: How do you filter out the noise to get what you need? How can you find an Artificial Intelligence technology that actually addresses the business problem?

Today with me is Liran Akavia. As many of you probably know, Liran is the co-founder and the COO of Seebo. Liran is an expert on the field of industrial Artificial Intelligence and reducing manufacturing losses, and has extensive experience working with the world’s leading manufacturers.

Hi, Liran.

[Liran] Hello, Eden.

[Eden] So, Liran, how can manufacturers match their own unique production losses with the right artificial intelligence technology? Is there really a silver bullet out there or do they need to hunt for a specific solution for each individual problem?

[Liran] Manufacturers have diverse types of losses, and there is no silver bullet. There is no one artificial intelligence technology that you could deploy in your factory and will simply solve all the problems.

The good news is that there is a light methodology that every executive can deploy for their own teams to identify the relevant losses, and what is the relevant technology that works with that.

[Eden] Can you elaborate more about that methodology?

[Liran] Yeah, so… Just before we jump into the actual methodology I would like to share with you and with the audience how computers are thinking. Obviously we are not going to do a data-science lecture today, or not even a computer science lecture, but we are going to look in a very simplified way at the ways computers are looking at manufacturing problems, and once we understand that every manufacturing executive will be able to connect between the losses to the right technology.

So let me start with a quick couple of slides.

So let’s first take a look at how humans are solving manufacturing problems. So when we look at manufacturing problems we would differentiate between frequent problems and infrequent problems. When we have a frequent problem we have many examples of the problem and as humans we will try to look for a pattern, we ask what is common between all the problems and we will try to look for the common denominator in all those problems. In other cases, when we have infrequent problems, unfortunately we don’t have enough evidence for how the problem is looking, so we would ask completely different questions. Is it a normal behavior or an abnormal behavior?

Once we understand how humans solve problems, it is very easy to understand how computers are looking at problems.In the computer, or in the professional language,the frequent problems “world” will be supervised learning, and the infrequent problems “world” will be the unsupervised learning.

Obviously this is an over-simplification of the ways computers work. But generally, for computers, when there is a frequent problem, a computer would look at many examples of the problem and will try to look for a pattern, while when there are infrequent problems, the computer will ask a completely different question: Are we in a normal or abnormal situation?

[Eden] Okay, Liran, this is really helpful and very interesting, but how does this answer my question? How do manufacturers still find the right solution for their problem?

[Liran] You are right. Actually, let’s take a look at the methodology: So, the first step will be to list your production  goals, all the losses you are looking to solve. It could be improving quality, or improving yield, or improving concentration, reducing downtime, increasing the asset reliability, process stability, energy consumption. Make sure that you have a very clear list and prioritize it.

With that list, take each one of the problems that you listed and put it somewhere around these quadrants.

So this magic quadrant goes on what drives the problem — process-driven or asset-driven — and what is the frequency of the problem: is it a high-frequency or a low-frequency problem?

Let’s take two classic examples. If I’m looking at bread baking, and on the weight of the bread. This is related to the process and usually high-frequency because the weight changes for every bread (loaf)/ The other extreme is a failing pump. It will probably fail once or twice a year, and the reason is usually the asset, is the pump itself. So the bread weight will be usually a process-driven, high-frequency problem, while the failure of  the pump will be an asset-related, low-frequency problem.

So the first step was making a list of your problems. The second step, putting them on the quadrants, and the cool thing comes now. Each one of those quadrant areas is a different technology. Let’s start with quadrant one. In quadrant one we need artificial intelligence that understands the manufacturing process — after all, this is a process-driven problem — and we need supervised machine learning. If you remember what I said two  minutes ago, the supervised machine learning is when we look at frequent problems and find the pattern, and explain why did it happen. On the other extreme, when we look at quadrant four, that was the pump example, it is an asset-driven, low-frequency problem. Here we usually use vibration or acoustic unsupervised learning, since we don’t have many examples, we simply ask if it is normal or abnormal and we do it per asset, not per  process.

Those are usually the most important quadrants. Quadrant number one will be world of predictive quality and yield, quadrant number four will be the world of predictive maintenance.

Let’s take a look at quadrant number two. This quadrant does not have its own technology, but you can convert the problem to quadrant one or quadrant four. So, if you find a process proxy — I will explain what that is in a minute — go to quadrant one. If you don’t have a process proxy, go to quadrant four. Let’s take an example. Let’s take a vacuum system, this is an asset problem, that is failing every second week. It’s a frequent problem. So that would be quadrant two. If we can, for example, look for a process proxy that would be the pressure of the pump, and we know that the pressure of the pump is telling the story of the failing of the vacuum system, that would be a classic quadrant one example. But if we cannot find a process proxy like the pressure, we need to solve it with vibration or acoustic measurements.

As for quadrant (three), process-driven losses that are low-frequency, there is a technology behind that, but usually it is not a real concern for most manufacturers. An extreme situation in the process, very rarely, is not something that many manufacturers are actually concerned about. It’s not happening always and the technology of process-driven unsupervised learning is also not very accurate.

To make a long story short, generally your problems are probably in quadrant one or quadrant four. For quadrant one you will need predictive quality and yield, process-based technology which is supervised learning, and for quadrant four you will need predictive maintenance, vibration or acoustic unsupervised machine learning  per specific asset.

Another thing that is super-important to know at this stage: no one can do it all. There is not one supplier that can tell you “I’m the best in the world in supervised learning and I’m the best in the world in unsupervised learning”. Messi is a great football player but he’s not the best in basketball. You can simply not… You cannot be the best in all.

[Eden] So essentially we would put the Seebo solution in quadrant number one?

[Liran] Yes, you are right. In Seebo, our entire world and expertise is in quadrant number one. Our algorithms are designed to solve problems that are process-driven and frequent. And because of the pure focus that we  have and due to the fact that this is the only thing we do we are the best in the world at that.

[Eden] Thank you, Liran, and thank you everyone for all the great questions you’ve been e-mailing to us. Keep sending them in, the e-mail is right there.

See you next time!

Here’s how Seebo solves process-driven manufacturing losses like quality, yield, waste and throughput.


New Funding & World Economic Forum Recognition
Bring Plenty of Press Coverage for Seebo

It’s been a busy and exciting few weeks at Seebo, capped by two very exciting announcements that have garnered international coverage.

Here’s a summary of the key highlights.

Seebo’s latest funding round

Most recently, Seebo announced our latest funding round, led by Ofek Ventures, together with Vertex Ventures, Viola Ventures and TPY Capital.

That announcement generated significant interest, at a time when manufacturers are working harder than ever to optimize their production processes — particularly in the food industry (see our recent paper “Moving Manufacturing From Crisis to Competitive Edge” for more on that topic.)

Or, to quote a recent interview around that announcement with Seebo CEO and Co-Founder Lior Akavia in FoodNavigator:

Companies are not only looking to cut losses, increase efficiency, and improve their financials… they are also under pressure to maintain supply in he face of unprecedented spikes in demand. “For quite a few of our manufacturers, demand for their food products increased in the dozens of percentages after COVID-19 started,”…

Manufacturers were left with two choices: to either build new production lines to meet demand — but “this takes years” and “huge investments”, said Akavia — or optimise their processes, which can “quickly translate into increased capacity.”

Other publications which covered the funding announcement included industry and tech sites like VentureBeat, FoodProcessing.com, Bakeryandsnacks.com, and innovator.news, as well as several Israeli business and tech sites, including Globes, Calcalist and NoCamels.com

Seebo was also featured a “

Seebo Selected by the World Economic Forum

Another recent, exciting announcement was Seebo’s selection as a World Economic Forum “Technology Pioneer”, for its contribution to the fields of Advanced Manufacturing and Industrial Artificial Intelligence.

As noted in our press release:

This year’s cohort selection marks the 20th anniversary of the Tech Pioneers community.

Throughout its 20-year run, many Technology Pioneers have continuously contributed to advancement in their industries while some have even gone on to become household names. Past recipients include Airbnb, Google, Kickstarter, Mozilla, Palantir Technologies, Spotify, TransferWise, Twitter and Wikimedia.

News of Seebo’s selection, among a handful of other Israeli tech startups, was featured extensively in the Israeli press, spanning national, business and technology publications — including the Jerusalem Post, The Times of Israel, Calcalist, NoCamels.com, Israel21C and more.

With the ongoing COVID-19 crisis, the manufacturing industry stands at a pivotal crossroads. Download our free guide to learn how manufacturers can move from crisis management, to building a long-term competitive edge:


Watch: How Nestle CPW Increase Capacity & Reduce Losses With Seebo

At the recent Israel Industry 4.0 Week in Tel Aviv, Nestle CPW’s Head of Continuous Improvement Thierry Friant showcased how Seebo is helping his teams increase production capacity and reduce quality and waste losses.

He was joined on-stage with Seebo CEO and Co-Founder Lior Akavia, who offered a stark prediction for the future of Industrial Artificial Intelligence.

“Seebo are the experts in Artificial Intelligence and Machine Learning… they came to us and proved their solution works.”

– Thierry Friant, Head of Continuous Improvement at Nestle CPW

See here for a short interview with Lior and Thierry:

You can see the full 10-minute Q&A panel with Thierry and Lior here:


Seebo's Work With Nestle CPW Featured in Forbes

A recent Forbes article highlighted Seebo’s work with Nestle CPW, and explains why so many manufacturing leaders are looking to Process-Based AI to solve their yield, waste and quality challenges.

Here are some highlights (you can read the full article here):

Theirry Friant, Continuous Improvement & Digital Manager at Nestle CPW:

The main goal of the Seebo technology partnership, Friant said, is to “know what we don’t know, especially about losses around our processes.” CPW now has two factories implementing Seebo’s software. Nestle itself, the world’s largest food and beverage manufacturer with over 300,000 employees and active in 190 markets, is interested in what Seebo is doing,

“…Seebo helped us fine-tune our business case. They are experts in AI and machine learning. We are not. They came and proved that their technology works.”

Lior Akavia, Co-Founder and CEO at Seebo:

For Seebo, Nestle is just the beginning. “Manufacturers who don’t adopt AI on time are likely to lose their relevancy in their markets. Alternately, early adopters are likely to gain strategic advantage over the competition… Nestle is thinking several steps ahead.

Read the full article here


AI for process optimization

Making Smarter Decisions by Using AI for Process Optimization in Manufacturing

The use of AI for process optimization in manufacturing is gaining rapid traction. I want to believe that at this point if a company is not leveraging the capabilities tied with AI, it’s only because they are at least in the process of exploring it.

Here’s the thing, Industrial AI, which is defined as the use of advanced analytics applied to data from the production floor, to enhance manufacturing performance, is suitable for many different manufacturing industries. 

With that in mind, the idea that not all factories are using AI for process optimization at this point is mind-boggling. But I think we are on the right track. One reason for this is that I am seeing more and more “industrial influencers” making the shift. 

I’ll give you an example.

I recently stumbled across an article discussing Tesla’s move to automated factories

The Tesla Model Y, which is scheduled for production in 2020, will be part of the industrial revolution they’re undergoing: “Tesla CEO Elon Musk recently expressed his intention to make the Model Y production system a “manufacturing revolution”, Elon has been going back and forth on how much to simply copy the existing manufacturing process for the Model 3 versus trying something new, untested, and ambitious. For now at least, it sounds like Elon wants to do the latter.”. says Trent Eady

The effect that Tesla has on the automotive community is one of the largest, as they are held accountable for the catalyzation of the whole industry transitioning from gasoline to electric propulsion. To think their shift to smarter factories will have a similar effect is not only optimistic, but I want to think it’s realistic. 

This revolution that Tesla intends to undergo, is a testament to the understanding of the short and long term business benefits that come with adopting Industry 4.0 initiatives.

Not only will Tesla be optimizing processes with the use of AI, but they will be reducing costs at a great scale. 

And they’re not alone.

According to a recent report by Capgemini, by the end of 2022, automotive manufacturers expect that 24% of their plants will be smart factories and 49% of automakers have already invested more than 250 million dollars in smart manufacturing.

Sounds promising.

But still, although many companies understand the need to connect machines and gather data, it’s important to understand that using AI for process optimization is not only about gathering data. It is about aggregating and analyzing it.

This can be challenging as not all companies and manufacturers understand the need to have systems that also leverage the real-time data. But the ones who do, are definitely making faster and better data-driven decisions, which translate to optimizing processes. 

At Seebo, we are leveraging industrial AI technologies to ensure the gathered data is being put to use, primarily, for smarter decision making to optimize production. By employing digital twin analytics, automated root cause analysis, and predictive analytics - process manufacturers have visibility into production processes and their losses. What this means, is that they have the ability to not only understand why they suffer from quality and yield losses, or what these losses are but also when they will happen next. This allows them to prevent them from happening in the future. 

By being able to predict and prevent losses in production quality and yield, companies using Seebo are improving profit margins and production quality at the same time.

Ready to get started with process optimization, driven by data and machine learning?

Request a demo of Seebo Process Optimization today