Seebo partners with Microsoft to deliver scalable
process-based machine learning solutions to manufacturers

Microsoft and Seebo, the pioneer in process-based industrial AI, announce co-sell partnership benefiting enterprise clients.

Tel Aviv, April 1st, 2019 - Seebo, a pioneer in process-based industrial AI, with solutions to predict and prevent losses in production yield and quality - today announced that it has earned IP Co-Sell Ready status with Microsoft.

Seebo is now among an elite group of global independent software vendors selected by Microsoft for intensive joint sales, support, and go-to-market initiatives.

Seebo will work directly with Microsoft’s global sales teams to increase the adoption of Seebo’s AI-based solutions among manufacturers around the globe. Specifically, Seebo will work closely with Microsoft’s enterprise sales and delivery teams to engage mid-size and large process manufacturers with the opportunity to optimize quality, yield, and throughput.

Warrick Hill, Senior Managing Director of Microsoft Europe, says “Seebo solutions deploy Microsoft Azure AI services at scale. We’re excited to be a strategic component of the digital transformation that Seebo delivers to our enterprise clients.“

“I’m excited for this partnership as it is a true testament of the strategic value we bring to manufacturers and is built on the success and collaboration that we’re already seeing in winning together.” Said Lior Akavia, Seebo CEO and Co-founder.

Manufacturers are continually challenged to overcome unexpected inefficiencies in their manufacturing processes. Process inefficiencies such as the formation of undesired side products, process instabilities, and blockages, damage production yield, quality, and availability.

Seebo predicts and prevents these inefficiencies, enabling production teams to anticipate when and why they will happenת and to understand how they can be avoided altogether.

The Microsoft IP Co-Sell program was officially unveiled at Microsoft Inspire in June 2017. Since then, it has expanded to a global scale, and includes a $250 million investment in Microsoft seller incentives, to drive engagement, growth and amplified visibility of Microsoft’s technologies.

Seebo is available on Microsoft Appsource


About Seebo

Seebo is a pioneer in process-based Industrial AI, with solutions to predict and prevent process inefficiencies that damage production yield and quality. Customers use Seebo to know:

  1. When these process inefficiencies will happen - leveraging process-based predictive analytics
  2. Why they will happen - with automatic root cause analysis
  3. How to avoid them - using predictive simulation

Manufacturers across industries – including Nestle, Grundfos, Stanley, Procter & Gamble, Hovis, Allnex, and many more – use Seebo to increase production yield while continually improving quality.



Oren Ezra

VP Marketing, Seebo


7 Wastes Plus One? The Hidden 8th Waste in Lean Manufacturing

Lean Manufacturing

The manufacturing industry is as broad as it gets, it consists of 5 different types of processes, spans dozens of verticals, and involves various methods,  philosophies, and approaches.

But all manufacturers have one common challenge they need to overcome, which is the problem of waste.

The adoption of a waste minimization mind state is what’s called lean manufacturing, and is defined as a systematic method for waste minimization.

Lean manufacturing categorizes waste to seven different categories and was originally derived from the Toyota Production Systems (TPS) in 1990. What it means is that anything that doesn’t add value (while not sacrificing productivity), is considered waste.

The 7 wastes in lean manufacturing

While lean manufacturing categorizes 7 different types of waste, it seems there is one more type of waste, “the hidden waste”, which I will discuss later in this post.

But first, let’s understand what the 7 wastes are.

The 7 waste in lean manufacturing:

  1. Transporting - moving materials from one position to another. The transport adds zero value, and there’s definitely room to minimize the costs.
  2. Waiting - one of the more serious wastes in manufacturing, simply put, waiting for goods to move or be processed.
  3. Inappropriate Processing - often, organizations use high precision equipment, which can easily be replaced by much simpler tools. And by investing in smaller, more efficient tools, this waste can be eliminated.
  4. Defects - defects eventually affect quality, which leads to loss of money, either because a product is sold for less, or not sold at all. It’s not enough to detect these afterward as this will also result in extra time and money, but rather prevent it from happening in the first place.
  5. Overproduction - this relates to when there are more produced products than demand for them. Overproduction can also lead to other wastes, such as waiting, inventory, resources and more.
  6. Unnecessary inventory - unsold products lead to extra inventory that organizations are “stuck” with, which need to be stored and take up space and transportation.
  7. Over Processing - when inappropriate techniques are used, or the wrong equipment, processes that are not required are performed, which cost time and money.

The 8th ”hidden waste” in lean manufacturing

Though the list of opportunities and potential to minimize waste in manufacturing seems comprehensive  - there is one more type of waste we see many process manufacturers dealing with, with huge potential to minimize:

  1. Process Inefficiencies - process inefficiencies are different “disturbances” in the production line that can affect quality and yield.

For example, in the chemical manufacturing industry, such process inefficiencies can be:

  • Formation of undesired side products that affect the product purity- when 2 or more reactions occur simultaneously
  • Incomplete reactions - which damage yield and quality of the finished product
  • Losses during separation - of the desired product from the reaction mixture
  • Process instability - due to blocked assets, leakages, and other asset faults
  • Losses during purification - due to the transfer of material from reaction vessels
  • And more.

The bad news is that these process inefficiencies come on account of meeting production goals, such as increasing product purity, preventing asset failures, increasing throughput, and much much more, but most importantly - reducing waste.

But the good news is, there is a way to minimize them. With the rise of Industrial Artificial Intelligence (AI), more and more manufacturers are leveraging process-based machine learning solutions to predict and prevent process inefficiencies.

Predicting and preventing process inefficiencies with Industrial AI

When it comes to AI, it’s important to understand the difference between traditional AI vs. process-based AI.

While traditional AI looks at raw data from production lines (OT data) and applies machine learning to it (causing many false-positives), process-based AI contextualizes the data by adding business data from IT systems into datasets -- together with the specific production process flow context -- and builds a process-based data model.

It then applies process-based machine learning algorithms, which are able to clear the noise and pinpoint actionable insights.

What this means, is that by implementing process-based machine learning - we can now understand 3 important insights:

  1. Why process inefficiencies happen
  2. When they will happen, and
  3. How to avoid them from happening again

The end result? Improved yield and quality, which leads to reduced waste.

There’s no question regarding waste being a strategic issue for manufacturers and the importance of understanding how to combat waste in the most cost-effective way.

Manufacturers, and process manufacturers in particular need to address the 8th waste of Process Inefficiencies as a core cause of waste. This requires the implementation of machine learning with its predictive analytics capabilities.

Industrial IoT Trends in 2019

8 Industrial IoT Trends in 2019

Capitalizing on technological advancements in industrial manufacturing, companies are taking bolder steps to improve growth and operational efficiency in 2019. Here’s a look at the top trends and predictions for industrial IoT in the coming year.

It’s no surprise that worldwide technology spending on the Internet of Things is forecasted to reach $1.2 trillion by 2022 (IDC).

Manufacturers are looking to solve the complex problem of consolidating all production systems – OT and IT data, BI, quality management, and production processes –  into a single data model. And they know that those who manage it successfully can beat their competitors in the process. For this reason, adoption of IoT devices and services is set to hit 20% in 2019 (IDC).

But the question isn’t “what”, it’s “how”: there are numerous Industrial IoT solutions to target a myriad of business problems, and manufacturers can only budget for a few POCs or solutions at a time.

With so many possibilities for improvement, where will manufacturers, investors, and governments choose to put their money?

Based on research, we’ve identified the trends that will continue to gain traction in 2019. These are the actions manufacturers will take to better manage operations, deliver improved products and services, and grow business smarter.

8 2019 Trends in Industry 4.0:

Going beyond POCs

If 2018 was the year of IIoT proof of concepts, 2019 will be the year manufacturers move from early proof of concepts to deploying pilots for Industry 4.0 solutions, such as predictive maintenance, digital twin, and predictive quality.

Industry 4.0 solutions are so new that we still lack data for much of the ROI from Industry 4.0 initiatives.

Here is an example:

Predictive maintenance is the headline subject at practically all of the recent and upcoming international Industry 4.0 conferences. But predictive analytics in manufacturing still requires months of collecting enough data to act upon before providing full ROI.

Furthermore, while some manufacturers have reached the predictive stage, very few early adopters have reached the prescriptive analytics phase.

But that is about to change.

With major players in food & beverage, chemicals, and other giant industries deploying Factory 4.0 solutions, there will be more information about how these solutions are effectively implemented per industry by this time next year.

The rise of industrial AI in manufacturing

AI and Industrial IoT are merging to digitize production processes to increase productivity and reduce downtime. Machine learning algorithms for manufacturing are being formulated and tailored to specific production line challenges – such as reducing production waste, improving process stability, minimizing unplanned downtime, and eliminating process disturbances

What does this mean on a practical level?

Search “AI in manufacturing”, and you’ll read articles about more accurate insights into the manufacturing process, and reaching a higher OEE than was possible through previous methods. These aren’t just superlatives – the vast scope of IoT use cases makes manufacturing the most logical field for AI applications.

Industrial IoT trends 2019

AI and machine learning are a wide umbrella term for a multitude of different algorithms and applications; many of the trends below are part of that shift towards integrating AI solutions into existing manufacturing processes.

Contextualizing OT data – and tools for contextual analysis

OT and IT have been converging for some time, and ‘collaboration’ used to be the goal – but many manufacturers are taking their operational and IT data a step further to improve the relevance and accuracy of data-driven insights.

What is that step?


The only way for manufacturers to measure the right data, and reach accurate conclusions, is by combining all the relevant operational data from the plant or line environment with business context data from the IT systems.

Are you contextualizing your OT data with the following dimensions?

  • Production process flows
  • Production recipes
  • Batch and product details
  • Quality test results

Here’s an example of data contextualization in predictive maintenance:

A food and beverage manufacturer deploys software with machine learning algorithms applied to OT data from one production line, searching for patterns that predict asset breakdown.

But this software doesn’t take into consideration alerts from quality control tests, or which batch and product is being manufactured.

So an oven could overheat for specific recipes – but without the context of the recipe the machine learning algorithm could never yield accurate, actionable insights to the production team.

In the coming year, manufacturers will be budgeting for systems that help them glean manufacturing excellence insights  through the lens of process and business data that influence the production environment.

Using digital twins

24% of companies implementing IoT solutions already use Digital Twins to increase safety and efficiency – and according to Gartner, that number is about to jump.

Digital twins are virtual copies of a physical entity that link to the entity, often in real-time. In the manufacturing world, digital twins support many industry 4.0 solutions, from automated root cause analysis, to predictive quality, predictive maintenance, inventory intelligence, and supply chain optimization.

Digital twins are most commonly used in the areas of design, modeling and simulation, so it’s not surprising that they were a buzzword in 2018, the year of Industrial IoT pilots.

In 2019, we will not only see greater adoption of the digital twin in general, but also an expansion in their popular use: more digital twins used to optimize production processes rather than the individual assets in day-to-day operations and processes.

These ‘full’ digital twins’ will incorporate process data that will help manufacturers reach more accurate insights, whether by deep-diving into individual machines or viewing the high-level process architecture to identify and address manufacturing inefficiencies.

Early adopters are already using digital twin software to improve the accuracy of predictive AI applications, and more manufacturers will adopt this approach in the coming year.

Edge computing

As devices become more powerful in 2019, more manufacturers will take advantage of local data processing and AI capabilities, also known as edge computing.

By 2020, IoT sensors and devices will generate over 5.07.5 zettabytes of data.

Manufacturers are for the most part already collecting data, but managing it via cloud computing puts a financial strain on manufacturers – not to mention the security risks of storing all your raw data in the cloud.

Edge computing helps businesses by analyzing and storing data close to its source decrease time and expenses related to data analytics, as well as improving data security.

Imagine this:

Multiple machines in one production line monitoring vibration of machine components.That’s hundreds of data points per second.

Uploading all that data to the cloud for cleansing, processing, aggregating and analysis is redundant.

In edge computing, each machine in the line is connected to an edge  computer to collect, store, and preprocess OT data.

The edge computer not only processes vibration data, for example, but also performs feature extraction to select a predefined amount and type of vibration data – highs and lows within a given time frame, for example – to go to the cloud.

Even this basic level of data analysis processing performed at the source streamlines the process of aggregating production line data to an incredible degree. There is much less historical and real-time data for machine learning algorithms to sort through, speeding up findings that could affect everything from yield to uptime to product quality.

Secure and cost-effective

Edge computing also cuts down on the cost of data storage in a cloud: a dozen data points for every ten thousand measured. Limiting the raw production data sent to the cloud also mitigates data security risks.

Mobile Industry 4.0– ERP and quality management systems.

The arrival of 5G next-gen mobile networks heralds greater adoption of IIoT applications.

Due to 5G and other advancements in mobile tech, 2019 will see a rise in real-time IIoT applications and the use of IIOT for teams once excluded from direct interaction with the technologies involved.

For example, while many ERP systems are now integrated with Industry 4.0 systems and even include applications, such as MES, most of these systems do not support all the user roles and business functions related to the manufacturing floor.

This is unfortunate, since many of the solutions forecasted to grow in adoption, such as operator productivity and inventory intelligence, must be accessible to teams on the factory floor.

Expect to see a rise in companies offering apps specializing in different user personas, such as quality management software apps, or applications with different dashboards per business role.

Supply chain optimization

What was once purely a logistical function, now has their own business models and optimization processes.

Coupled with this, Online-consumer trends have dramatically changed customer expectations for on-demand services, transparency, speed, and efficiency. Supply Chain 4.0 is a way to  meet the new demands and changing supply chain landscape through digitization.

Supply chain optimization can and does utilize many of the other  top Industry 4.0 trends for 2019: Digital twins, mobile apps, and AI-powered predictive tools. Artificial Intelligence will be imbedded in common supply chain processes.

Accurate – Digitally map the supply chain and real-time production and product data create transparency and increase the accuracy of inventory data.

Faster – Advanced forecasting tools, coupled with real-time data on spare parts supply and demand, will create process where finished products reach their destination faster.

Flexible – real-time data leaves room for flexibility in the distribution process

Securing IIoT endpoints

Enterprises are already invested in securing their OT infrastructure, to the same degree as they do their IT systems. However, the clear and present threat to organization’s cybersecurity, coupled with the boom in Industrial IoT adoption, will see OOT security and ICS security go mainstream in manufacturing plants, whatever the size or industry.

It won’t be easy; securing IoT devices or machines is becoming increasingly difficult, so much so that Microsoft recently released a list of best practices for IoT devices.

And with the proliferation of edge computing comes a plethora of new industrial IoT endpoints, i.e. devices with computational capabilities and network connectivity ( So even the bonus of securing data by sending less to the cloud comes with the added risk of increase endpoints.

Despite this, the gain to businesses in developing smart production lines is clear enough that securing data is becoming less of a deterrent to manufacturers who want to increase yield and improve operational efficiency through AI.

Forecast: Top 8 Trends in Industrial IoT in 2019 

1. Going beyond POCs

2. Rise of industrial AI in manufacturing 

3. Contextualizing OT data – and tools for contextual analysis

4. Using digital twins 

5. Edge computing

6. Mobile Industry 4.0– ERP and quality management systems.

7. Supply chain optimization

8. Securing IIoT endpoints

Industrial IoT Predictions for 2019

We’re seeing more and more deployment of Industrial IoT solutions that are revolutionizing the manufacturing landscape digitally – transforming customer relationships, differentiating offerings, and driving massive operational improvements to meet the growing demands on production.

Based on these trends, industrial IoT early adopters are positioned to be five times as likely to generate revenue from Industry 4.0 initiatives compared to late adopters. But take note – companies must first decide which business value drivers they want to contribute to. Only then, can they align their digital strategy with the business goals they are targeting in order to manage, secure, and operate IoT platforms and processes effectively.

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