Industrial IoT in Manufacturing
Tailored for Your Industry


By using Industrial AI, we optimize production processes
to improve quality and yield across industries.

Industrial IoT in Manufacturing
Tailored for Your Industry


By using Industrial AI, we optimize production processes to improve quality and yield across industries.

IoT in Manufacturing

The world’s population is growing exponentially and is expected to reach 10 billion by 2050. This means there are a lot more mouths to feed.  Therefore, there’s an ongoing need to increase production throughput, while meeting stringent regulatory and quality standards.

To do so,  manufacturers must minimize process-driven inefficiencies that damage throughput and create waste. Process-driven losses can include net-weight overweight and underweight, size variability, color inconsistencies, packaging faults and more. Minimizing such losses in real time is crucial for the profitability of food and beverage manufacturers.  

By employing process-based machine learning, Seebo predicts and prevents production waste and quality issues by identifying and anticipating areas of loss and prescribing focused actions that reduce them.

This allows for process and quality engineers to solve issues, rather than investigate them – resulting in the ability to continually improve processes and minimize waste.

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IoT in Manufacturing

The chemical industry coincides with the beginning of the Industrial Revolution. It comprises about 15% of the US manufacturing sector, manufactures more than 70,000 different products, and is responsible for 90% of our everyday products.

When it comes to optimizing production, chemical manufacturers face many challenges. They  must address different process inefficiencies such as the formation of undesired side products, process instabilities, losses due to impurities and purification, and more, on an ongoing basis.

However, given the complexities of chemical manufacturing, understanding the causes of process inefficiencies is extremely time-consuming and difficult, let alone anticipate when they will happen. Moreover, it can often be the specific behavior of the combination of multiple production parameters that cause inefficiencies to happen.

This has led chemical manufacturers to adopt industry 4.0 technologies, and implement IoT in manufacturing processes, to optimize their production. 

Reportedly, chemical companies that have implemented Industrial IoT in manufacturing processes, are seeing big benefits and results –  72% report more than 2x improvement in certain process KPIs, and 37% a 5x improvement.

Seebo leverages methods of supervised machine learning to identify and anticipate process inefficiencies.

With Seebo, manufacturers can identify specific suspects of process inefficiencies by using digital twin visualization and automated root cause analysis. Additionally, by using Industrial Predictive Analytics, process inefficiencies can also be anticipated in advance, enabling process engineers to prevent them, and by doing so increase production yield and quality.

Solution OverviewCase Study
IoT in Manufacturing

The car manufacturing industry is divided into two: car manufacturers (OEMs) and car parts manufacturers (Tier 1 and Tier 2). With today’s vehicles being more complex and the fact that they involve many more parts and electronics than in the past, there is an increase in the number of parts manufactured by suppliers rather than manufacturers.

And together with the continuous strive to perfect the end product, car manufacturers are increasingly facing quality challenges that are time-consuming and labor-intensive to resolve. 

Seebo enables automotive manufacturers to address processes-driven quality losses and failures in production and assembly processes, such as surface quality issues, coating issues, paint thickness problems, dashboard assembly issues, interiors and more.

By leveraging artificial intelligence, and more specifically machine learning, quality issues that are rooted in process inefficiencies within the pressing, body in white, paint, final assembly and powertrain processes are mitigated

Leveraging the power of process-based machine learning, Seebo applies automated root cause analysis technology to discover primary suspects of quality issues, as well as predictive analytics to anticipate and prevent them from occurring in the future.

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IoT in Manufacturing

The oil and gas industry is considered the largest as it generates hundreds of billions of dollars globally every year. 

The world dependency on oil and gas continues to grow as most economies rely on petroleum-based products. Petroleum is the primary material for a multitude of chemical products, including pharmaceuticals, fertilisers, solvents and plastics. As both resources are nonrenewable, the preservation of as much oil and gas as possible is crucial for our future. 

The economic rewards of maintaining high-levels of productivity and efficiency are therefore massive. The environmental regulations increase the complexities of production, which introduces added difficulties in optimizing the production process. 

This has led oil and gas manufacturing companies to turn to industry 4.0 solutions, and implement IoT in manufacturing processes. 

Seebo leverages industrial AI to optimize downstream production process, that refine raw material into useful consumables for the industry. Downstream production manufacturers rely on Seebo to reduce production inefficiencies that damage yield and quality.   

The Seebo solution employs process-based machine learning techniques, coupled with digital twinning, to perform automated root cause analysis and predictive analytics. This enables oil and gas manufacturers to reduce process-driven losses, such as impurity events, kerosene losses, and more, on the production line. 

Solution Overview
IoT in Manufacturing

As science and engineering principles evolve, so does the pharmaceutical industry. The industry is expected to reach global revenues of over $1.4 trillion by 2022. With rapid economic growth in developing countries, the worldwide spending on medicine is projected to continuously increase. In addition, the increasing use of generic drugs worldwide, has led Active pharmaceutical ingredients (API) manufacturing to form the largest share of the pharmaceutical market.

The pharmaceutical manufacturing industry faces many challenges in implementing new technologies. This challenge stems from stringent regulations on drug development and production, making it necessary for companies to adopt methods that will strictly regulate the quality and efficiency of the production processes. 

Today, the pharmaceutical industry is increasingly adopting Industry 4.0 solutions to ensure production quality and improve manufacturing efficiency. Smart manufacturing technologies are increasingly deployed to connect human and machine learning efforts – to optimize throughput without compromising quality standards. 

Seebo offers API production and packaging teams an Industry 4.0 solution based on an AI-powered process digital twin, enabling pharmaceutical manufacturers to predict and prevent production stoppages and waste losses. By utilizing process digital twins, automated root cause analysis and predictive analytics, manufacturing teams can reduce process-driven losses that drive out-of-spec issues (OOS) and packaging faults.

Solution Overview
IoT in Manufacturing

Paper, wood and metal fabricating producers are amongst some of the biggest sectors in the manufacturing industry. Global paper consumption in 2020 is expected to amount to 500 million tons. Industries within wood manufacturing include a variety of products such as lumber, plywood, containers, flooring, homes, and buildings, thus generating a constant demand for the product. The demand for metal has a forecasted worth of $165.5 billion by 2021, and its growth is largely driven by the growth in automotive manufacturing and electrical products.

In particular, wood and paper manufacturers face pressure to  continuously reduce costs, as the demand for products are either stable or declining. This can be explained by the fact that many paper products are being recycled, thus decreasing demand to produce at a larger scale. As a result, the demand for manufacturers in these industries has significantly decreased, but the need to have efficient processes in place has increased. Seebo plays a crucial role in their efforts to improve production processes: having the ability to predict and prevent process-driven losses with AI-powered root cause analysis, and predictive analytics to continually optimize processes that translate to improved ROI.

Solution Overview