On May 10, 2022, Augury announced its acquisition of Seebo, an AI-based process intelligence company. Since its founding in 2011, Augury has developed solutions to help usher in Industry 4.0. Augury’s machine health solutions are used by corporations—including PepsiCo and Colgate-Palmolive—to predict performance issues with manufacturing equipment and help assess machine reliability. Previously, Augury offered both hardware and software solutions to enable manufacturing-scale Internet of Things (IoT). The benefit of the Seebo acquisition will be the addition of its proprietary process-based AI that helps companies make decisions when faced with multiple, conflicting parameters—such as improving yield or reducing waste and energy consumption.
The acquisition shines a light on the future for Industry 4.0, where many companies are looking to expand beyond collecting massive data, through IoT, and actually using it. We’ve heard that companies can use AI to support the real-time decision-making necessary in manufacturing facilities. But have we achieved this scale of AI reliance? Engineering.com sat down with Saar Yoskovitz, CEO and cofounder of Augury, to discuss this.
In the discussion, Yoskovitz notes how Augury hopes to make this AI-driven value a reality with its Seebo acquisition and recent moves to provide insurance-backed diagnostic guarantees. Like other companies, its solutions are being developed to focus on the end user, to ensure that process engineers don’t need to be data scientists to glean the necessary information from their data. So, what is Augury doing differently from the many solutions popping up in this space?
Interpreting Big Data Is Holding Back the Potential of Industry 4.0
Many enterprises are not struggling to collect data. In fact, major technology companies are already recognizing the gap in data interpretation that exists in just about every industry. Of course, that means an investment boom in AI-driven applications focused on interpreting big data in manufacturing. Companies like Google, Microsoft and IBM are all making moves in this space. We recently covered Google’s two new manufacturing-focused solutions that use AI to support Industry 4.0 applications. Google partnered with Litmus Automation to provide the hardware sensors required for its solutions.
Where Augury stands out is its unique full-stack solution, which combines its proprietary hardware and software products with an easy-to-interpret interface. The company develop its own sensors, AI diagnostics and IoT management solutions for large-scale manufacturing across industries. Currently, it offers prebuilt models for most of the standard machines that operate in production lines to help minimize time to value for new deployments. The goal is to provide a solution that can rapidly deliver information on machine operations and performance to support various key performance indicators (KPIs).
Putting Machine Health in the Context of Process Health
In a discussion with engineering.com, Yoskovitz describes some of the motivations behind the Seebo acquisition. He wants to take the company’s full-stack solution to the next level and further improve the time to value by combining Augury’s machine health with Seebo’s process health expertise. With Seebo, Augury wants to provide the operational context for machine health and improve its AI-driven decision-making supports.
“Augury is the leader of machine health, and Seebo is the leader of process health,” says Yoskovitz. He describes the combination of the two companies as a no-brainer for delivering a scalable solution that will make an impact on large-scale manufacturing operations.
“Machine health is the killer application for Industry 4.0,” explains Yoskovitz. He adds that the value of machine health for most manufacturing facilities is obvious: less unexpected downtime can lead to higher throughput and a boost in revenue.
But Yoskovitz and the Augury team noticed one problem that goes underserved for most manufacturing companies and is keeping them from achieving value on the global scale. He explains that machine and mechanical health naturally affect product quality and energy consumption, but notes that machines are not operating in a vacuum. He wants to partner with Seebo to provide the context in which these machines perform and help companies achieve conflicting KPIs. Many companies now have sustainability goals and using AI to better understand a facility’s process health can ensure that machines can perform to meet both yield and sustainability requirements.
Here is an example of how process health can be used to gain additional insight into machine health. Let’s consider a machine that we know will have an upcoming failure in about one month. The manufacturing facility will likely order parts and schedule the required downtime to avoid prolonging unnecessary periods of inactivity. Where process health comes into the equation is in helping an engineer understand that the same machine can operate for six months, instead of one, by simply switching its product. This can be especially useful for manufacturing facilities that deal with fluctuating demand periods, where intelligently pushing downtime on machines can help them meet important quotas. It can also be useful for sustainability metrics to help reduce waste in the production pipeline.
Yoskovitz also describes how Augury’s solution differs from current products available on the market. He says that other machine health solutions are focusing on leveraging existing datasets, a process that can often take seven months to onboard and build models. To reach scale, this process usually needs to be replicated for each individual factory.
Augury instead uses prebuilt optimizations based on common equipment found in production facilities to provide a full-stack solution in less time, which is also easier to scale. The standardized data is based on outputs from Augury’s hardware sensors, which have gathered over the last 10 years. The data includes magnetic flux, temperature, vibration and more.
Aurgury then crowdsources from all its clients to continuously improve its base solutions and ultimately customize per use-cases over time. The mechanical data from Augury’s sensors will now also be fed into Seebo’s process health software to drive value related to company objectives. Whether focusing on sustainability, product quality or overall yield, combining the two company’s solutions can leverage existing data to provide suggestions and insights necessary for real-time decision-making in manufacturing operations.
Combining the Power of People, Machines and Processes
“The world is currently divided to individually focus on machine health, process health or workforce empowerment,” says Yoskovitz. With Augury’s acquisition, Yoskovitz explains that the company hopes to help companies optimize all three. The goal is to empower workers with technology that relies on existing expertise instead of strictly replacing areas of the workforce.
Augury and Seebo’s solutions are already being implemented across diverse manufacturing industries. For example, Augury and Colgate-Palmolive have partnered for several years to deliver predictive maintenance and machine health solutions for manufacturing. Their partnership immediately led to the identification of an event that without intervention would have led to a stretch of long and unpredictable downtime.
“We’ve applied Augury’s Machine Health Solutions to reduce downtime and increase capacity as a foundation of our digital transformation. Our next goal is the integration of those machine-level insights with other variables, from product mix to environmental or process changes, that let us control quality, yield and efficiency in ways we can’t today,” said Warren Pruitt, VP of Global Engineering Services at Colgate-Palmolive. “We see the combination of capabilities that Augury and Seebo are working to bring together as exactly the right approach to helping us maximize production health and achieve our other goals around sustainability, workforce engagement and profitability.”
Industry 4.0, Insured
In the closing of his interview, Yoskovtiz mentions a particularly interesting aspect of the Augury solution that remains unique in this ever-growing field. Following many years of discussion, Augury partnered with Munich RE/HSB to offer insurance-backed guarantees of its AI-driven machine diagnostics. This means that if their diagnostics or machine health insights fail, a company can now be insured by Munich RE/HSB for those repairs or lost production time.
Yoskovitz hopes that formal insurance partnerships can also assist with the scaling of AI-driven solutions for manufacturing, providing companies with additional peace of mind that their solutions will deliver accurate feedback.
Hopefully, over the next few years, we will see the concept of AI insurance adopted by other companies, helping these technologies finally reach the scale necessary for Industry 4.0.