Augury
Augury's solution in action.
Following the European Parliament’s approval of the Net-Zero Industry Act (NZIA) earlier this year, Manufacturing Quality got the chance to talk to Augury about how artificial intelligence (AI) technologies may hold the key to helping the manufacturing industry fight climate change.
Saar Yoskovitz, Co-Founder and CEO of Augury, spoke to us regarding a range of topics including how metrology helps better AI’s capabilities, the role AI can play in helping companies become more sustainable, and the importance of automation in both measurement applications and manufacturing as a whole.
Additionally, Yoskovitz provides us with more information regarding Augury’s Process Health platform which utilises AI to optimise the potential of production lines by improving quality, yield, and throughput levels whilst reducing waste and energy losses.
MQ: Could you briefly outline how your Process Health platform works?
SY: Augury’s Process Health platform leverages hybrid intelligence, combining AI with process experts to optimise production processes. It identifies inefficiencies, provides real-time insights, and prescribes optimal settings to achieve production goals. It provides operators with real-time instructions to enable them to directly manage production while production is occurring, meaning that changes in one aspect of the production line can be accounted for and responded to appropriately to maintain optimal outcomes.
By stabilising and refining processes, it helps reduce waste, energy consumption, and emissions while improving yield, quality, and throughput. Users can diagnose issues, implement solutions, and continuously improve inefficiencies, ensuring sustainable and efficient production.
MQ: If Augury utilises metrology methods when calculating quality assurance levels with its Process Health platform, are measurements informed through automation processes or by process experts?
SY: Measurements in Augury's Process Health platform are primarily informed through automation processes. This integration ensures consistent and precise quality assurance levels across manufacturing processes. While automation drives real-time data collection and analysis, process experts – operators and process engineers – collaborate with AI systems to interpret results and optimise production. In the case of metrology, measured parameters such as temperature or humidity can often be automated so that the AI engine can include them in continuous simulations. Where direct automation isn’t possible, other methods for experts to engage with the platform exist. In the Process Health space, Augury leverages existing metrology techniques and methods applied by other sensors and technologies to integrate via automation where possible, to make those techniques provide near real-time acquisition.
This synergy between automation and human expertise ensures that Augury delivers reliable insights for maintaining high standards of quality and efficiency in manufacturing operations.
Ori Habushi
Augury's Saar Yoskovitz.
MQ: With artificial intelligence in its infancy, how can you measure the significance AI has on helping businesses achieve greater levels of energy efficiency?
SY: It’s actually a misconception that AI is in an infancy period, although public perception of the use of AI certainly is. Augury has been using AI techniques and solutions for several years and has developed a robust way of understanding the value and the ability of AI to enhance and accelerate the work people perform.
Businesses typically measure and analyse key performance indicators (KPIs) like energy usage, costs, waste, safety incidents, quality and defect rates, and emission generation to quantify improvements before and after implementing AI. These same KPIs can be trended using Augury’s AI engine to ensure that these KPIs aren’t just one-time measures but can be used as part of an organisation’s TPM program to see improvement over time, whether that is per shift, per day, per week, per month, or year.
Case studies and longitudinal data further demonstrate AI’s impact, showcasing tangible benefits like lower utility bills and reduced carbon footprints, underscoring its role in advancing energy efficiency.
MQ: In your article introducing the Process Health platform, the key features of “uncover inefficiencies,” “stabilise and optimise your processes”, and “be autonomous” are highlighted. Which benefit do you think AI helps enhance the most? And which benefit holds the most potential for future development?
SY: AI significantly enhances the "stabilise and optimise your processes" feature through its ability to learn continuously from millions of data points and make dynamic recommendations, even as conditions or goals change. This immediate feedback loop allows for quick adjustments, minimising waste and energy use while boosting throughput and yield.
Looking ahead, the "be autonomous" benefit holds the most potential for future development. AI-driven autonomy empowers frontline teams with user-friendly, actionable insights, enabling them to make informed decisions independently. This fosters a culture of continuous improvement and adaptability, essential for thriving in dynamic and ever-changing production environments.
MQ: You reference the Net-Zero Industry Act (NZIA) as a key document that proves that AI is a “key decarbonisation technology”. Also listed in the STEP (Strategic Technologies for Europe Platform) Regulations, like AI, under quantum technologies is metrology. Could you explain if there could be any overlap between metrology technologies and AI?
SY: For Process Health, the metrology technologies can serve as key, highly accurate and trustable inputs into the process health AI model. In other words, the more accurate and precise the metrology methods deployed, the more reliable and accurate the AI model which leverages them can be. In the case of Process Navigator, where we provide turn-by-turn instructions to operators on the shop floor, the ability for the operator to know that the input data used (obtained via metrology techniques) and the AI-model output are both trustable is a fundamental step towards moving towards operator autonomy and efficiency.
MQ: Do you believe that metrology could play a part in improving AI technologies?
SY: It already does. In manufacturing, metrology, typically delivered via sensors and data acquisition platforms, ensures that the data fed into AI systems is reliable and consistent, which reduces uncertainties and enhances the AI's predictive capabilities. By guaranteeing the precision and accuracy of measurements, metrology provides a solid foundation for training and refining AI models. High-quality, traceable data enabled by sound metrology techniques enables AI algorithms to make more accurate decisions, leading to better process optimization, improved product quality, and higher energy efficiency. This synergy between metrology and AI drives innovation and continuous improvement across various industries.
MQ: In your opinion, what could the future hold for AI technologies in the manufacturing industry’s mission to become more sustainable?
SY: AI is already optimising production processes to reduce waste, energy consumption, and emissions.
By providing real-time insights and predictive analytics, AI helps manufacturers fine-tune operations, ensuring efficient use of resources in real-time, rather than retroactively.
Additionally, AI can aid in the development of more sustainable materials and processes, leading to greener manufacturing practices. As AI continues to advance, its ability to drive innovations in recycling, circular economy models, and overall resource efficiency will be pivotal in achieving long-term sustainability goals in the manufacturing sector.