Unlocking the Power of Predictive Maintenance in Smart Factories

Both LG Electronics and Samsung Electronics are making strides in the smart factory industry. Samsung Electronics is set to change its semiconductor manufacturing lines into smart factories, actively. Samsung Electronics considers the capability in ‘Digital Twin’, notably. Digital twin is one of the core technologies of smart factories, which is crucial for Samsung Electronics’ success in catching up with - the world’s leading semiconductor foundry - TSMC.

Recently Samsung Electronics chose Nvidia as its digital twin partner. On March 19^th, Yoon Suk-jin, vice president of Samsung Electronics’ Innovation Center, revealed the plans of the company in utilizing the Omniverse during a presentation at the “Nvidia GPU Technology Conference 2024 (GTC 2024)” that was held in San Jose, California. The presentation was titled ““OmniVerse-based Fab Digital Twin Platform for the Semiconductor Industry.” Vice President Yoon stated, “The goal is to realize a fully automated fab by 2030.”

Today - in the age of Industry 4.0, smart factories are redefining the manufacturing segment. Advanced manufacturing facilities implement cutting edge technologies which comprise IoT, AI and big data analytics to optimize operations, improve productivity and increase efficiency. Among these, predictive maintenance stands out as one of the important strategy for reducing downtime, costs and optimizing asset utilization. In this article let us look at the key strategies that are implemented for unlocking the power of predictive maintenance in smart factories, which comprise data collection, advanced analytics and integration with enterprise systems.

“Leveraging IoT and other advanced technologies has also enabled manufacturers to implement predictive maintenance techniques, saving resources, especially in maintenance costs, and ensuring zero downtime,” Ankit Jain, Head - IT, WIKA Group.

Data collection & integration

Data which includes vast streams of real-time information that is gathered from sensors, IoT devices and equipment embedded within smart factories lies at the heart of predictive maintenance. This data comprise a wide range of parameters that include energy consumption, pressure, temperature, vibration and more, offering insights about health & performance of production assets. Effective predictive maintenance starts with robust data gathering as well as integration mechanisms that aggregate & contextualize this data into a centralized repository. Smart factories can leverage the power of real-time data for monitoring equipment condition, expect potential failures and detect errors. Moreover, data standardization & normalization promote cross-system interoperability and this helps manufacturers to obtain actionable insights from various data sources & bolster in making informed decisions.

“Smart Factories are not only in the domain of big factories and large-scale production sites. SME’S and smaller factories can equally benefit by exploiting all the power and effectiveness of highly integrated solutions,” says Sayeed Ahmed, CEO, Biesse India.

Advanced analytics

Advanced analytics emerges as one of the critical enabler of predictive maintenance owing to the proliferation of data within smart factories. Machine learning algorithms play a major role in uncovering correlations, patterns & anomalies that are hidden within huge datasets. These algorithms can find subtle indicators of equipment degradation by implementing techniques like supervised learning, reinforcement learning and unsupervised learning. Also, predictive maintenance models can learn from past performance trends & expect future maintenance requirements by leveraging real-time sensor readings & analyzing historical data.  Furthermore, anomaly detection algorithms can flag deviations from expected behavior, which will trigger proactive interventions and prevent catastrophic breakdowns. Since the artificial intelligence field continues to advance, smart factories can utilize advances in predictive analytics, deep learning, and neural networks to improve the accuracy as well as predictive capabilities of their maintenance needs.

“Leveraging IoT and other advanced technologies has also enabled manufacturers to implement predictive maintenance techniques, saving resources, especially in maintenance costs, and ensuring zero downtime,” Ankit Jain, Head - IT, WIKA Group.

Integration with enterprise systems

 Smart factories should be integrated with their maintenance systems with broader enterprise systems that include ERP, MES and SCM platforms. This integration not only streamlines data flows throughout the organizational silos but it also aligns maintenance activities with manufacturing schedules and promotes cross-functional collaboration. Furthermore, integrated maintenance systems reduce disruptions in the manufacturing process and in order to improve the overall operational efficiency and these are carried out by automatically generating work orders as well as scheduling maintenance activities and maximizing the use of spare parts inventory. Furthermore, manufacturers can leverage historical insights to drive predictive analytics, refine maintenance strategies over time, and identify performance trends.

“In manufacturing, the industry's data infrastructure and analytics propel smart manufacturing and predictive maintenance through real-time insights from IoT devices, optimizing production efficiency and enhancing supply chain management,” says Sanjay Agrawal, Head Presales & CTO, Hitachi Vantara (India & SAARC).

“Manufacturers can harness operational insights that maximize production, regardless of ever-changing factors, by implementing end-to-end solutions that leverage data from machines and sensors, ubiquitous connectivity, sophisticated predictive analytics, and AI,” he adds.

Predictive maintenance becomes an important part of the smart factory system via seamless integration with enterprise systems by driving synergies throughout the value chain. In conclusion, predictive maintenance depicts a corner stone of the smart factory revolution which facilitates manufacturers to prevent, anticipate & mitigate equipment failures with both unprecedented precision and efficiency.