The manufacturing industry is experiencing a profound transformation in which using data to its fullest potential is central. Businesses can gather data from different sources using connected devices and sensors. Processing these large datasets with advanced data analytics tools, artificial intelligence and machine learning models, manufacturers can make informed decisions critical for optimizing production processes, creating additional revenue streams, and pioneering new business models.
The next frontier is a shift from automation to autonomy, moving from a situation where physical systems seamlessly integrate with digital technologies to a realm where hyper-connected autonomous technologies are set to revolutionize the entire value chain. Predicting how and how fast this transition will occur is harder. However, several trends emerge as the pillars shaping the future of industrial automation.
Artificial intelligence moves to the Edge
Today, manufacturing builds on a thorough collaboration between AI and IoT. IoT edge sensors manage connectivity and data gathering, while cloud-based AI takes the next step with data analysis, learning, and optimization. This convergence elevates enterprise intelligence and enables new functionalities and services. As IIoT networks become increasingly sophisticated, they introduce a set of challenges encompassing latency, network availability, and data security concerns. The integration of AI algorithms directly into IoT edge devices addresses these challenges, providing a new level of machine intelligence and overcoming the constraints of cloud AI.
Edge AI requires powerful CPUs and GPUs to extend devices’ capabilities to execute AI, Deep Learning, and Machine Learning models locally. A new generation of AI chipset and hardware accelerators delivers augmented processing capabilities, unlocking the full potential of Artificial Intelligence at the Edge. Properly integrated into embedded systems, they help in processing a vast amount of data generated by IoT devices directly at the edge, decreasing the workloads on the cloud.
In the context of modern manufacturing, implementing AI algorithms directly on edge devices leads to efficient equipment health monitoring and service prediction, increased production efficiency, and heightened operational flexibility.
The emergence of Digital Twins
Digital twin technology is gaining momentum as it holds the promise of improving efficiency and ushering in a new era of predictive maintenance. A digital twin is a dynamic, virtual replica of a physical object or system. It uses sensors and IoT devices, AI, and data analytics to continuously gather data from its physical counterpart and react correspondingly in real-time. Companies are beginning to adopt digital twins to achieve production efficiencies in the virtual realm before implementing changes in the physical space, reducing costly trial-and-error processes.
One of the most important contributions of digital twins to modern manufacturing encompasses predictive maintenance. Imagine a scenario where a digital replica of a critical machine, constantly updated with real-time data, enables not only the prediction of potential issues but also prescriptive insights for preemptive maintenance. Offering a real-time mirror into the health and performance of physical assets, digital twins can promptly detect anomalies or signs of wear and alert operators. This proactivity enables timely, targeted maintenance interventions, minimizing downtime and extending the lifespan of machinery.
In a networked manufacturing environment, multiple digital twins can interact with each other to picture the entire production ecosystem, optimizing operations in real-time to adjust to changing parameters like shifts in demands or resource availability. This coordination guarantees efficiency and agility in responding to dynamic market conditions, leading to data-driven decision making.
Getting started with generative AI in manufacturing
Generative AI in manufacturing is rapidly emerging as a powerful tool for refining operational strategies both on the factory floor and the organizational level. Generative AI is a subset of Artificial Intelligence focused on using neural networks and machine learning algorithms to learn from human-created datasets and create new content across a multitude of formats – images, videos, audio, or text. At the core of generative AI’s impact in manufacturing is its ability to analyze vast amount of data to identify patterns and structures, proposing novel solutions that overcome traditional design constraints. Consider a manufacturing plant that employs generative AI to optimize the layout of its production floor. The AI algorithm analyzes data on workflow patterns, machinery efficiency, and employee movements to propose layout configurations that enhance both efficiency and worker safety.
The benefits of generative AI in manufacturing are transformative. Models can be trained on data from the machines – like temperature, vibration, sound, etc. – to predict potential failures or inefficiencies, allowing manufacturers to perform predictive maintenance that reduces downtime. Analyzing historical data, generative AI can forecast demand to enable more accurate production schedules and optimal inventory levels, leading to reduced costs linked to overproduction or stockouts. Quality control can also benefit from models’ ability to quickly identify defects and anomalies that might be missed by manual inspection. As a whole, generative AI contributes to shaping the very infrastructure of the manufacturing environment.
Prioritizing industrial cybersecurity
As automation systems become hyper-connected and data-driven, facilities’ exposure to cybersecurity threats increases significantly, with potential far-reaching consequences. In a connected manufacturing environment, a ransomware attack not only could encrypt critical systems but could manipulate sensor data, leading to inaccurate readings and potentially hazardous operational decisions. The effects extend beyond financial losses to encompass operational safety and erode public trust. The challenge for companies is not just about preventing breaches, but also ensuring the reliability and integrity of the data flowing through interconnected systems.
Implementing secure network architectures, encrypting data transmissions, and adopting stringent access control measures are all means of mitigating risks. However, the premise is to design solutions with security as an integral part of the industrial automation system. That means integrating security features into both the hardware and software. To accomplish this, OEM companies can rely on experienced partners which prioritize the safety and security of their customer’s products, helping them to configure automation systems to comply with industry regulation standards (e.g., EU Cybersecurity Resilience Act – CRA) through dedicated security software and hardware solutions. Adopting such a holistic approach to cybersecurity in manufacturing means developing future-proof systems capable of sustained and advanced protection against evolving cyber threats in the long term.
Sustainability: beyond rhetoric to tangible value
As companies globally focus on meeting stricter legislation and carbon targets, becoming more sustainable while remaining profitable is a major concern. One of the primary challenges lies in dispelling the misconception that sustainability comes at the cost of profits. Initiatives promoting sustainability in manufacturing can generate both top-line and bottom-line growth in the short term while positioning businesses for success in the long term. In short, a more sustainable use of resources goes hand in hand with lower costs and boosted margins.
Digitalization and automation are key to balancing ESG goals and economic profit: gathering and analyzing data across industrial operations helps to minimize resource waste and improve outputs, reducing both environmental impact and operational costs. Despite the substantial initial setup cost, AI-empowered systems enable manufacturers to collect and analyze large amounts of data, triggering a near real-time loop of actions that extend equipment’s lifespan, streamline operations, optimize resource consumption, and reduce waste and inefficiencies. In this way, not only is the company’s environmental footprint reduced, but performance across the whole value chain is improved and operational costs are also lowered to increase profit.
Accelerating manufacturing pace with the right equipment
The future of automation runs through the seamless incorporation of technologies with transformative reach, creating opportunities and challenges for their users. Keeping up with tech advancements and exploiting their full potential requires not only the intellectual knowledge but also the infrastructure to support their implementation.
SECO specializes in providing end-to-end, edge-to-cloud solutions to OEMs seeking to unleash the full potential of their business through the deployment of breakthrough technology solutions. Whether it’s Edge Computing, IoT, or AI, our comprehensive, integrated, and modular offerings cater to the specific needs of manufacturing companies, empowering them to deliver their next-generation product lines. Contact our team of experts today and let them guide you in finding the right solutions for your business.
