The Future of Cloud-Driven Manufacturing: Built to Scale

Categories: CloudDigital TransformationIoTManufacturing and IndustrialTechnology

The last two years have upended the global marketplace and the manufacturing sector is no exception. As the global pandemic rolled across the world, plants shut down, supply chains were disrupted, and widespread socio-economic instability ensued. In an effort to minimize future disruptions, mend operational inefficiencies revealed by the pandemic, and get ahead of changing consumer expectations, the manufacturing sector is undergoing a digital transformation. Welcome to the era of Industry 4.0.

Digital transformation is driving big gains for businesses by improving operational efficiency, reducing costs, improving product quality, and enabling quicker responses to evolving market requirements and customer demands. There are benefits as far as eco-positivity via reduced energy, lower material consumption, proactive monitoring, etc. as well.

These innovations mean we’re now seeing robotic processing automation, artificial intelligence, machine learning, augmented reality (AR) and virtual reality (VR) all working together in the Industrial Internet of Things (IIoT) to provide manufacturers resilient, agile solutions for persistent challenges. 

A recent Gartner survey found that 36% of manufacturing enterprises realize above-average business value from IT spending in digitalization at a reasonable cost when compared with peers. Is your manufacturing operation on trend? Let’s take a look at what cloud-driven smart manufacturing and Industry 4.0 look like in practice.

Digital Twins

One of the challenges of innovation in manufacturing is the sheer size of equipment, space, and logistics. Shutting down a production line to repair, replace or add a part or piece of equipment is expensive, potentially hazardous, and time-consuming.  In addition, despite the best measurements, fixed structures, wires, overhead beams or doors can be missed in the design phase, requiring costly changes and repeated shutdowns until the repair or part is installed, tested, and completed. 

Artificial intelligence uses augmented reality (AR) and virtual reality (VR) to create a 3-D model of the equipment, component or space, and then developers and engineers can work with this digital twin technology to design, tinker, adjust and perfect the equipment in virtual simulation before it is built and installed. 

According to a study by Gartner, 13% of organizations that have implemented Industry 4.0 and IoT are employing digital twin technology, and a further 62% are in the process of implementation. 

Some of the benefits of digital twin technology include reduced risks, accelerated production time, remote monitoring, enhanced collaboration, and improved fiscal decision-making thanks to advanced analytics and rapid testing in the cloud. 

GlobalLogic is a leader in building digital twin technology. Learn more about how it works in “If You Build Products, You Should Be Using Digital Twins.”

Predictive Maintenance

Equipment breakdowns and malfunctions are costly, time-consuming, and potentially dangerous to employees. One advantage of Industry 4.0 and digital twin technology is the ability to perform predictive maintenance in VR/AR. Unlike preventative maintenance, which is performed on a schedule whether the servicing is actually required at that point in time, predictive maintenance relies on data to predict when the maintenance should be performed. Successful predictive maintenance capabilities are dependent on the use of artificial intelligence, sensors, and cloud solutions. 

According to the US Department of Energy, an investment in a PdM strategy can reduce maintenance costs by up to 30%, reduce the number of unexpected breakdowns by ¾ and reduce the number of downtime hours by almost half. If properly implemented, it is also 25% cheaper compared to preventive maintenance.

The IoT sensors generate big data in real-time, and artificial intelligence and machine learning can analyze, flag anomalies, and initiate repair protocols before a problem halts production. Digital twin technology can scan a production operation from all angles continuously, and make recommendations for predictive maintenance that can be scheduled rather than completed on an emergency basis. This saves time, decreases production downtime, increases efficiency and safety, and mitigates risk.

Robotics & Autonomous Systems

Whether it is a full-scale automated robotic processing system, or a single station collaborative robot (cobots), robotics and autonomous systems have been changing the manufacturing landscape. 

Since the pandemic, however, robotics and autonomous systems have been driving digital transformation. Robots can work 24/7/365, they don’t take vacation, sick days or personal time off. They can provide rapid ROI and improve productivity while freeing human workers to do higher-value tasks. 

In recent years, the incorporation of AI, VR/AR, and machine learning has been employed to work side-by-side with human workers, and cobots with end-of-arm-tools equipped with machine learning can be moved by a worker in “teach” mode, and then it operates autonomously, becoming more efficient as it “learns” the task. 

The next innovation in robotics is individual microsystems, designed to work as autonomously as possible, while still collaborating with other microsystems. That way, if other microsystems crash, the others can continue to operate. Each microsystem is “choreographed” to work with others in collaboration while doing its part. It can be easily scaled and coordinated. Think of it as a colony of bees, each worker autonomous, but contributing to the whole synergy. Check out “Collaborating Autonomous Systems” to learn more about GlobalLogic’s work with microsystems. 

Connected Devices and the IIoT

Just as the IoT connects your smartphone to your thermostat, television, tablet, or speakers, the Industrial Internet of Things (IIoT) connects smart applications in manufacturing and industry. For example, the IIoT connects sensors on a cobot with the engineer’s tablet in another building, or the alarm system that activates if a sensor detects a chemical spill or heat increase. IIoT relies on cloud technology so that the data can be accessed from anywhere.

The data from these many sensors, controllers, and attached servers is often distributed across many remote locations. The data is uploaded continuously to the cloud, allowing for real-time updates at any time across multiple locations. McKinsey predicts that IIoT will be a $500 billion market by 2025.

One advantage of the IIoT is it provides simultaneous data from multiple locations and sources, whether within the same manufacturing facility or spread across multiple facilities or geographic locations. The cloud allows for centralized management of all the IIoT resources, but that management can happen from anywhere in the organization or the world. It provides business continuity and resilience if one location experiences an emergency or natural disaster, as operations can continue at the other locations, and real-time updates allow for quick response. 

Traditional IT storage requires hardware, system ware, servers and massive databases, and if the location goes down, the data can be lost. With IIoT cloud technology, the data is protected and accessible, while being encrypted and safeguarded by the cloud cybersecurity protocols.

IIoT is a form of edge computing, where the goal is to bring the resources from traditional data centers and bring them as close as possible where they are needed while maintaining safety, data protection, and guarding against cyber-attacks. 

GlobalLogic’s “Immunizing Edge Computing” takes a more in-depth look at how to protect data when working on the edge.

Conclusion

These are just a few examples of how cloud technology is transforming the manufacturing space. Intelligent automation, including VR/AR, artificial intelligence, machine learning, automated robotic processing, and autonomous microsystems are leading smart manufacturing innovations.

As more automation moves to edge computing – whether it’s a sensor, a pump, a car or a gateway – this trend will continue as the costs of computing power and related resources continue to decline. Determining precisely how to use the cloud and what can happen at the edge is an integral part of your smart manufacturing strategy and working with an expert in cloud technology is an important part of your intelligent automation business plan.

As innovation continues to evolve, the “edges” will get smarter, allowing for more powerful collaboration. With machine learning, the more the edge nodes “learn”, analyzing data, sensing the environment, and processing data, the more information will be available to share, whether peer-to-peer or through a network. IIoT will allow for smart edge collaboration in one form or another. 

Single station cobots, warehouse robots, and self-driving autonomous cars will continue to be innovation-driven, representing the span of collaborating autonomous systems, with no limits on the horizon. Intelligent automation, the IIoT, and other applications will continue to evolve robust, scalable, powerful systems with nuanced behavior. 

See how we can help you harness the power of cloud to engineer products to scale your manufacturing here.

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Author

Raja Renganathan

Senior Vice President, Cloud Engineering

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