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In 2023, a total of 41.76 billion IoT or Internet of Things-connected devices are anticipated to be deployed, per ReportLinker. As per Guidehouse Insights’ estimations, The global market for IoT in manufacturing is set to grow to $23.1 billion by 2031, growing at a compound annual growth rate (CAGR) of 15.0%. Industry 4.0 is here, set to boost competitiveness across companies.

 

A McKinsey research reveals IoT is poised to deliver $1.3 trillion in economic value to the manufacturing sector. Among these also is standardized production settings, but requiring scale for ensuring thorough capture.

 

McKinsey research also states IoT’s potential global economic value to unlock by 2030 could be $5.5 trillion to $12.6 trillion. But barriers exist, in the form of machinery upgrade costs, investment optimality, and cybersecurity risks.

 

The ultimate Industrial Internet of Things or IIoT applicability lies in the manufacturing sector. Production lines and industrial machines’ productivity could massively boost with internet connectivity and sensors, monitoring production. These could include monitoring temperature, humidity, noise, or vibrations.

 

Let’s examine the various IIoT trends in manufacturing:

 

Transforming IoT Business Model

 

Industrial IoT Solutions are appealing as they enhance factory performance via augmented output, whilst also bettering key business metrics. IoT is an automation catalyst, allowing manufacturers to transform business models with innovation. The approaches on which IoT works include the product, the supply chain, and the manufacturing. Championing these three aspects would enable charting a crystal-clear smart IoT adoption strategy.

 

Predictive Maintenance & Performance Tracking

 

Big enterprises have heavily invested in their IoT and IIoT infrastructures in 2022. In 2023, much attention would be directed toward predictive maintenance and performance tracking in products and plants.

 

In today’s advanced industrial environment, factories are even looking forward to lights-out manufacturing (total automation). IIoT solutions and deep analytics software today can enable end-to-end production processes’ performance tracking by manufacturers. Novel machine learning (ML) technologies have facilitated predictive performance tracking capabilities via sensor-collected big data analyses.

 

ML can also facilitate performance tracking to get better with time, enabling industries to operate for sustained periods without human oversight.

 

Equipment downtimes in production environments result in out-of-the-blue delays and subsequent losses. So, smart operational schedules are a must for efficient execution and asset availability. That’s when predictive maintenance can considerably decrease machine failures and power outages, boosting overall asset life.

 

Use predictive maintenance to enhance asset life-cycle, reduce maintenance costs and time, and decrease machine breakdown to enhance equipment monetization It also allows a reduction in accidental malfunction to strengthen workers’ physical safety.

 

Augmented Reality (AR) and Virtual Reality (VR)

 

AR (Augmented Reality) and VR (Virtual Reality) can be accessed to experience immersive training, remote assistance, and collaboration. Instruction manuals’ digitization via AR and VR is the next frontier in redefining industrial technologies.

 

Capitalizing on the prospects presented by data exploration, Virtual and Augmented Reality can minimize waste in industrial processes. VR and AR can be easily integrated with IoT. Together these technologies can reap tremendous benefits. Such breakthroughs can come in the form of higher profits, exploring fresh growth avenues via new products or services, decreased costs, etc.

 

AR acts as an enhancement of the user’s surroundings via the addition of digital components in a live environment. VR replaces real-life environments with simulated ones as the user is completely immersed in a digital environment. In the manufacturing sector, VR and AR can be leveraged to make available designs, organize manufacturing lines, sharpen ideas, and remote machinery interaction. If in the building process either of the steps is omitted, its identification is possible via AR and VR.

 

AR and VR can be integrated in these ways with IoT:

 

Asset Management: Equipment know-how, performance, and health data are collected via IoT sensors to transform these into their virtual form. By doing so, real-time visualization of breakdowns and crashes is possible.

 

Space Management: The AR technology optimizes inventories in factories or warehouses, developing optimal routes for workers to navigate across facilities.

 

Employee Training: AR allows manufacturers to develop virtual prototypes of products for integration with IoT data. By doing so, it can develop a simulation wherein your employees can learn machines’ optimal usage.

 

AR and VR can guide technical work by offering real-time instructions. These technologies can also facilitate technical support on a remote basis, making training experiences real. AR can analyze machine environments’ problems. Computer vision in AR can give a map of machines, allowing highly-skilled laborers to witness real-time manufacturing processes’ stats.

 

Big Data Insights to Boost Optimization

 

IoT technology essentially collects data. Industrial Big Data, for that matter, is a central aspect of Industry 4.0. A smart factory operates at its optimal level owing to an accurate collection and the finest analyses of data. There are also several challenges presented by Big data, such as the efficient collection of the required data (via IoT analytics) and attributing to it enough value (via integrating artificial intelligence). The availability of Big Data can make available deeper insights. Unlike traditional technologies, a multitude of production or supply chain aspects can be tracked via IoT devices. So, IoT is crucial from the Big Data and manufacturing perspective.

 

Edge Computing

 

The edge computing trend is eagerly looked forward to by the manufacturing industry as an enabler of automation implementation in factories and supply chain processes. Edge computing can enhance manufacturing processes via advanced robotics and machine-to-machine communication. Edge computing essentially involves various networks and devices surrounding the user to process data as near to its area of generation as possible at high speeds and volumes. The outcome of this is a result-oriented action plan in real-time. In manufacturing, via edge computing, several local edge network factory devices can enable processing with no need to send data (to a local server first). Edge computing is speedy, highly efficient, and indeed secure.

 

There is no need in edge computing for the sensitive manufacturing data to leave (for distant server processing) the factory premises. So, there is a reduced risk of hiccups or third-party intrusion. Enterprises of today can boost their business prospects via the integration of edge computing and AI, to develop Edge AI.

 

This way, edge computing facilitates undertaking AI computation close to the user at the onset of the IoT network, instead of on a cloud. So, enterprises can attain real-time intelligence in industrial operations, strengthen privacy and cybersecurity, contain costs, and better manufacturing processes.

 

Conclusion

 

Manufacturing industry solutions based on IIoT are poised for stupendous growth with efficient asset management and monitoring, predictive maintenance for reduced machine breakdown (via performance tracking), and boosted workplace safety. Entrepreneurs can look forward to profitable business with IIoT transforming business models.

 

Industrial IoT offers an avenue to manufacturers for leveraging emerging technologies for a smarter and speedier production environment. Manufacturers, today, are integrating solutions that facilitate the automation of business operations by leveraging advanced technologies to enable innovation.

The future of enterprises is digital. Transformation is the way forward for manufacturing. A smarter, speedier, seamless, and safer production environment is next. Digital transformation is paving the hyper-automation pathway for improving efficiency.

Advanced wireless networks are set to be used to reduce downtime as the Industrial IoT (Internet of Things) gets deployed everywhere. As the production landscape is in a constant state of evolution, it’s vital to clearly decipher what digital transformation actually means.

Digital transformation primarily integrates digital technology across various areas of a business. The objective is to considerably alter the manner of operation, for delivering value to customers. Digital transformation digitizes non-digital services, operations, and products. The underlying intention is to elevate value via invention, innovation, and efficiency.

Factories and processing plants globally are embracing automation for enhanced production, efficiency, and quality.

But the future automation platforms would offer vital smart insights for real-time operational efficiency. These timely recommendations might include, for instance, machine learning-powered automation in decision-making.

Industrial Internet of Things (IIoT) technologies, cloud and edge software, machine learning (ML), artificial intelligence (AI), and low-code platforms are poised to facilitate advanced automation. Together such an apparatus would enable companies to march towards reaping the advantages of implementing artificial intelligence of things (AIoT).

With manufacturing entering the digital phase, a stark momentum is apparent in customer expectations and technological advancements.

Companies can expect to boost efficiency, use data optimally, make innovation conducive, and control cost with digital transformation. What are the buzzing trends in digitally transforming the manufacturing sector? Let’s examine the 5 (manufacturing) digital transformation trends in 2023.

1. Industrial IoT

IIoT is a futuristic system to keep your business’ manufacturing lifecycle up and ready. Industrial IoT transforms processes for improving efficiency, whilst meeting the Industry 4.0 standards. In IIoT, smart sensors tend to enhance manufacturing operations. In an IIoT setting, Informed business decisions are swiftly reached at, courtesy of the intelligent devices and real-time analytics.

The idea is to boost internal processes’ cost-efficiency, whilst providing increased value to customers. An IIoT solution works in tandem with cloud computing and AI-based technology stack for robust manufacturing.

The utilization of such smartly connected devices can be done in the fields and factories, or at remote facilities. Look forward to automating core factory operations via applying IIoT solutions to deploy digital transformation. It tends to solve such pressing challenges as productivity, asset utilization, quality, and process automation.

An IIoT network of servers and devices tends to manage manufacturing and machinery lifecycles. It also collects, stores, and analyzes the data from the sensors, executes commands given remotely, develops smart alerts rules. Based on these, smart and actionable insights are prepared to streamline industrial systems, efficiency, and predictive maintenance. Refer to the generated trends monitoring and historical analysis reports, for predictive maintenance. 

1. Predictive Maintenance

Predictive maintenance is a prominent Industrial IoT trend, given its benefitting potential for the manufacturing sector. Predictive maintenance based on IIoT tends to utilize real-time data that analyzes the assets’ condition on an ongoing basis.

The benefits of predictive maintenance are an increased asset lifecycle, reduced maintenance costs, increased time efficiency, reduced machine breakdown boosting equipment ROI, a drop in unexpected malfunctions leading to better workers’ safety.

IIoT data lets companies use analytics tools to access insights. With it, companies use data visualization to assess the ongoing and predict the future performance challenges. 

1. Artificial Intelligence and Machine Learning

The integration of Artificial Intelligence and Machine Learning facilitates harnessing manufacturing data. These technologies’ assimilation in the service infrastructure and applications expedites the processing of data, thereby improving efficiency.

AI and ML-led digital transformation is boosting industrial robustness with operational streamlining via automation. Companies are actively investing in technologies that process massive data volumes in real-time and with accuracy.

Processing tons of data strengthens manufacturers to swiftly process and develop products while reducing costs and industrial waste. 

1. 3D Printing

3D Printing works towards developing products via digital data, wherein lasers work on materials’ coatings. Manufacturers can build prototypes with 3D Printing and troubleshoot errors to ensure seamless production at scale.

The not-so-affordable task of tooling is no more a necessity now with 3D Printing. Also, there is no need for physical prototypes.

3D Printing sharpens the competitive edge whilst giving constant feedback throughout the product launch pipeline. Making design solutions even more immersive, the intended users can get an actual feel of the product prior to actual manufacturing.

3D Printing enables mass customization and paves way for new geometries. 

1. Augmented Reality (AR) and Virtual Reality (VR)

Enabling the digitization of instruction manuals, AR and VR are poised to redefine mainstream industrial technologies in years to come. AR and VR can be leveraged to offer immersive training, remote assistance, and collaboration.

Together AR and VR can instruct on technical work via offering real-time instructions. These technologies can also facilitate technical support on a remote basis, and inject life in training experiences.

AR can analyze machine environments’ complications. Via computer vision, AR can give a map of machines (similar to a real-time visual manual). As a result, highly-skilled labor service turns into a “downloadable” skill. Workers can see real-time stats of the manufacturing process, with AR, to attain precision.

VR is a critical manufacturing process component applicable in technical training, remote equipment servicing, and reviews of designs.

       Conclusion

Customized, modular, automated, and efficient are the words that describe manufacturing of the future. Industrial evolution is a pressing need that provides robust potential of boosting the returns on investment. With robotics, AI, and IoT digitization in place, data insights and smart robotics investments would boost output and lessen cost.

Asset efficiency, less machine breakdowns, and higher workplace safety would transform business models with digital transformation of manufacturing.