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.
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.
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.
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.
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.
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.
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.