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Ongoing disruption is the “new normal” of today’s global manufacturing environment. Just two years ago, manufacturers found themselves in the vortex of a pandemic, requiring massive process and infrastructure changes to remain competitive and overcome unprecedented workforce and supply chain challenges.
Technological innovations, including artificial intelligence (AI), edge computing, and universal automation, have recently proliferated and converged across the industry. Plant managers and their teams now face the daunting challenge of assimilating the new trends into their operations.
New technology means more than just disruption and their rewards for investment and change can include:
- Increased productivity
- Lower development and production costs
- Improved product quality
- New product innovation
- Enhanced competitiveness
Three technologies leading the future of manufacturing
Key technology innovations contribute to achieving new industry manufacturing productivity and competitiveness goals and can complement each other:
- Artificial intelligence – AI and machine learning (ML) algorithms enhance real-time data analysis for more accurate predictions and minimize problems like product defects. Repetitive tasks prone to human error are now easily automated. AI learns production patterns and behaviors so staff can manage output more quickly and efficiently, reserving time and skills for complex problem-solving and innovation. For instance, manufacturers can use AI to evaluate plant equipment conditions and predict when maintenance should be performed, lowering unanticipated downtime.
- Edge computing – Edge computing implies a network of geographically distributed computing devices that, while capable of cloud connection, are not exclusively supported by cloud services. For low-latency applications, a centralized cloud computing model may struggle to process all remote data with acceptable response times. For this reason, much of the data produced across manufacturing lines will never be transmitted to the cloud. Still, they will be processed at the network’s edge, providing flexible computation, data storage, and communication to large numbers of users who work close to their machines. Instant access to machine data helps to drive faster and more accurate production decisions.
- Universal automation – Universal automation represents “plug and produce” automation software components — akin to an industrial app store. It decouples hardware from software to create portable, interoperable, and software-centric industrial automation systems. These systems can drive significant improvements across the entire operational lifecycle.
Harnessing the collaborative power of the three technologies
Using the Food and Beverage industry as an example, let’s consider how AI, edge computing, and universal automation converge to boost productivity and lower costs. Software platforms like Schneider Electric’s EcoStruxure Automation Expert, a universal automation tool with a hardware-agnostic virtual controller, can help streamline machine design for OEM engineers by integrating AI and edge computing capabilities more efficiently.
Imagine an online packaging machine for assorted gourmet chocolates. If it fails to place the chocolates precisely in the correct locations in the box, the box will be rejected and require manual repackaging, slowing down output and reducing profitability.
However, by integrating AI into the machine (by training cameras to differentiate between a good box of chocolate and a bad one) and using an attached edge computer for real-time decision-making on the conveyor belt path, the machine becomes more autonomous. The machine can make the package and detect its quality without human intervention. EcoStruxure Automation Expert’s event-driven nature helps machine builders more easily integrate the benefits of AI, edge computing, and universal automation. EcoStruxure Automation Expert works with AI to enhance productivity for plant floor workers by reducing error management time and allowing more focus on boosting output and lowering downtime.