Say Hello to Industry 4.0, and the Benefits of AI and Machine Learning for Smart Factories
The idea of leveraging the cloud in company strategy is synonymous with speed, agility and innovation. It’s not surprising that across business and IT, cloud technology is helping to support an era of incredible growth. What might be less obvious, however, is the way that industries such as manufacturing and automotive are benefiting from the same public cloud technology.
Across the board, companies are reinventing their products, processes and machinery to create smart factories. At both the back-end and the front-end, these speed up operations, improve processes and open up new revenue streams.
If you’re looking to make a digital transformation in a traditional industry like manufacturing, AWS could be just the infrastructure you need.
Benefiting from Data in Industrial IoT
Data is the lifeblood of the cloud, and being able to access, analyze and utilize untapped data is one of the reasons why Industry 4.0 has taken off. For the first time, many industrial plants and factories can create Data Lakes, where all data can be aggregated either with or without structure. Even if you might not have a use for this data today, it’s accessible for tomorrow’s innovation. Once this data has been collected, analytics engines can be used to help make strategic business decisions, answering previously unanswerable questions such as ‘Which machines are performing best?’ ‘Where should I invest more resources?’ or ‘How can I combat idle time?’
AWS provides tools for benefiting from data from end-to-end. One great example is AWS Greengrass/Edge. This technology allows your business to pull data from field devices and industrial plants, including sensors and control systems, even in situations without internet access. Another option is AWS IoT SiteWise. Here, data is aggregated and classified from industrial equipment at scale, across geographies, machine types and more.
Leveraging Artificial Intelligence and Machine Learning for Industry 4.0
Without any need for in-house data collection, manufacturers can use this data to benefit from Machine Learning capabilities. These provide predictive analytics on machines and production, highlight process inefficiencies, and streamline performance.
With a sophisticated Data Lake solution, even mixed streams of data can be cleaned, organized and analyzed by creating data sets that work for your business. This might involve an automotive manufacturer pulling data on a certain make or model of vehicle to look for consumer trends over a specific period of time. By adding AI and ML into the mix, these trends could be found for you, without the need for a gut instinct or an initial hypothesis. Open-ended questions as vague as ‘How can I make this process better?’ can be answered for the first time.
One easy to visualize example is Georgia Pacific, one of the largest manufacturers of paper towels and related goods. Production data was pulled from the machinery and sent to Amazon SageMaker, AWS’s fully managed Machine Learning deployment platform. Here, the analytics platform could provide real-time feedback on the process, and then suggest the top three drivers of low productivity, breakage of the delicate materials, and the variables that needed to be adjusted by human operators for maximum efficiency and quality control. The company has eliminated 40% of parent-roll tears, and saved millions of dollars in the process.
Smart Factories at Low Cost
It’s not just about analytics and data. High-performance computing capabilities are making a huge difference in what traditional industries are able to achieve. Take a company like Innovium, which creates the silicon for Ethernet switches. The VP of Tech felt that the company was held back on-premises by the lack of physical space, cooling capacity, technology and power necessary to scale, expand or even deliver reliably.
On the cloud, this has been turned on its head. The company now has an HPC environment on AWS, and uses high-memory EC2 instances. Innovium can now scale to 264 cores per job, scaling quickly and improving performance across multiple machines. As well as the improved performance for every single process, the reliability of knowing that they will get it right every time, and never become low on resources has improved company confidence and efficiency as a whole.
The icing on the cake is that this almost unlimited capacity comes at a very low cost, with a small initial investment and the ability to use PAYG microservices and serverless computing.
Leveraging Industry 4.0 to Focus on Innovation
The combination of smart infrastructure and low cost has freed up the manufacturing industry to put more emphasis on innovation and design. Rather than allocating your revenues for large capital investments, you can save resources for the next big thing. On top of this, your ability to innovate is improved, as an HPC environment in the cloud is far easier to design, test and deploy using in comparison to on-premises environments. Extras such as the ability to scale large numbers of tasks in parallel to one another, or quickly solve compute-intensive problems have helped designers and engineers do much more in a shorter period of time.
With improved operations, more efficient processes, streamlined quality control and infrastructure headaches taken off your hands – the cloud is making waves in industrial settings, and the majority of Industrial IoT developers trust AWS to implement their solution.
Not sure where to start? AllCloud’s Data Practice has years of experience in strategic planning for industrial environments looking to leverage digital transformation on the cloud.