HPC-powered artificial intelligence to take manufacturing efficiencies to a new level

Today, enterprises are leveraging the self-learning power of Artificial Intelligence (AI) and parallel process systems of a High-Performance Computing (HPC) architecture to customize business processes and get more done in less time. In the current unprecedented scenario, industries across verticals had to fast-track digitization and are testing HPC-enabled AI to synchronise data and build new products and services.

MarketWatch predicts that HPC-based AI revenues will grow 29.5% annually as enterprises continue to integrate AI in their operations. Moreover, with the growth of AI, Big Data, as well as the need for larger-scale traditional modelling and simulation jobs, the HPC user base is getting expanded to include high growth sectors like automotive, manufacturing, healthcare, and BFSI among others. These verticals are adopting HPC technology to manage large data sets and scale-out their current applications.

The manufacturing companies, especially, can reap the benefits of HPC as they strive to enhance their operations – right from the design process, supply chain, to delivery of products. A study by Hyperion Research indicates that for each $1 invested in HPC in manufacturing, $83 in revenue is generated with $20 of profit.

Similarly, they are leveraging Artificial intelligence (AI) and Machine Learning (ML) to accelerate innovation, gain market insights and develop new products and services. Manufacturing organisations have been able to introduce AI into three aspects of their business, including operational procedures, production stage, and post-production. According to a report by Mckinsey’s Global Institute, the manufacturing industry investing in AI is expected to make an 18% estimated annual revenue growth than all other industries analyzed.

Optimising processes together with HPC and artificial intelligence

As manufacturers aim to achieve optimal performance and quality output, their focus is to implement HPC-fuelled AI applications to proactively identify issues and enhance the entire product development process, thereby improving end-to-end supply chain management.

At the same time, M2M communication and telematics solutions in the manufacturing sector have increased the number of data points in the value chain. The usage of HPC drives sophisticated and fast data analyses to ensure accurate insights are derived from large data sets. Combining HPC with AI applications allows network systems to automate real-time adjustments in the value chain and reduce the breakdown time. This results in enhanced product quality, accelerate time-to-market, and make the production process more agile.

Substantial use of computer vision cameras in the inspection of machineries, adoption of the Industrial Internet of Things (IIoT), and use of big data in the manufacturing industry are some of the factors adding to the growth of the AI in the manufacturing market for predictive maintenance and machinery inspection application.

Enterprises in the manufacturing industry can use the power of AI with HPC capabilities to deploy predictive analytics. This will not only help them optimise their supply chain performance but also help design demand forecast models and use deep learning techniques to enhance product development. There will, thus, be a need of high-speed networking architecture and systems storage to roll out and power the AI-based programs.

On the other hand, the manufacturing companies are increasingly leveraging HPC systems with Computer-Aided Engineering (CAE) software for performing high-level modelling and simulation. And there is a significant inter-dependability between HPC-powered CAE and AI, where simulations generate huge sets of data and AI models apply data analytics repetitively for even higher quality simulations. By now it is evident that the integration of CAE and AI will accelerate product development and improve quality; however, the scalability required to address the Big Data and compute challenges can only be managed by an HPC infrastructure.

Cloud-enabled approach to HPC

More data means more modelling, and, therefore, a more intensive machine learning solution. It is also important to invest in an HPC-Cloud for faster delivery of results by AI/ML models. A cloud-enabled HPC will help companies scale up their computing capabilities, as many AI workloads run in the cloud today. HPC applications built on cloud, allows companies to innovate by incorporating AI and enhance operations. AI workflows require continuous access to data for training; however, it can be a task to do so on-premise.

Today, manufacturing companies can choose from hybrid and multi-cloud options to provide a continuous and smooth computing HPC environment for on-premise hardware and cloud resources.

The power of one

The manufacturing industry stands to benefit most from the convergence of HPC and AI technologies. Instead of using AI and HPC as different technologies, the organizations in this sector are unifying the two clusters to reduce OPEX cost and optimize resources. Just to reiterate, the powerful combination of HPC and AI tools are helping manufacturing companies in high-quality product development, improvement of supply chain management capabilities, analysis of growing datasets, reduction in forecasting errors, and optimal IT performance.

By combining AI and HPC capabilities, the manufacturing sector has found multiple ways to deliver the right products and services, accelerate time to market, and drive efficiencies at each stage of development.

By Manish Israni, Executive Vice President and CIO, Yotta Infrastructure