Ever since the industrial revolution, the world has been changing exponentially. Industries, especially manufacturing, are the foundation of the global economy and our combined growth. With digital technology coming into the picture, the Fourth Industrial Revolution (4IR or Industry 4.0) is founded on the disruptive nature of digital technologies like AI, machine learning, IoT, automation, and analytics to harness the optimum potential of the manufacturing sector.
Industry 4.0 is itself a precursor of Industry 5.0, which will be a radical shift in the next stage of industries. While 4IR focuses on efficiency and sustainability, Industry 5.0 shifts the perspective towards the benefit to the people and the planet. Although, ESG and other social goals cannot be met without Industry 4.0 upgrades.
The AspenTech 2020 Industrial AI Research stated that 83% of large industrial companies believe AI can deliver better results. However, only 20% have been able to adopt it so far. This is due to the lack of domain expertise in AI and digital infrastructure. And that’s just a fraction of the complexity involved in implementing AI into manufacturing and related processes.
The gains to be achieved with AI operations (AIOps) far supersede the technical difficulties involved in implementing it. The 4IR technologies are expected to create a $3.7 Trillion value by 2025, with AI alone clocking up to $2 Trillion out of it (McKinsey). AIOps can be implemented in almost every aspect of manufacturing, generating value and overall efficiency. Let’s look at the most trending applications of AI in manufacturing.
Predictive Maintenance and Plant Health – Deep Integrations
While still a little ahead into the future, manufacturing is estimated to produce roughly 1800 Petabytes of data, much more than any industry (Deloitte). Specialized sensors and hardware are required to generate the data, especially considering most machinery and equipment in use are not designed to be monitored that way. However, the shortcomings of the physical systems can be overcome with AI as it continues to build correlation and connections; learning based on the data and testing predictions against monitored outcomes.
The end-to-end process visibility AIOps can provide is unmatched even now. It can enable machinery and equipment to last longer and work without affecting the processes. According to a McKinsey survey, 16% of the industry experts in the sample reported up to 19% cost savings in operations by using AIOps. These savings are a combination of efficiency, better equipment performance, and lower downtimes. And there’s more to be achieved as the hardware and software become improve.
Demand Forecasting and Optimized Production – Halfway To Go
Keeping production constant while meeting the variable demands throughout a long period is a balance most manufacturing companies strive to achieve. Demand forecasting is a fairly difficult task, but AI-enabled analytics has the potential to provide practical results quite consistently. This can be directly applied to production planning, reducing losses in multiple aspects. Most manufacturing processes can be optimized on multiple facets, from procurement to inventory management and sales. This application will also pave the way to Industry 5.0 goals of sustainability.
4IR technologies powered by a central AI can provide deep analytics and insights for an optimal production environment and output. Combined with automation and robotics, much of the production can run without human intervention. In fact, few Japanese manufacturers already have a robots-to-human ratio of as high as 14:1. With training and development, the human element can add greatly to the effectiveness of AIOps as the industries bridge the gap between old and new (and often incompatible) technologies.
Quality Control and Supply Chain Management – Current Affairs
Two of the most current applicable uses of AIOps are found in quality control and supply chain management. Computer vision technology combined with AI has already reached a point where it can be applied to a diverse set of activities. BMW Group already uses the technology for automated quality testing and related purposes. AIOps in supply chain and logistics management is already well adopted in many industries as well, especially where exhaustive, time-sensitive material and products are concerned.
SLK Software is a digital transformation company that follows a collaborative approach to project management. We already have ample experience in the manufacturing industry, providing modernization and digital transformation to our clients. Our futuristic approach has enabled us to implement AIOps in IT and IoT infrastructures for manufacturers with a holistic view for future integrations.
AIOps can bring a world of change in manufacturing, with some applications available right out of the box while others require long-term planned implementations. However, true potential is unlocked with centralized AIs that can be integrated with current and future implementations of 4IR technologies. It is important to understand the nature of business and manufacturing units while planning the implementations. The impact on an organization will uniquely depend on the scope of implementations and the processes where AIOps can make the most difference. When taken into account, making the difference is simply a matter of knowing the difference!