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How AI is Driving Industry 4.0

18 Dec
2019
Industry 4.0

According to Industrial AI Market Report 2020-2025 by IoT Analytics, industrial AI is emerging as a new hot trend in IoT markets. Currently, the estimated global industrial AI market size is slightly below $15 billion and is projected to grow at a CAGR of 31% to hit $72.5 billion by 2025. This rapid growth in industrial AI is fueled by its two largest market segments, namely Predictive Maintenance and Quality Inspection and Assurance.

Market Segment 1: Industry 4.0 Predictive Maintenance

Predictive maintenance is a compelling industry 4.0 use case that nearly eliminates asset downtime and the high costs associated with a factory shutdown. By using AI-enabled computer vision and machine learning algorithms, it is now possible to analyze machine data to identify patterns and predict asset failures before they happen.

Predictive maintenance contrasts sharply with preventive maintenance. In the latter, maintenance windows are scheduled to manually inspect and monitor assets, which consumes more resources and is typically less effective to prevent productivity losses. AI-powered automated, continuous monitoring capabilities make predictive maintenance a promising alternative for manufacturers to lower service costs, maximize uptime, and improve production throughput.

The AI technology in predictive maintenance involves predictive analytics algorithms that apply insights to continuously streamed data from production assets to accurately predict asset health, generate alerts, and thereby avoid downtimes.

Large-scale availability of big data, advances in sensors and IoT connectivity, and the increase in venture capital investments are collectively driving the AI in the manufacturing market to reach $17.2 billion in 2025 at a CAGR of 49.5%.

Manufacturers can as well offer predictive maintenance as a post-sales subscription-based service to their customers thus creating new revenue streams and accelerating wider adoption of predictive maintenance.

Market Segment 2: AI for Industrial Quality inspection and Assurance

Product quality is a critical competitive differentiator for manufacturers. Industry 4.0 and AI now offer promising new avenues to automate quality control to drive down both operating cost for quality and defect rates in shipped products.

According to Technavio, the global automated industrial quality control (QC) market is poised to accelerate with a CAGR of 8% through 2021 to a market size of $688.6 million. One of the key drivers for this market is the benefits of using AI-powered automated QC systems in smart manufacturing plants.

Manufacturing operations rely on human inspections to identify defects and certify products. This approach suffers from lack of accuracy, standardization, speed and the ability to scale with high-speed production cycles. Traditional automated QC practices address some of the inefficiencies by replacing human-led audits with industrial cameras and sensors to take product images during various phases of production and then process these images to obtain actionable insights.

This traditional automated approach, however, lacks flexibility. For example, in the textile industry, classic machine vision cannot work with all types of fabric. Automation fueled by AI overcomes these limitations and is a significant source of productivity for manufacturers.

Using AI (deep learning and machine learning) machines can adapt to a wide variety of contexts just like humans. AI models can be trained to adaptively learn and identify new visual or audio patterns, analyze and communicate sensor inputs, test logs, etc.

AI takes automated quality inspection to a whole new level by increasing productivity and profitability due to lowered defect density. According to the Mckinsey report of Germany’s industrial sector, advanced AI-based image recognition techniques when used for visual inspection, fault detection, etc. can increase productivity by up to 50%. Specifically, when compared to human inspection, AI-based visual inspection using image recognition technology may increase defect detection rates by up to 90%.

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