The potential for artificial intelligence to transform manufacturing

Aug 10, 2023

Key takeaways

Middle market organizations need to understand applications, benefits and challenges of AI.

AI can help manufacturers get a more accurate forecast to ensure supply meets demand.

Data security, ethical considerations and reliability are some AI-related concerns.

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Manufacturing Artificial intelligence Economics

AI can play key role in predictive maintenance, quality control, supply chains

Manufacturing is one of many sectors across the economy adopting generative artificial intelligence (AI) and exploring its potential to revolutionize processes and unlock new human capabilities. As we navigate a world where machines able to mimic human intelligence and automate complex tasks are increasingly prevalent, organizations need to understand the applications, benefits and potential challenges these technologies bring, especially to the middle market.

AI evolution

The manufacturing industry has witnessed a remarkable evolution in the past two decades, with the adoption of advanced technologies, particularly AI, leading the way. Today’s manufacturing landscape bears little resemblance to previous industrial revolutions. From early machine automation and data integration to advanced stages of machine learning, collaborative robots, and intelligent supply chains, the impact of artificial intelligence within the manufacturing sector has been nothing short of revolutionary. 

As with any new technology, manufacturers are taking a methodical approach to the use of AI; nearly 57% of executives surveyed said they were experimenting with a range of small-scale pilot projects, according to the June report of the Manufacturing Leadership Council (MLC) on the future of industrial AI in manufacturing. The integration of these advanced technologies into the manufacturing process has ushered in a new era of human-machine collaboration, bringing vast new possibilities but also notable concerns.

Use cases and benefits

AI’s growing significance in manufacturing cannot be overstated. This game-changing technology has already unlocked a wide array of benefits and introduced unprecedented levels of efficiency, productivity and innovation to manufacturers. As organizations continue to better understand how they can apply and manage AI, they should consider several notable use cases:

Improving predictive maintenance

AI empowers manufacturers to move from reactive maintenance approaches to proactive and data-driven strategies. Predictive maintenance uses AI algorithms to analyze real-time data from sensors and other equipment to anticipate equipment maintenance needs and failures. This use case is the more significant potential benefit of AI adoption in comparison to the other use cases noted below, according to the aforementioned MLC survey. By continuously monitoring data from the factory floor and comparing it against key performance indicators, AI can detect anomalies or early signs of equipment malfunction. This allows for proactive measures, avoiding costly, unplanned downtime and improving the life span of the equipment.

The ability to schedule service activities during planned downtime optimizes resource allocation and maximizes availability of equipment. Predictive maintenance also enables condition-based maintenance, as compared to traditional maintenance practices that involve routine inspections and replacement schedules. Performing maintenance only as needed leads to cost savings and minimizes costly repairs or replacements. By leveraging data analysis, pattern recognition, and predictive modeling capabilities, industrial organizations can optimize equipment performance, increase operational efficiency, reduce costs, and enhance overall productivity.

Optimizing supply chains

Manufacturing operations generate and consume vast amounts of data. AI can help manufacturers harness that data to identify trends more quickly and generate a more accurate forecast to ensure supply meets demand. Complexity in the supply chain continues to increase exponentially, and strategic use of AI—to analyze sales data, evolving market trends and external factors—can provide a competitive advantage. This advanced technology can improve collaboration across supply chains and break down traditional silos of decision making that hinder organizations’ ability to analyze data in a timely manner.

AI also plays a crucial role in logistics and route optimization; algorithms analyze transportation costs, delivery times and traffic patterns to determine the most efficient and cost-effective routes for shipping and distribution. AI-driven supply chain optimization can also allow companies to analyze supplier performance data and better assess their reliability and quality to proactively identify potential issues.

Better visibility, increased agility and better planning ranked nearly equally on the MLC survey as potential benefits of AI adoption for supply chain. Overall, the strategic application of AI across the supply chain will greatly increase resiliency and responsiveness. 

TAX TREND: Artificial intelligence

For manufacturers adopting or advancing their AI and automation capabilities, integrating new systems with modern tax applications can help effectively manage complex tax and financial data. For example, supply chain transactions often have tax implications for which effective data processes are crucial to managing costs and compliance. Involve the tax function at the outset of any project to promote an effective integration.

Enhancing quality control and defect detection

AI-powered vision systems—including images and videos—can be trained to recognize patterns and anomalies with a high degree of accuracy, identifying complex or even subtle defects that would often be challenging for a human to detect. With real-time monitoring, manufacturers can identify and rectify issues before they result in defective products or quality failures, improving overall process control.

Additionally, continuous learning allows AI systems to update models and adapt to evolving production requirements and conditions, further improving the detection of defects. An AI-driven quality control program can enhance both quality assurance and process optimization for manufacturers.

With many manufacturers only just starting to leverage AI, success metrics are still largely undefined, with over 60% of MLC survey respondents noting this gap. The development of these metrics will be a critical step forward for companies looking to increase AI usage. 

CONSULTING INSIGHT: Generative AI

Generative AI is revolutionizing the development and delivery of products and services, and many organizations are working to understand how to use this technology. Learn how you can capitalize on the generative AI trend, increase value and mitigate risk. ­ 

Challenges and concerns

Manufacturers are expected to accelerate their use of AI in operations in the coming years, with levels of investment predicted to rise in 96% of companies, per the MLC; however, understanding the technology’s implications will be an essential first step before adoption.

The most pressing concerns around the use of AI include: 

Job displacement

Debate about potential job displacement continues as more tasks and processes are automated. However, we expect manufacturers will need to hire more people in the future, as AI requires upskilling of current job roles and creates demand for workers who can perform high-value tasks. 

Data security and privacy

AI’s heavy reliance on data requires manufacturers to safeguard sensitive information with a robust data protection program. According to the MLC, data issues (access, format, integration, privacy and governance) are the biggest challenge to AI adoption. 

Ethical considerations 

To avoid biased outcomes and discrimination arising from AI algorithms and pattern detection, manufacturers must ensure transparency in system design, data collection, and decision making, and address ethical considerations from the outset.

Reliability

Completeness and accuracy of the data derived from AI is critical to ensure the reliability and trustworthiness of the system. To generate new content, AI systems rely heavily on quality training data that is diverse and represents the communities it serves. If the training data is biased or unrepresentative, AI systems may reproduce those biases in the content generated, resulting in potential stereotypes, discrimination or misleading information.

Legal framework

Rapid adoption of AI technology has outpaced the development of regulatory guidance, but manufacturers should be prepared to closely monitor compliance to ensure responsible use. Opinions vary on whether ethical considerations around AI should be defined by individual companies, through cross-collaboration involving industry, government, and academics, or solely by the government.

Addressing these concerns will require coordination and collaboration among manufacturers, regulators and stakeholders to ensure responsible adoption and use across the sector.

A new era

For manufacturing companies, AI’s potential to enhance predictive maintenance, optimize the supply chain and improve quality control is only the beginning. Increased productivity, enhanced decision making and improved cost savings will continue to drive broader adoption of the technology across the middle market. And while challenges exist, if companies plan carefully, invest in infrastructure, and focus on ethics, AI will continue to revolutionize the industry, creating smarter factories and equipping manufacturers to stay competitive in the digital age.

RSM contributors

  • Katie Landy
    Katie Landy
    Principal

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