What We Can Learn From AI Use Cases in Manufacturing

As one of the top data-generating industries, manufacturing presents opportunity for AI adoption. AI is playing an increasingly important role in day-to-day operations and the entire sector.

Now, with growing practical use cases, manufacturing is emerging as a leader of the pack on AI adoption.

AI Use Case Trends

Although AI has raised as many questions as it has answered, manufacturers are well-positioned to use AI throughout the value chain due to the vast amount of operation data available. Manufacturers are increasingly evaluating and adopting AI solutions to leverage their data, a trend publicly traded companies have been highlighting to investors in company presentations and earnings calls.

A 2023 report from Capgemini found that 55% of a survey group of industrial manufacturing companies “have started exploring the potential of generative AI” and 45% “have begun working on some pilots of generative AI initiatives.”

To identify trends in how manufacturing organizations are adopting AI, RSM reviewed earnings and investor presentations for publicly traded companies. While specific use cases vary widely by industry and sector, here are some key areas that stood out in how manufacturers are adopting the technology:

Predictive maintenance improvement: The use of AI in predictive maintenance enables a shift from reactive to proactive strategies, leveraging data-driven approaches. AI algorithms analyze real-time data to predict maintenance needs and failures, with recent surveys highlighting this as the highest potential benefit of AI adoption.

Through continuous monitoring and key performance indicator collection, AI identifies patterns on the factory floor, detecting anomalies and potential malfunctions. This proactive approach minimizes unplanned downtime, extends equipment lifespan and allows manufacturers to optimize resource allocation through scheduled service activities during planned downtime, enhancing overall productivity and reducing costs.

Energy efficiency and resource utilization: Companies are using AI to optimize energy consumption and resource utilization in manufacturing processes. These capabilities analyze real-time data from sensors, production equipment and other sources to identify patterns and trends in energy usage. This can inform predictive recommendations to optimize energy consumption, reduce waste and enhance overall resource efficiency.

Supply chain visibility: Manufacturers deal with enormous amounts of data in their operations, and the integration of AI technology allows real-time observation, quicker trend identification and more accurate forecasting to meet demand effectively. AI algorithms analyze historical sales data, market trends and external factors, enabling more precise demand forecasts and aligning production and inventory levels. This technology can help break down traditional decision-making silos, improving collaboration across the supply chain.

In logistics, AI optimizes routes by analyzing transportation costs, delivery times and traffic patterns, enhancing efficiency and cost-effectiveness. The strategic use of AI in the supply chain offers benefits like improved visibility, increased agility and better planning, enhancing overall resiliency and responsiveness.

Quality control enhancement: AI can improve manufacturing quality control through vision systems trained on images and videos, accurately detecting complex product defects. Real-time monitoring identifies issues promptly to prevent future defects, and AI’s continuous learning enhances defect detection. This can ultimately improve quality assurance and optimize processes.

Looking Ahead

The use of AI in manufacturing operations in coming years is only expected to accelerate. Investment in AI technologies is forecast to rise among 96% of companies by 2030, according to the Manufacturing Leadership Council’s June 2023 survey report on the future of industrial AI in manufacturing. In this context, developing the proper foundation to be able to harness AI capabilities will be essential for manufacturers that want to be on the cutting edge.

The foundational essentials for successful AI implementation span people, processes and platform considerations, as outlined below:

People

What changes might your organization need to make to ensure teams have the necessary skillsets and structure to implement and work with AI technologies?

How is your organization fostering a data-driven culture?

Processes

How is your company identifying and prioritizing potential AI use cases?

How do the company’s data governance measures take AI technologies into account?

Platforms

What types of data do you already have access to, and what types of data might you need to tap into?

Do you have the right systems and tools in place to enable AI implementation and adoption?

AI's facilitation of improved predictive maintenance, environmental efficiency and resource utilization, supply chain optimization and enhanced quality control signifies just the initial stages of its impact. Early adopters that define AI as a strategic priority will be in a position of strength as this space continues to evolve, benefitting from enhanced organizational agility in an increasingly competitive digital age.

Katie Landy is U.S. industrials senior analyst, RSM.