AI in manufacturing: Practical examples, use cases, and benefits

In manufacturing, efficiency is the engine of excellence. 

Repetitive artifact creation within manufacturing is now routinely handled using generative AI (GenAI). GenAI can analyze your business’s corpus of documents to generate documentation, summarize information across multiple documents, create templates, and produce quotes, term sheets, project management assets, or operational and safety manuals.

Leveraging GenAI a step further, many manufacturers have already integrated it directly into their operations. They are using it to analyze proprietary company data from various sources like production lines, quality control systems, and customer relationship databases to create detailed, company-specific documentation that can be used across the organization.

Excel, for example, is an excellent tool, but it can lead to limitations in scalability, collaboration, and real-time insights. Version control is also difficult to maintain, and there’s a risk of documents getting lost or interpreted differently. Creating a single source of truth takes the ambiguity and chance for error out of the operation.

Another practical area that can drive efficiency is cloud technology, which simplifies the process of value extraction without the need for expensive and complex proprietary solutions.

“Cloud can help you aggregate your data from all departments, get a holistic view of your business, and generate reports in real time to help you make decisions,” Dostaler says. “Moving to cloud-based data systems can also lower the capital cost of having to hold the data on-premise.”

Predefined models can be applied immediately, simplifying the implementation process.

In a manufacturing landscape where you are selling as you produce, understanding the minute-by-minute changes in production, demand, and resources becomes paramount.

Data analytics and AI-driven insights propel rational and robust decision making, using large volumes of data to generate predictive models and trend analysis. These valuable insights empower strategic planning, enabling manufacturers to shift from being reactive to proactive.

“The more awareness you have about your operation, the more informed your decision making can be. The ability to have real-time data and situational awareness amplified by having machine-based predictions can make every one of those decisions have a greater impact on productivity," says Dostaler.

Reporting solutions like Microsoft’s PowerBI or Tableau Software can provide highly detailed insights into production numbers or sales forecasts. Depending on the sector, consideration can also be given to external data like weather patterns that may impact operations.

AI-driven systems streamline data capture with pinpoint accuracy throughout the entire production process. These smart systems can automatically gather data from sensors, machines, and production lines to identify patterns, revealing gaps, opportunities, and data attrition. This holistic view helps sculpt a fully integrated manufacturing process where every cog in the machine is informed by data.

Interactive data visualization software, equipped with a GenAI-powered Q&A feature, enables instant creation of highly customizable reports right at your fingertips. These GenAI-enabled features are now standard in leading platforms like PowerBI, making self-serve analytics more intuitive and immediate than ever.

Data empowers manufacturers with comprehensive visibility into their supply chains, allowing them to accurately forecast demand, monitor inventory levels, anticipate shortages, and implement a successful preventive maintenance program that improves OEE.

“If you can predict when machinery needs to be maintained and you have a schedule for it based on your predictions, you can optimize your supply chain so you have the right people and the right parts in the right place at the right time,” notes Dostaler. “You may also have the advantage of buying parts in bulk because you have a better forecast of what you'll need, reducing your costs without overstocking your supply.”

This level of supply chain visibility paves the way for a deeper analysis. The application of data to 80/20 analysis is fundamental for staying competitive and maximizing ROI while ensuring operational cohesion from the production floor to the finished product.

The 80/20 principle suggests that 80% of outcomes come from 20% of causes. For a manufacturer, this might mean that 80% of production output comes from 20% of the skilled workforce, or that 80% of sales might come from 20% of product lines.

Discover how an 80/20 analysis can unlock hidden efficiency in your manufacturing.

As expectations evolve around sustainability considerations and the strategic management of supply chains, the manufacturing sector is increasingly turning to technology as a resource to tackle these emerging challenges head-on.

With the aid of technology, manufacturers can embed sustainability values deeply within their supply chain processes, elevating operational efficiency and promoting a culture of sustainability and social responsibility. Moreover, AI's ability to optimize operational efficiency leads to decreased waste and, subsequently, a potential reduction of overall production costs.

A growing skills gap and ongoing labour shortage are significant obstacles in manufacturing, but they also offer a clear call to action for industry leaders, providing endless opportunities to transform these challenges into stepping stones toward a more resilient, skilled, and adaptable future.

“Efficiency is strongly correlated to the amount of labour you need to do the job. With digital tools, you can gain a better understanding of your business and predict certain conditions, allowing you to optimize how you’re staffed depending on those needs,” says Dostaler.

Robotic process automation (RPA) and AI automation can take over highly repetitive, simple tasks, freeing up human employees for higher-value tasks.

“It's important to understand that data and AI are not there to replace existing jobs,” adds Dostaler. “They're there to assist and facilitate work so that people can be more productive and focus on tasks that require human interaction, creativity, and problem-solving skills, and less on the monotonous, repetitive tasks.”

Implementing RPA is far less complicated than widely assumed. In most cases, it can be overlaid on top of existing applications and solutions, without requiring a complete system overhaul.

GenAI is also being used to support workforce training and productivity by generating process documentation, answering system queries, and assisting with onboarding materials.

Practice shows that manufacturing environments that embrace innovation also act as a magnet for top-tier talent attraction. The current generation of workforce is eager to engage in environments where state-of-the-art technology is embraced, making tech-forward manufacturers stand out as attractive workplaces.