How AI can help your manufacturing business

5 practical use cases for AI in manufacturing

1. Predictive maintenance

Predictive maintenance involves anticipating a machine’s maintenance needs and preventing breakdowns. Using acoustic, ultrasonic, temperature, vibration and oil analysis sensors, AI can detect early warning signs that operators may not notice.

Predictive maintenance helps businesses:

• schedule servicing before equipment fails
• reduce breakdowns and unplanned downtime
• extend the useful life of equipment
• strengthen after-sales service for machine manufacturers

Example: A business that sells equipment to other manufacturers can use sensor data to provide customers with a maintenance schedule. This makes after-sales service more professional and increases customer satisfaction. 

2. Demand and production planning

AI can analyze data on past orders, sales, seasonality, market signals and production constraints to help businesses forecast demand and plan production more accurately.

Using AI for demand and production planning helps businesses:

• reduce delivery delays
• avoid running out of stock or overstocking
• reduce equipment idle time
• improve efficiency and ease pressure on teams during busy periods

“Using AI to forecast demand and optimize production is an excellent use case for SMEs affected by seasonal demand or market fluctuations,” adds Mouhtadi.

Example: A steel manufacturer can adjust its production capacity to anticipate growing demand from international customers looking to build inventory and benefit from rising prices in their respective markets.  

3. Automating quality control

Production defects are one of the main sources of waste in manufacturing.

By drawing on camera feeds and closely tracking production processes and engineering documentation, AI can help determine where the most waste is occurring and when failures arise.

Example: In a metal forge, AI can detect when defective parts are being produced more often at a particular temperature and help adjust the temperature to correct the issue.

4. Virtual assistants for operators

Machine operators often need to ask their supervisors questions or request approvals, leading to interruptions and delays.

Mouhtadi explains that AI can give every operator on a production line “a colleague or virtual assistant” who knows the manuals by heart.

Using retrieval-augmented generation (RAG) chatbots can draw on a database to provide teams on the production floor with accurate information.

Example: A chatbot connect to an internal directory containing documentation on the manufacturing process. Operators can then quickly find answers to questions without having to contact their managers or search through printed manuals.

5. The interconnected smart factory

For more advanced businesses, Mouhtadi suggests a more complex use case, where all parts of the factory are interconnected, including all manufacturing processes.

“The idea is to have all the systems interconnected so they can trigger alerts dynamically. If inventory is running low while sales demand remains strong, the system flags the issue before it becomes a problem,” he explains.

For a manufacturing SME, this can mean:

• linking production, inventory, sales and procurement
• identifying mismatches between demand and capacity
• recommending preventive actions before problems arise
• giving teams a more coherent view of factory operations
• improving competitiveness

How to get started with AI in manufacturing

1. Identify a specific business challenge 

The AI projects that deliver the best returns are usually the ones that address a repetitive, costly and measurable problem.

The AI solution should also fit seamlessly into the current process, so as not to create extra work for staff.

“The right approach is to start on the production floor, not the executive committee’s dashboard. The executive committee provides direction, the production floor provides the means,” says the expert from Moov AI. “Your production team knows best where to find irritants, repetitive tasks, and areas where time or money is being wasted.”

If your goal is to reduce losses from machine failures, an AI use case focused on demand planning may not be the right fit, even if it reduces the number of hours worked. 

Use AI first to diagnose problems, then as a tool for transformation. It can help you determine whether your data is usable and identify steps that can improve the quality of your predictions.

2. Check data quality 

High-quality data is the foundation of any AI project. Start by confirming that the data you need is available and usable. 

For a business that is just getting started with AI, well-structured spreadsheets are often enough. Business management software, such as a CRM or ERP, is not required to start taking advantage of AI’s potential.

3. Start with a proof of concept

Choose one production line or process to test your assumptions. Many projects fail because they start too quickly or have too broad a goal.

4. Measure the results 

Within 6 to 12 weeks, you should usually be able to see results and decide whether to scale up your proof of concept.

Getting started

Identify a recurring, high impact process that can be automated using AI.

“Production planning remains the most broadly applicable use case. It affects the most factory processes, and improving it offers the greatest potential for transformation. Indeed, by covering a single production line end to end, you can predict the quality of the finished product when raw materials are ordered,” says Mouhtadi.

Next step

Get advice and preferential-rate financing to adopt AI, digital tools and state-of-the-art equipment.