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Jon Taylor

Head of Brand & Content

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11 AI in manufacturing examples to increase efficiency

By Jon Taylor on June 11, 2024

It’s mind blowing to think how much change the manufacturing industry has seen over the last century.

100 years ago, it relied on assembly lines along with machine tools and skilled workers to operate them. Now, artificial intelligence (AI) is rising as a 21st-century revolution in manufacturing to give companies insight into everything from supply chain optimization to strategic planning and real-time hazard detection.

Manufacturing companies that embrace AI can significantly boost their efficiency, reduce operational costs and stay ahead of the competition. From predictive maintenance to quality control, AI applications in manufacturing are transforming how businesses operate.

Here are 11 ways to use AI to modernize your manufacturing process and grow your company 👇

AI in manufacturing: 11 examples

1. Inventory management

Linking AI to your inventory management system is the best way to spot bottlenecks and track product levels to reduce stockouts. 

As AI can monitor stock levels and sales data in real-time, it can automatically reorder materials when they hit a specific threshold to ensure there is no break in production. AI can also help manufacturers optimize inventory levels using historical order, product and location data. 

To summarize, AI allows manufacturers to have the right products, in the right place, at the right time across their entire network.

2. Demand forecasting

Traditional forecasting methods in manufacturing rely on historical data, estimations and human input.

Pairing AI with demand forecasting can ensure manufacturers have the correct product quantities at the correct locations. This is essential to meet order demands, avoid stockouts and reduce unnecessary over-stocking to maximize margins and profits.

AI demand forecasting in manufacturing: an example

AI can track product inventories and SKUs across sites and against customer demand. It will then compare this to stock on hand and allocation to help manufacturers efficiently meet customer demand. An advanced demand forecasting tool can go a step further by monitoring seasonal changes, competitors and supply chain disruptions to help manufacturers make more accurate decisions. 

3. Hazard detection

AI can do more than just accurately forecast demand or track inventory. It’s also perfect for detecting safety hazards and potential risks for your team. 

Instead of waiting for an incident to happen, manufacturers can use AI to monitor and identify hazards like spills, obstructions or equipment issues in the warehouse. AI can also store data from incidents and use past hazards to predict future risks. By analyzing patterns and trends, AI can forecast potential risks and help teams plan and (hopefully) avoid them. 

Once AI detects a hazard, it can raise the alarm in real-time to protect your employees on the floor.

4. Manufacturing supply chain optimization

Managing a supply chain involves numerous variables, from raw material availability to varying levels of customer demand. 

Incorporating AI into your warehouse supply chain optimization can help fulfill orders on time and minimize disruptions. For example, AI can model the most efficient route to transport materials while ensuring you meet customer demand. It can also optimize wave and workforce planning to ensure warehouses — and your workforce — are optimized for efficiency. 

These small but mighty changes in a supply chain can reduce costs and make sure every order is filled at the lowest possible expense to your business.

5. Reduce safety stock levels

The more safety stock a manufacturer has, the higher its storage costs are. AI allows manufacturers to adjust safety stock levels to meet demand and fluctuations whilst keeping storage costs low. Here’s how it works in reality 👇

Optimizing safety stock levels with AI: an example

Marshalls, a UK-based natural stone and concrete manufacturer, uses AI to help it produce and store materials based on demand. It uses Peak to calculate how much inventory to produce and what minimum stock levels it should store in each of its locations to meet customer demand. 

Thanks to AI, Marshalls makes 4,000 decisions daily about where to allocate orders and products to keep safety stock to a minimum.

Our continuing investments in digital and operational efficiency programmes mean that we are now in the best possible position to benefit from future market growth.

Sion Harrison

Digital Director at Marshalls



AI-powered inventory optimization. Minimized costs, maximized service.

6. Smart robots

Robots aren’t new for manufacturers, but the way they are being combined with AI is changing the game. 

Known as collaborative robots or “cobots”, these AI-driven robots are being used alongside human workers to perform assembly tasks quickly and accurately. The reason these cobots are working with humans and not replacing them is that both are needed on the manufacturing floor. The cobots will usually handle the more dangerous tasks, while humans manage the operations and make sure assembly tasks are being completed correctly. 

Check out how Phoenix Contact, a US-based manufacturer, uses cobots alongside its human workforce on its production line:


The cobot takes care of monotonous tasks like stacking products into boxes. When a human comes near it, it immediately stops its work. The manufacturer says this frees up human employees to do more important tasks, while the cobot’s automated work helps it meet consumer demand.

This is a great example of how AI can help improve existing processes and allow humans to take on more cognitively demanding tasks.

7. Predictive maintenance

The traditional approach to maintaining manufacturing equipment was to set a schedule, do regular checks and fix equipment when it broke down. Not only is this costly, but it also ensures key equipment is not being used for substantial periods of time. 

Manufacturers are now using AI to replace this system with predictive maintenance.   

AI algorithms analyze data from sensors to predict when equipment might fail and only conduct maintenance when it’s needed, instead of on a regular schedule. Not only does this mean AI is continuously monitoring equipment for faults, but it also reduces the downtime on the manufacturing line to minimize any supply chain disruptions. 

Take a look at how Siemens uses predictive maintenance to monitor gas turbines, compressors and generators ⬇️


Sensors send data to the manufacturer’s cloud-based system to flag any maintenance issues and minimize downtime in its factories. The result?

According to Siemens, using predictive maintenance has cut costs by 30%.

8. Compliance monitoring

Maintaining compliance with safety regulations is crucial for manufacturing organizations. But traditional methods of doing so require a lot of box-checking and monitoring that’s prone to human error. 

So, how does AI help manufacturers stay compliant? 

To start, it can run automated audits to ensure equipment, training and regulations are up to spec. As it can monitor data in real-time, AI can also immediately alert staff to any issues or out-of-date reporting to make sure everything is kept above board. Finally, when it’s time for official inspections, AI can generate comprehensive reports to meet compliance standards. 

Think of AI as a handy warehouse assistant that can take care of compliance in the background.

9. Energy management

AI can find ways to make manufacturers more efficient to cut the cost of their energy bills.  

Again, the power of AI is its real-time monitoring. It can track every second of energy consumption across manufacturing operations to analyze use and see if any savings can be made. For example, if a particular tool consumes a lot of energy but isn’t being used for blocks of time during the day, AI can power it down to reduce its overall consumption.

As AI collects a ton of data around energy consumption, it can also run audits to suggest improvements on what machines to power down or make more efficient to cut down on waste.

10. Strategic planning

It’s nearly impossible for humans to analyze market trends and plan accordingly without the help of AI. 

Not only can AI leverage data around materials and costs, but it can also keep tabs on external factors like supply chain risks. Feeding an AI model with this data also allows leaders to make more strategic decisions about outcomes and build a risk profile around increasing output, what products to produce and whether it’s a good decision to enter new markets.

11. Order management

Finally, AI can optimize order management and supercharge the entire fulfillment process. 

Any manufacturing company knows order fulfillment and meeting demand spikes can be knocked off course with human errors. Injecting AI into order management can look at past data like demand estimation, consumer preferences and supply chains to optimize inventories and improve fulfillment processes. 

For example, AI will collect data like inventory, product metadata and even warehouse locations. This data is then run through the AI model and consider orders by profitability or due date, time horizon and stock on hand. The model will then make a real-time recommendation about what orders you should ship from which site, including the products and quantities required to deliver on-time and in-full (OTIF). 

Want to understand how AI can optimize your manufacturing processes?

Click the button to learn more about our AI for inventory software.

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