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Manufacturing is a crucial sector that enhances life in every aspect.
Machines make work easier; electronics help us communicate, chemical products complement our daily activities. Without these products, the world would be far less habitable. So your output as a manufacturer significantly improves life.
Yet manufacturing involves a complex set of processes and many workers collaborating to produce the end product. And each of these steps comes with challenges.
How do you solve these challenges?
In most cases, by implementing new technology.
The latest innovation, artificial intelligence, has made its way to the manufacturing industry and is radically transforming processes.
A survey by Deloitte found that 93% of companies believe that AI will be a crucial technology that contributes to the growth of the manufacturing industry.
Do you believe so too?
If not, let me take you through 5 innovative applications of artificial intelligence in manufacturing in 2021.
These use cases will demonstrate how AI can save you a lot of time, energy and resources from your manufacturing process.
1. Quality Assurance
Companies thrive on the production of high-quality products. If you have a reputation for shoddy work, your business won’t last long. Hence, ensuring that your products achieve the set standards is paramount before you even think of sales.
Yet, quality control is a complex process requiring a defined process, inspection, and testing, among other activities.
Measuring, collecting and reporting quality metrics is especially challenging. First, the question of whether the metrics accurately represent the data being collected is always on the table.
Secondly, are you certain that the metrics play a crucial role in quality assurance?
If a quality control culture is not embedded in the entire manufacturing system, then the impact of other processes on product quality might not be accounted for.
Of the many applications of AI in manufacturing, quality assurance is among the most significant. According to Capgemini’s research, 27% of manufacturing AI use cases are on quality assurance.
Assembly lines are data-driven networks that work based on parameters to produce the best possible guidelines. With AI, their efficiency can be guaranteed.
Machine vision technology detects defects from the standard product outputs and alerts workers to make appropriate adjustments.
By providing more eyes in the manufacturing process, AI empowers you to address the challenge of product anomalies and recalls.
Hexaware leveraged artificial intelligence to optimize quality control for a heavy vehicle manufacturing company. The implementation led to a 25% reduction in scrap rate and a 70% reduction in fault rates.
AI made the company’s inspection process smoother through automatic alerts if parameters were crossed. Workers could shift their focus to other functions without compromising on quality.
2. Inventory Management
The heart of your business is getting your high-quality products to your customers. Being on top of your inventory through efficient and timely management helps you meet the demands and make sales.
But the process of inventory management requires precision and thick skin.
By definition, it involves a myriad of activities such as; receiving, recording, storing, just to mention a few. Hence if you employ rudimentary stock management systems, you’re bound to encounter many challenges;
- Inconsistent tracking of products
- Inaccuracies in data collection and storage
- Supply chain complexities
Without a proper system, you will lose time and resources. Inventory inefficiencies cost the globe an estimated $1.1 Trillion in revenue opportunities. Recouping this loss can be a game-changer for your business.
Inventory management is an artificial intelligence use case in manufacturing. Machine learning algorithms can provide powerful insights for planning, predictions and modelling of inventory.
AI inventory tools can process stock data to make orders of raw materials needed. Also, they can estimate what demand will be like in future, and make recommendations for optimum inventory stock levels based on demand predictions.
AI can also analyze customer behaviour patterns, among other factors and help minimize the risk of mismanaging the stocking process for effective response to customer demands.
With such an elaborate inventory management system, what are the chances of overstocking or understocking?
Almost none!
A popular toy manufacturing company approached SynergyLabs AI to help them tackle increased loopholes in warehouse management. As a result, the toymaker experienced a 10% reduction in overall work time by implementing AI and cognitive applications into their work system.
Also, this change improved consistency, enhanced speed and resulted in a radical change in their business operations.
3. Predictive Maintenance
Just as you need to take your car for regular check-ups, your production equipment and assembly line tools require consistent maintenance. Repair and maintenance are necessary to preserve ideal operating conditions.
And you want to ensure your assets have a long life. If equipment is ignored, you’ll face machine failure, operational challenges and budget overrun.
But however elaborate your maintenance process is, you can never have a holistic view of all machines all the time.
Even if you hired manufacturing maintenance technicians in all equipment stations (very costly), they could not possibly watch over the machines 24/7.
What if you miss a crucial fault that could potentially lead to downtime?
Considering that the average cost of downtime is $260,000 per hour, is that a risk you want to take?
I have a solution for you; predictive maintenance, one of the most innovative applications of artificial intelligence in manufacturing.
This technology involves machine learning utilization in the proactive maintenance of equipment based on data.
An AI-powered tool constantly monitors your equipment through sensors, data collection and real-time communication between equipment and software.
With an extra set of untiring eyes on your equipment, you can lighten the burden of uncertainty, keep downtimes at bay, and avoid expensive equipment repairs consequently saving your business maintenance expenses.
Best of all, you can schedule maintenance when your equipment actually needs it rather than wait for it to break down so that you can repair it.
This innovative application of AI in manufacturing can offer many unparalleled benefits in productivity and efficiency.
Nissan, a global vehicle manufacturer, faced challenges with equipment analysis despite having an abundance of data. They worked with Senseye to develop a predictive maintenance solution that resulted in a 50% reduction in unplanned downtimes and an increase in overall equipment effectiveness.
Through AI implementation, Nissan now benefits from efficient, targeted preventative maintenance and asset life extension.
4. Generative Design
As a manufacturer, you get involved in the product design process, working with engineers, marketing experts, and other professionals to produce a product customers love.
Software such as Computer-Aided Design, design review, simulation and product life-cycle software significantly help you design products.
But no software recommends the best shapes, dimensions, and other parameters. All that depends on your knowledge, past experiences and personal preferences.
Consequently, you get into an endless and costly path of redesign to improve the products.
Lest you forget, the average direct cost of a recall is $10 million, exclusive of claims.
That’s a cost you wouldn’t want to incur, would you?
That’s why you should learn about one of the most innovative artificial intelligence use cases in manufacturing; generative design.
Generative design is an iterative design process that creates unlimited designs based on design goals and parameters such as performance requirements, materials and cost limits.
An AI-powered design software explores all possible permutations of a solution and quickly comes up with design alternatives. It also simulates and tests each design, learns from each iteration and recommends the best design that meets your demands.
Imagine entering conditions on a computer, having a cup of coffee, and in 10 minutes, the design software formulates a product that would have taken you decades to perfect.
Yet another automaker, General Motors, harnessed AI’s power by working with Autodesk to explore arrays of design solutions for vehicle parts. In one collaboration, they used Fusion 360 to formulate a seat that was 40% lighter and 20% stronger than a previous seat bracket.
Considering that the lighter a vehicle is, the more economical it is with fuel, this innovation can be a turning point in the automobile industry.
5. Process Optimization
Manufacturing is a business of creating products and selling them to offset production costs and make a profit. Hence, as a manufacturer, you are often concerned with how you can enhance your production process.
The rapid changes in manufacturing technology and shift in markets can be unforgiving. Yet, identification of changing patterns is a common challenge. Some of these changes can have a drastic implication for your company.
Without a good understanding of the conditions of your production assets, you’ll not identify opportunities, and manufacturing optimization will be out of reach.
Process optimization, an application of AI in manufacturing, can keep you at par with changes, help you spot opportunities, and make recommendations based on data.
According to McKinsey, 15% of companies use AI to optimize yield, efficiency, and throughput in manufacturing.
What’s holding you back from implementing AI?
AI will help you optimize processes and achieve sustainable production levels. AI-powered mining tools can identify and eliminate bottlenecks in the manufacturing process.
They can select process inputs to deliver optimal combinations of results based on historical data, monitor processes for anomalies identification, and manage risks in your operations.
For instance, for a business with factories in multiple regions, you can use AI to compare the performance of different entities down to individual workers and recommend changes to streamline production.
A large plastics manufacturer implemented the BHC3™ Process Optimization application, reduced average product transition time by over 30%, and saved millions of dollars.
Previously, the company had to transition manufacturing settings for each order and test each batch for quality assurance. The elimination of this bottleneck demonstrates how artificial intelligence use cases in manufacturing can significantly improve operations.
Conclusion
The 5 innovative applications of artificial intelligence in manufacturing we have discussed above are just the tip of the iceberg.
Many more innovations of AI in the industry exist while others are being invented as time goes by.
As a progressive manufacturer, you should stay informed of these innovations and apply what you can to improve efficiencies and increase your profits. AI-powered tools process data faster and better than you would and can give you accurate, actionable recommendations.
Also, if your competitors are using these advanced tools, they can have a massive advantage over you. It would be best to consider them in your manufacturing process.
Ultimately, you want more revenue at a lesser cost.
