What is the state of manufacturing today?
The manufacturing process entails many tasks and activities, such as inventory and supply chain management, that can sometimes be inefficient, affecting production.
Manufacturers often face challenges like defective product delivery and machine breakdowns.
However, these challenges are the very reason why we are experiencing a dramatic rise in the use of artificial intelligence applications in manufacturing.
According to a survey by Deloitte, 87% of manufacturing companies have adopted AI technology or plan to do so in the next two years.
From better design products to significant cuts in unplanned downtime, and providing data insight to revamp the manufacturing process, manufacturers are leveraging AI-powered analytics to improve product quality and efficiency.
In this article, we’ll discuss 5 innovative applications of artificial intelligence in manufacturing through their main applications.
Here we go
1. Inventory Management
Inventory management is the lifeblood of any manufacturing business. Staying on top of your inventory and managing it efficiently ensures you meet shifting customer demands.
However, inventory management is a daunting process that is labor-intensive due to the various processes involved, such as picking, packing, and shipping. When there is inconsistent tracking due to manual processes, it becomes redundant, time-consuming, and error-prone.
Overstocking your warehouse brings in storage challenges that increase costs and the risk of theft or damage. The lack of a particular item, on the other hand, due to understocking causes delays in your production.
Artificial intelligence highlights interesting trends from data to assist warehouse and procurement teams to better manage inventory tasks. It has the capacity to understand real-time inventory control dynamics that impact stock levels.
Through reinforcement learning and human oversight, artificial intelligence technology can recommend actions and make useful predictions. It enriches and standardizes data, ultimately providing a foundation for AI analytics to make data-driven recommendations that you can choose to reject, revise, or accept.
When you leverage applications of AI in manufacturing you can automatically order the required amount of raw materials through optimisation of demand prediction in inventory control. By merging datasets, and using these predictions you are able to make well-informed decisions, increase profits, and reduce waste.
A popular toy company, SynergyLabs, was experiencing hurdles in maintaining rampant workflows between departments to meet distribution demands. They leveraged AI cognitive applications to increase efficiency.
This enabled the client to improve consistency and enhance speed in administering tasks.
2. Generative Design
Manufacturers are coming up with new products daily to meet customer needs and gain an edge in the industry.
One of the challenges you could be facing as a manufacturer is coming up with new product designs that stand out. Limited choices and lack of creativity can leave you lagging behind resulting in feelings of frustration and being stuck.
In addition to that, if you’re developing a product, let’s say for the construction of a hotel lobby, last-minute additions due to lack of creativity can cause undue pressure.
Generative design software is one of the applications of AI in manufacturing that is changing how we design products.
To stay on top of these industry trends, you might want to sign up for the best online tutorials for learning AI. Manufacturing processes continue to embrace automation by the day, and project managers need to follow suit to stay afloat.
A notable benefit of generative design is that it allows you to simultaneously explore, validate, and compare thousands of design options. The software displays and compares the options in a way that enables you to choose a design that meets your product’s needs.
However, before homing in on the best design solutions the AI-powered software gives you insight on useful data regarding particular restrictions and parameters like budget limitations, time constraints, and material types.
When you leverage AI-powered software, you can dramatically accelerate design timelines and shorten research for new products.
One of the examples of uses of artificial intelligence in manufacturing is generative design by Claudius Peters. Using software analysis in their first generative design test, they were able to create a 25% lighter cast metal.
Finite Element Method analysis showed that the new part would be stronger, simpler, and more cost-efficient.
3. Predictive Maintenance
Maintenance procedures in a manufacturing plant are part of the daily routine but the work comes with its own challenges.
Given the breadth of equipment and machinery that require intervention and machinery by technicians, it’s manufacturers experience some of the highest errors.
Unplanned downtime due to machine failure presents a major expense, critically impacting the bottom line of an asset-reliant operation. It affects the entire supply chain function due to the inability to predict such a problem.
On experiencing machine failure, active processes upstream experience bottlenecks while downstream processes remain idle.
Predictive maintenance has become essential because it enables you to predict machine failure on time. This is made possible by advanced AI algorithms in the form of artificial neural networks and machine learning that formulate asset malfunction predictions.
With the adoption of industry 4.0, your facility gains access to complete data on operating parameters that helps engage in predictive maintenance.
Unlike preventive maintenance, predictive maintenance eliminates guesswork by programming your machines to constantly account their status onan up-to-date basis. Manufacturing systems are embedded with modern analytics tools, detectors, and digital twins to implement predictive maintenance.
Due to the drastic reductions in costly unplanned downtimes, predictive maintenance saves you labour costs and time, while ensuring optimal performance.
Wind turbine owners have little insight into the condition of their machines. They also lack visibility on how their machines are performing.
P4A and IBM harnessed analytics and IoT solutions to build a minimum viable product that would monitor the turbines and provide real-time alerts to technicians.
4. Quality Assurance
Customers are increasingly becoming quality conscious and expect faultless products nowadays.
Because of the rise in the complexity of products and very short periods of marketing deadlines, manufacturers are finding it challenging to adhere to quality standards and regulations in maintaining high quality levels.
According to LNS Research poor metrics is cited as the primary roadblock to achieving quality success by 37% of manufacturers.
Product recalls or high defect rates can ruin your reputation.
You end up wasting a lot of resources due to high product returns from customers and disposing off byproducts of defective products. These resources would have otherwise been used in other lucrative projects.
Quality 4.0 is among the innovative applications of artificial intelligence in manufacturing that uses AI algorithms to notify you when there is an emerging production fault that is likely to cause production quality challenges.
By leveraging new tools like blockchain and social listening, Quality 4.0 gains insights on factors like greater visibility of parts and customer satisfaction.
Data-driven, autonomous, and interconnected assembly lines work based on a set of algorithms and parameters that provide guidelines to the production of the best possible products.
Using machine vision technology, AI systems detect differences in the output and trigger an alert for you to make adjustments.
A specialty plastic manufacturer needed to improve visibility in their manufacturing processes. On incorporating the Ople Platform for the optimization and automation of data science processes, they were able to gather data and understand how it drives insights into the quality of the plastic sheeting being manufactured.
5. Collaborative Robots
One of the main challenges manufacturers face is the need to simultaneously produce many product variations at different plants for global distribution. In addition to that, there are more variations and complexities in current products.
There’s always a safety risk when you’re working in a manufacturing environment. You may need to handle dangerous tasks that require you to be in aseptic environments like life science facilities which require sterile conditions.
Another notable concern for the majority of manufacturers is talent shortage.
Robots are an ideal choice in manufacturing; they are more agile, smaller, and durable. In case you’re sold on process automation and need help choosing a robot, you’ll find this article helpful.
Manufacturing and robotics are a natural partnership. According to the International Federation of robotics, robot density is rising globally with an average of 74 robots per 10,000 employees.
Fully autonomous robots fulfill numerous manufacturing roles like automating high-volume repetitive roles.
They are able to work in high-risk environments, and their ability to reduce margins of errors due to their speed, accuracy, and durability offers unparalleled advantages.
Advanced robotics are AI-powered with increased abilities to take on human characteristics, such as sensing, memory, and trainability, making them more useful in the manufacturing world.
With the adoption of these AI initiatives you can close the skills gap, reduce operating costs, keep inventory lean, and increase efficiency leading to higher ROI.
Ford purchased a collaborative robot from Mobile Industrial Robots to automate their internal logistics processes.
The robot was equipped with an automated shelving system that enabled it to transport spare parts for production from the warehouse to production lines. It was also able to survive in the hostile environment.
Conclusion
It is without doubt that artificial intelligence plays a crucial role in building better manufacturing processes.
According to research by Forbes Insight, 49% of the respondents find AI highly essential to the success of manufacturing.
Now that we have gone through innovative applications of artificial intelligence in manufacturing, such as generative design and collaborative robots, I hope you can see how critical they are for cost effective manufacturing in 2021.
Are you ready to experience the powerful uses of artificial intelligence in your manufacturing?
I would recommend predictive maintenance if you’re just getting started. This way, you can REDUCE unplanned downtime due to unexpected machine failures.