Manufacturing industries today need to optimize their operating processes right from the supply chain, order management to designing, engineering, logistics, and services, in order to establish themselves in the existing global market.
The C- suite executives are continuously trying to innovate and find new stream revenue as unplanned downtime, labor costs, and other system issues affect the productivity and growth of the business.
With this in mind, manufacturers are investing in next-gen technologies like Artificial Intelligence. Fueled by massive amounts of data, manufacturing environments are connecting products, small factories, and supply chains in new ways. In fact, AI in the manufacturing industry is estimated to grow nearly by 50% to hit $17.2 billion by 2025.
Why is AI crucial for Manufacturing Industries?
Manufacturing industries are collecting enormous volumes of data at the stage of production and leveraging it to improve operational efficiency, productivity, and performance. It is helping them achieve significant cuts in downtime, generate better-designed products, and improving employee members’ safety.
As per Forbes Insights Survey on Artificial Intelligence, 44% of respondents from the automotive and manufacturing sectors classified AI as “highly important” to the manufacturing function in the next 5 years, while almost half, 49% said it was “absolutely critical to success.” The scope of AI application in manufacturing is remarkably vast and helps companies to adapt to the ever-changing market.
Here are some of the key benefits of AI in manufacturing.
AI facilitated predictive maintenance can easily tackle both planned and unplanned downtime. It leverages the key components i.e artificial neural network and machine learning to predict asset-related malfunctioning. The AI algorithms continuously monitor the production processes and flag any anomalies encountered, even at the microscopic level to take timely maintenance and repair actions.
As per a report by Mickensey, AI-driven predictive maintenance can increase asset productivity by up to 20 percent and reduce maintenance costs by up to 10 percent. It gives the technicians every minute update on production and offers insight into the component that needs inspection or where maintenance is necessary. It is also capable of suggesting solutions, suitable tools, and methodologies, that facilitate pre-scheduling maintenance tasks and repairs. This helps manufacturers to monitor the machine’s health without process interruptions and extend the remaining life of machines.
Quality of Production
Artificial intelligence is helping manufacturers achieve both better process and better product that meets the quality standards and regulations. It ensures that equipment operates properly with new-age technologies like machine learning and big data without any variabilities in material or manufacturing processes.
With the help of sensors connected to production machines, data such as temperature, relative humidity, and other environmental information could be tracked. This data is used to learn about any emerging faults that could affect product quality and hence alert the production team. It helps manufacturers attend a level of specialization that they could not achieve earlier along with downtime reduction and increased productivity.
Better Collaboration at work
Robots though well known to work on their fixed tasks in limited spaces, context-aware robots use computer vision and AI to operate alongside humans in a shared environment. They can avoid distractions and carry out their assigned tasks such as moving parts on manufacturing floors effortlessly.
As per Mckinsey, collaborative, and context-aware robots will improve production through labor-intensive settings, increasing productivity by 20% for certain tasks. As more and more job responsibilities are shared with robots, human workers can be trained for advanced positions in design, maintenance, and programming.
Organizations that understand the collaboration between humans and AI tools will enhance their organization’s effectiveness and create maximum value. Since the end of 2018, there are more than 1.3 million industrial robots at work in factories all over the world. Energy and resource-friendly production help industries to work economically and remain competitive.
Generative Design Process
With the help of AI, manufacturers can also improvise the way they design the products. Generative design is a process where designers can input details and parameters like material types, available production methods, budget limitations, and time constraints. The AI algorithm then processes the details and gets a list of possible configurations to recommend the most
optimum product options.
The most suitable solution can then be tested for actual performance considering different manufacturing conditions and scenarios. The main advantage of the generative design process is that it eliminates the human bias design options and suggests options that will meet the performance demands adequately.
Adaptable to an ever-changing market
The use of AI is not limited to the operational level of production only, it also plays a vital role in optimizing supply chains, recognizing patterns, and analysis of consumer behavior. This ensures that the company can anticipate market changes, develop strategies for it, automate analytics
production, and even cost management processes.
Moreover, with AI algorithms manufacturers can estimate market demands based on collected information such as political status, consumer behavior, can optimize staffing, control and monitor the supply of raw materials or inventory, and many other critical processes for the industry.
Optimizing Supply Change
The adoption of AI in supply chain management provides transparency and visibility on supply chain data and processes and enhances customer service. It gathers and analyzes real-time as well as historical data from multiple connected devices and systems to obtain broader and deeper insights. It helps the procurement team to foresee any challenges and help make better industry-related decisions. This reduces both the time as well as the overall cost of the warehousing and shipping.
AI tools and solutions can analyze huge databases and balance demand-supply gaps, plan and manage production, schedule factory activities, and avoid overproduction or shortage of products. Mckinsey estimates that AI-enhanced supply chain management helps companies to reduce forecasting error by 20 to 50 percent.
Future of AI in manufacturing
Well, the implementation of AI in the manufacturing industry is no more a thing of the future. AI has already made tremendous progress with the help of algorithms and a lot of data. AI-powered next-gen industries will create intelligent decision making in unexpected situations along with increased lean inventories, condensed production glitches, reduced labor costs, unplanned downtime, and better productivity.
AI in manufacturing will create a safer operational environment,network-connected factories, and novel job opportunities for humans. AI ahead will serve as the key element for digitally-driven industries.
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