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INSIGHTS

Smart manufacturing technology and how it benefits factories

Smart manufacturing technology and how it benefits factories
Smart manufacturing technology is gaining rapid market acceptance due to its various benefits, including increased productivity and reduced cost.
Smart manufacturing technology is gaining rapid market acceptance due to its various benefits, including increased productivity and reduced cost.
 
That was the point raised by Smart Plants, which offers a cloud-based smart factory solution. “It provides real-time insight to different manufacturing sites with detailed information on the past and current production details and analytics, like predictive maintenance and tool usage. The solution is integrated with most if not all ERP and third party systems with API,” said Stig Olsen, Sales Director at Smart Plants.
 
He made the remarks amid the popularity of the Industrial Internet of Things or IIoT, which leverages connected devices and the data they generate. According to Smart Plants, benefits of IIoT are manifold, including cost savings achieved through the following ways:
 

Lower energy costs

 
According to the company, the largest savings made by digital disruption in a factory setup would be energy costs. The biggest advantage of having a visual monitor of the entire manufacturing process down to the micro-level means that the operator can take quick and effective actions to make their factory leaner and leaner, Smart Plants said.
 

Lower inventory holding costs

 
Once all inventory is part of the connected network, the smart factory application can help the user visualize, simplify and streamline even the most resource-intensive process, the company said.
 

Lower quality costs

 
Since smart factory works in real time, web app users have a powerful capability to view live operation details and save manual time costs, and the users can even “rewind” to a certain point in any connected machine’s performance history, the company said.
 

Lower downtime losses

 
According to the company, one hour of downtime can cost from US$100,000 to $1 million depending on the size of the factory and how critical the bottleneck was. “One of our customers’ results proved that one hour less downtime of a bottleneck machine is equivalent to one hour less downtime for the whole production. They had implied the use of easy-to-understand dashboards displaying machines’ status in our solution had helped mitigate their downtime,” Smart Plants said.
 

Lower maintenance cost and maximum asset utilization

 
A major advantage of immediate alerts by the digital system is that bottlenecks are identified instantly, meaning corrective action can be taken more quickly, and the costly rip-and-replace can be avoided, the company said.
 

Reduced Labor Costs

 
According to the company, digital disruption allows the user to allocate only the required number of personnel for given amounts of time, and redistribute personnel over different jobs. This will not only reduce overtime expenses for the user but also help save a huge chunk of extra labor costs, it said.
 
Smart Plants’ solution is made up of a gateway connecting various units to its cloud bases-solution, enhanced with Thales military-grade end-to-end encryption. “The system is flexible and non-proprietary, communicating with all types of machines, from 100-year-old machines to modern CNC machines. We can combine human centric interface and machine centric interface and we interface with third-party systems like ERP, MPS and tool databases,” Olsen said. “We have contributed to a 7.5 percent production increase in just three months.”
 
According to Olsen, Smart Plants’ solution is suitable for different types of manufacturing settings. “It’s ideal for machine shops as well as manufacturing facilities with a variety of machines, new and old, that can benefit from our human-centric and machine-centric way of collecting data for production optimization,” Olsen said, adding the solution is combined with AI and deep learning as well.
 
“This is done by comparing production data from machine A with machine B, analyzing key metrics for production optimization and predicting maintenance, purchase, logistics and more,” he said.


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