A digital twin works in the following manner:

• Smart components fitted with sensors are integrated with the physical object and these consolidate data about working conditions, real-time status, position, etc.
• These components are connected to a cloud that receives the information from the sensors and processes it.
• The information is then analysed against similar business data.
• The model learns continuously (Machine Learning) and updates itself from multiple sources, resulting in the acquisition of a specific level of intelligence.
• The lessons learnt are applied to the physical world to bring about a transformation in the business through improved efficiency.

Digital twins have become an integral part of industries for the following reasons:

• Operators can get in-depth information about their systems using digital twins. This includes assessing the levels of depreciation in machinery, analyzing traffic pattern in road networks for effective fleet management, and much more.
• In the event of a change in process, operators can first experiment with the digital twin before implementation on the real-world model.

Cobots can use Computer Vision to work with a basic level of intelligence.

• Collaborative robots can be reprogrammed easily and they can interact in a more natural way with humans, when compared to industrial robots of the yesteryears.
• They can analyse a large number of inventory items to check for deterioration.
• Cobots are also integrated with safety features, such as the ability to detect collision.
• Collaborative robots have Predictive Maintenance capabilities that help in avoiding hazards at the plant.
• They are equipped with specific features to take on strenuous or dangerous tasks that are often repetitive.
• This way, their human counterparts can work in a safer environment.
  
• Cobots have become more adaptable, compact and affordable due to the progressive innovations in robotics.
  

Although there are speculations that the introduction of cobots may result in loss of jobs for humans, statistics from numerous studies show otherwise. Robots equipped with artificial intelligence can only improve the safety and work conditions at factories. This may even result in additional job opportunities in the market.

• A high-speed internet connection provides field operators quicker access to information. It also ensures that IoT sensors and devices embedded in a smart factory are constantly connected to the network, ensuring operational efficiency.
• 5G is expected to have the potential to deliver download speed of up to 10 gigabytes/second. This makes it easier to transfer huge data packets quickly and leverage real-time AI decision-making.
• Low latency and high reliability are some of the other advantages offered by 5G technology.

Let us take a look at the benefits of edge computing in detail:

Scalability

When manufacturers look to expand their process of data collection and analysis, there is no longer a need to establish dedicated data centers if edge computing has been implemented. Through edge computing, data collection, storage and analytics capabilities are being increasingly bundled into applications/devices with smaller footprints. These can also be situated closer to end users. Hence, companies can utilize this facility to expand their reach and scale their computing needs in a more efficient manner.

Speed

An edge computing network reduces latency and hence, has the ability to improve network performance. In IoT-enabled edge computing devices, the data is processed locally (in data centers at close proximity). This implies that the collected information need not travel as much as it does in a conventional cloud network. The end result is that users get to enjoy higher speeds and latency is reduced to microseconds. Since speed is a definitive best practice that gives companies a competitive edge today, the prospect of using edge computing guarantees endless growth possibilities for organisations.

Security 

Edge computing offers significant security advantages to an organisation. A traditional cloud computing network architecture is centralized and is particularly vulnerable to power outages and distributed denial of service (DDoS) attacks. However, edge computing distributes the data storage and processing across multiple devices and data centers. This makes it difficult for a single interruption to bring down the entire network. In case there is a cyber-attack on an edge network, the distributed nature of the architecture makes it easier to establish security protocols that isolate compromised parts of the network.

• Quality assurance is a primary use case for AR in a manufacturing environment. Technicians in a plant can use AR overlays and compare pictures of vehicle parts under inspection with the images provided by suppliers. Whenever there is a discrepancy, the AR tool highlights the specific feature and allows the technician to identify the problem immediately.
  
  
• Augmented reality solutions are used for facial recognition to sign in employees to their IT systems. Studies have found that organisations that utilized this functionality have been able to considerably increase the efficiency of the personnel.
  
  
• AR and VR can also be used to get expert assistance during the inspection of a machine part. It is commonly found that manufacturers have distributed operations, and technicians are required to travel to a specific location to inspect a defective product.

The concept of mixed reality facilitates this process in an effective manner. The expert can wear the AR device and see what the on-site technician sees in real-time. He/she can then point out features that need to be examined further. This offers the potential for increased efficiency and savings for the manufacturer.

•  Another advantage of using augmented reality in industries is that it offers a solution to skills gap. The usage of AR devices can help in the provision of on-site training to a new technician and helps in supplementing his/her knowledge with expert assistance. This reduces the time taken to train each technician to a certain level.