Edge Vs Cloud in Manufacturing
Published on : Thursday 11-11-2021
Manufacturing information technology solutions are key drivers to success, says Darshana Thakkar.
In today’s digital environment, a humongous amount of information and data are available. These are processed and controlled to achieve various tasks in personal life as well as in the business world.
In the business world, every piece of information is critical for appropriate decision-making. Large amounts of scattered data across the organisation are combined in the computer system and computed to achieve the final result in terms of control and feedback.
The five basic operations that a computer performs are: input, storage, processing, output and control.
One or more computers and associated software with common storage make a computing system.
Types of computing environments
1. Personal Computing Environment
In the personal computing environment, there is a single computer system.
2. Time-Sharing Computing Environment
Here multiple users with different programs interact nearly simultaneously with the central processing unit (CPU) of a large-scale digital computer
3. Client-Server Computing Environment
This works with a system of request and response. The client sends a request to the server and the server responds with the desired information
4. Distributed Computing Environment
This consists of multiple software components that are on multiple computers but run as a single system. The computers that are in a distributed system can be physically close together and connected by a local network, or they can be geographically distant and connected by a wide area network.
5. Cloud Computing Environment
In a cloud environment a company, organisation, or individual uses a Web-based application for every task rather than installing software or storing data on a computer.
6. Cluster Computing Environment.
A group of interconnected computers or hosts that work together to support applications and middleware (e.g., databases). In a cluster, each computer is referred to as a “node”. Unlike grid computers, where each node performs a different task, computer clusters assign the same task to each node.
Cloud computing – The leading data processing trend in the current era
In the IT field in terms of the gross amount of data, we are going through a period of exponential rise in data to be processed. This has to lead to the entire cloud computing movement, to begin with since there was so much data that it couldn’t be all stored or analysed locally.
Using the internet all the data to be collected, and analysed remotely with more computing power and access than could get in a personal server is what created the cloud computing craze. This approach became so much more effective than the old way of doing things in a relatively short period.
Why Edge computing
Cloud computing seems powerful, successful, and widespread. But there are some limitations for certain applications with cloud computing.
Although cloud computing is preferred for more complex data processing scattered over different geographical areas and remote locations, in certain cases data processing near the source is demanding for a timely decision process.
At certain remote locations where there is no internet or limited internet connectivity, cloud computing is not effective. Apart from this for the instant where data processing is time-sensitive, cloud computing is not a suitable solution. The emerging trend of Edge computing is a complement to cloud computing.
Examples of Edge computing
a. Wearable on the wrist
b. Modern smartphone
c. Computers parsing intersection traffic flow
d. Smart utility grid analysis
e. Safety monitoring of oil rigs
f. Streaming video optimisation, and
g. Drone-enabled crop management.
Edge computing for manufacturing
Bringing industrial applications into the mix, industrial edge computing refers to the process of managing data-handling activities using individual sources of data such as smart edge devices. Thus, the adoption of Industry 4.0 business models in the manufacturing industry is accelerated with the help of edge computing technology.
Benefits of Edge computing in the manufacturing sector
Edge computing is essential for smart manufacturing success. Edge computing allows manufacturers to filter data to reduce the amount of data sent to a central server, either on-site or in a cloud. The ability to monitor the condition of their assets remotely helps manufacturers generate new revenue streams.
i. Lower latency.
ii. Increased cybersecurity.
iii. More manageable data analytics.
iv. Expanded interoperability.
v. Reduced storage costs.
vi. The future of Industry 4.0 depends on edge computing.
Areas where Edge computing is beneficial in manufacturing
The manufacturing industry is an early adopter of Edge computing. Edge computing has existed in the sector for several years: manufacturing plants have significant processing power on-premises. Examples are programmable logic controllers (PLCs), the machines themselves, or an on-premise data centre.
Due to global competition, the manufacturing business leader has to focus on core business. In such a scenario if Digital Transformation helps reduce plant processing costs and at the same time allows flexibility in operation that is a great opportunity for manufacturing business leaders. Edge computing fits into this wider context by allowing manufacturers to use more flexible, standard hardware and software to be able to access and share data relevant to their manufacturing processes.
1. Condition-based monitoring
Manufacturing plants have been built using many proprietary types of machinery and systems, which do not communicate with each other. Manufacturers are facing tremendous challenges to access data from their machines, system, and process. The operational technology is still quite traditional in many organisations. IT is modern with many organisations. So there is a need for IT/OT convergence. The data extracted from all the machines may overload the central server. In such cases, edge computing helps to filter data to reduce the volume of data to be sent to a central server.
2. Predictive maintenance
Predictive maintenance is being able to detect when a machine will fail through data analytics. Edge computing aids this data analytics near the sources and provides information about the potential failure in time. This way one can conduct maintenance in advance before potential breakdown. As a result risk and cost of breakdown and maintenance are reduced.
The major challenge in manufacturing is to achieve economies of scale. Automation and digital transformation of manufacturing are costly and the process is highly standardised. Moreover, due to continuously changing consumer demand, flexibility is also required in the process.
With the use of edge computing, manufacturing can be made more flexible and mobile by reducing the time it takes to set a site up as well as creating more sharing models where multiple parties can use the same facility. This way by creating shared facilities for multiple manufacturers will help to obtain economies of scale and at the same time can leverage the benefits of the technologies. Irrespective of the site location, edge computing processes the data. Further data processing is near the source only so it reduces concerns of the manufacturer regarding data security.
4. AR/VR in the manufacturing plant
Nowadays manufacturers are using augmented/mixed/virtual reality in the plant, for several purposes. Few examples:
a. For training to the employee on how to use new equipment or process
b. To guide employees about the hazardous environment
c. To assist repair and maintenance work with remote expertise, and
d. To detect a fault during a quality check of the product.
The VR headsets are having limitations in terms of processing the volume of data and the VR headsets are also heavy which sometimes create difficulty for the wearer.
In such a scenario, use of Edge computing is very effective. With edge computing, the headset becomes lighter which makes it user-friendly and also makes data processing from edge nodes faster.
5. Application in Industry 4.0 environment
The most important goal of Industry 4.0 is to use data from multiple machines, processes, and systems for real-time decision-making in the manufacturing process. This precision process of monitoring and control uses a huge amount of data and needs machine learning to determine the best action to be taken.
In such a scenario, cloud computing has limitations of huge data processing in real-time. While edge computing is most suited for collecting, aggregating, and filtering data that can be sent to the central server. So Edge computing helps the manufacturer to choose to distribute AI/ML processing across multiple edges.
Future of Edge computing
Edge computing allows manufacturers to make flexible choices about processing data to eliminate time lags and decrease bandwidth use, as well as to determine which data can be destroyed right after it is processed.
By 2025, edge computing is expected to be four times larger than cloud and generate 75 per cent of the world's data. This will require new, innovative approaches to software infrastructure – like a purpose-built operating system that seamlessly bridges from the data centre to the cloud to the edges.
Edge computing Vs Cloud computing
Both of these technologies are having strong demand in future also. But both of them have their specialised functionality.
Edge computing is a complement to cloud computing than its inevitable replacement. It can’t replace cloud computing because there’s likely going to continue to be a need for centralised processing.
Instead, edge computing is a response to cloud computing, to overcome some of its limitations. The effective way of using these technologies is to have cloud computing on the one hand, and then the processing power of an edge computing model.
Darshana Thakkar is MSME Transformation Specialist and Founder, Transformation – The Strategy Hub. An Electrical Engineer followed by MBA – Operations with rich industry experience, Darshana is an expert in transformation, cost reduction, and utilisation of resources. She has invested 25 years in transforming Micro and Small Enterprises. Her rich experience in resolving pain areas and real-life problems of SMEs helps organisations achieve quick results. Her expertise in managing business operations with limited resources helps clients transform their business practices from person driven to system driven with existing resources.
Darshana has helped many organisations to increase profitability and achieve sustainable growth. She is passionate to support the start-up ecosystem of our country. She is associated with CED, Government of Gujarat as a Business Function Expert in the Entrepreneurship Development program, as faculty for industrial subjects in the Second Generation Program (SGP), and as a start-up mentor and member of the start-up selection committee in the CED incubation centre.