CIOInsider India Magazine
Top 5 Industry 4.0 Trends In Manufacturing
Manish has been associated with Aditys Birla group for over two years now, prior to which he has handled key roles across companies such as Maahi Milk Producer Company, KSP Solution, Magna Seating, PMC Projects and Adani Group to name a few.
There are new possibilities offered by big data, 3D printing, machine learning and augmented reality in the manufacturing industry. Leveraging on these into a new way of doing business is a key factor in Industry 4.0 to gain a competitive edge, and for companies to be more profitable and scalable.
The global Industrial Internet of Things(IIoT) market is expected to reach $195 billion by 2022, growing from $113 billion in 2015, at a compound annual growth rate of 7.89 percent between 2016 and 2022, according to a market research report by Markets and Markets. A key factor for the growth of the IIoT market is the need to implement predictive maintenance techniques in industrial equipment to monitor their health and avoid unscheduled down times in the production cycle.
"manufacturing companies, merging data into one large pool has the capability to create insights that could not be obtained from data relating to just a single operation"
Five Industry 4.0 trends will be discussed in this article from big data, predictive maintenance, augmented reality, digital twin to cybersecurity. Industry players should be aware of these trends, as they have already begun to affect many aspects of industrial automation going forward. Industry 4.0 is no longer the future of the industry, and the time is now for companies to implement intelligent manufacturing practices.
1.Data/Data Analytics
Big data describes the large volume of data, both structured and unstructured. Insights from big data can enable better decisions to be made deepening customer engagement optimising operations, preventing threats & fraud and capitalizing on new sources of revenue.
A majority of the newly created data between now and 2020 will not be produced by people, but by machines, as they communicate with each other over data networks. The insights gained from big data analytics and the IIoT to drive greater manufacturing intelligence and operations performance is considered essential by 68 percent of Manufacturers, according to a recent survey by Honeywell. This highlights that manufacturers are increasingly aware of the importance of big data analytics and its potential in the industry.
The rate at which data is being generated is rapidly outpacing the ability to analyze it, according to Dr. Patrick Wolfe a data scientist at the University College of London. It is key to turn these massive data streams from being a liability into a strength. The complex nature of the information created requires solutions capable of addressing data security, privacy and flexibility issues.
Data Pooling: Data pooling refers to the integration and sharing of data. For manufacturing companies, merging data into one large pool has the capability to create insights that could not be obtained from data relating to just a single operation.
2:Predictive Maintenance
When repairs and maintenance are planned, it could save manufacturing companies 12 percent in cost savings, whereas a loss as much as 30 percent could be incurred when unplanned repairs occur.
Real time Condition Monitoring: Machine and sensor data can be catalogued and displayed in real time using Industry 4.0 software, which provides support for condition monitoring. Data
visualisation is not confined to the control station and can be accessed on any platform everywhere from tablets, smartphones and bigger screens, both on the production floor and in the cloud.
3: Augmented Reality
Augmented reality(AR) is an enhancement of a real time display using real images alongside computer generated information. AR is associated with Industry 4.0 practices relating to smart manufacturing and has tremendous potential to influence manufacturing industries. With augmented reality, challenges which arise with conventional 3D measurement can be eliminated.
Sensitive data is not limited to sensor and process information it also includes a company's intellectual property or even data related to privacy regulations
Potential Usage Scenarios: AR has numerous uses, involving different types of operations that can be executed on the factory floor manufacturing activities such as production, and support processes such as maintenance and training.
•Operations: Any kind of operation which requires some step by step procedure can benefit from the adoption of AR installation, assembly and machinery tool change.
•Maintenance & Remote Assistance: AR is efficient at reducing execution times, minimizing human errors and sending the relevant performance analytics to maintenance staff.
•Safety Management: AR allows risk and safety of operators and equipment to be managed.
•Design & Visualisation: AR provides tools that improve design, prototyping and visualization in the design phase.
•Training:For companies where training is a critical process involving many field technicians, AR guided training can be effective at training staff, especially in the beginning where there is a learning curve.
•Quality Control:AR support in quality control processes enables staff to determine if products meet manufacturing standards.
4:Digital Twin
A digital twin is a virtual copy of the factory or product parts to enable companies in performing simulation, testing, and optimization in a virtual environment before dedicating actual resources. The primary benefit of the digital twin is to provide a comprehensive outlook of the project at any time in the entire span of a product lifecycle. Moreover, it allows collaboration across different departments, and even outside the organization.
There is a distinction between AR and VR; in that AR relates more to smart manufacturing as it more connected to the physical world. The digital twin approach is built on three foundations a physical product in real space, a virtual product in virtual space, and the connection of data and information that ties the virtual and real products together.
5: Cybersecurity
The integrated nature of Industry 4.0-driven operations means that cyber attacks can have devastating effects, evident in the unprecedented ‘WannaCrypt' global cyber attack in May this year. Cybersecurity strategies should be secure and fully integrated into organizational and information technology. Picking the right cybersecurity provider is essential in ensuring data is protected.
They could startout with putting less sensitive data on the cloud, understand how it works first, and then understand how cybersecurity providers can help them. From there, they can move towards a more balanced approach.
Protecting Data: Sensitive data is not limited to sensor and process information it also includes a company's intellectual property or even data related to privacy regulations. As more IoT devices are connected to networks the risk of potential attack increases.
The Right Strategy is Important: "Companies have to ask themselves why they want to create a fully automated manufacturing factory, and what value it creates for the end users. Once the staff in the company knows why this is being done, it will change the company's culture, and they will start focusing on value is delivered to the customers. At the end of the day, these are big investments, and companies have to plan strategies for the long term and be willing to change their company culture", said Scott Maguire, Global Engineering Director, Dyson.
