Rishi Nayak

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Successful Role of Big Data Analytics to Drive Industry 4.0

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Successful Role of Big Data Analytics to Drive Industry 4.0

 

 

Introduction

 

The I4.0 has enhanced the industries with smart autonomous systems. These systems are essentially driven by a combination of data, AI, machine learning, analytics, and competitive technologies and of course an effective network/communication system. Data from PLCs, smart sensors and actuators among other field and enterprise applications are transferred and processed into vital insights and knowledge. This enables business enterprises and manufacturers to optimize processes and ensure higher productivity. 

 

Many operational and process industries have therefore leaped in to unlock the various possibilities of data as a catalyst to grow and improve their business efficiency. Manufacturing industries like OEMs and power utilities are slowly unlocking the data benefits and adapting them for a smooth and efficient work process. 

 

Role of Big Data Analytics in Industry 4.0

 

Undoubtedly, data is essential in creating a seamless network of machines and factory/process plants. Continuous digital data flow is required at all levels from field devices to ERP resource applications. IIoT is the face of I4.0 which includes a gamut of applications ranging from predictive maintenance, biometric identification for security, interconnected devices, smart factory, interconnected job site, asset tracking, and much more.

Various industrial communications standards like OPC lay down ground rules for IIoT, and other new-age technologies like automated robots, augmented reality which has paved the way for innovative services in an industrial ecosystem. All of these require data to process and execute. 

 

However, these data can be useful only when they are analyzed and sorted to meet the industrial requirements. This is where big data analytics becomes the forerunner of the industrial automation world. Big data analytics includes predictive analytics, prescriptive analytics, descriptive analytics, and predictive data analytics.

 

Big data analytics involves examining big data to understand certain patterns and other underlying insights attached to them. In industry 4.0, where accurate communication between devices remains top priority, data plays a key role. In short, data fuels the digitalization in I4.0.

 

Benefits and impact of data analytics in Industry 4.0

 

The data is collected from smart field devices via various mechanisms. It helps the stakeholders to understand patterns that can be used to improve their overall operational efficiency. Big data analytics aid in discovering the causes that lead to bottlenecks throughout the process and get insights into the source of a problem. It also helps in the obsolescence and hardware management using predictive analytics. By analyzing the data, manufacturers can understand the consumer trend to develop high-quality output. 

 

Manufacturers and businesses use these findings that help them in creating better strategies to reduce the operational and production cost and eliminate waste to a great extent. Big data analytics can help manufacturers and industry owners reduce the breakdown and unscheduled downtime by a significant percentage. 

How Data is Used in Industry 4.0

From interconnected IIoT technology to cloud computing to sensors to robotics everything is derived from data. These data are actionable data that can be used for data-driven intelligence and analytics that use artificial intelligence for a smart automated industrial workspace.

 

Devices such as sensors, actuators, translators, event generators, loggers, etc. interconnected in the real IIoT world are industry-specific components that continuously leverage and transform data to be able to work at par with the digital world. These devices facilitate components of the IIoT landscape through ICS capability of machine to machine, human to machine, and vice versa communication capability. 

 

From creating process performance strategies to prototyping and maintenance and production, these OT data play a crucial role in Industry4.0. Data is used by OEMs, discrete and process and power utility industries among others to leverage benefits like:

1. Efficient Predictive maintenance: 

Predictive maintenance is crucial in predicting any faults before they cause serious damages. With proper actionable data in hand, industries can create better predictive strategies and implement better fault management systems.

2. Automated Production Management

Industries use data from field devices like sensors to communicate with and operate PLCs and other production hardware. Automated production management greatly depends on the availability of these systems. 

3. Better Security

Security is one of the most crucial aspects of IIoT. Data holds a crucial place for all businesses irrespective of their verticals. It helps create a secure layer around the IIoT network through methods like data encryption, user access, authentication, authorization, user management, and much more.  Through effective incorporation of data security measures, business can control, monitor and manage their OT data.

 

Conclusion

 

That data is the driving force in the current automated and smart version of the industrial revolution is an undeniable fact. Therefore, if you want to stride ahead in this digitally smart era of I4.0 and digital transformation, you must be ready with the right tools and guidance to utilize the data in an optimum manner. 


Utthunga’s services consolidate the data-driven technologies to make your industrial and operational processes at par with the industry trends. We have a prolific history of serving industries and OEMs in discrete, process, power and building automation. Get in touch with our expert panel if you belong to these industries and are looking around for the perfect partner to catapult your business to achieve digital transformation. 

 

Successful Role of Big Data Analytics to Drive Industry 4.0

 

 

Introduction

 

The I4.0 has enhanced the industries with smart autonomous systems. These systems are essentially driven by a combination of data, AI, machine learning, analytics, and competitive technologies and of course an effective network/communication system. Data from PLCs, smart sensors and actuators among other field and enterprise applications are transferred and processed into vital insights and knowledge. This enables business enterprises and manufacturers to optimize processes and ensure higher productivity. 

 

Many operational and process industries have therefore leaped in to unlock the various possibilities of data as a catalyst to grow and improve their business efficiency. Manufacturing industries like OEMs and power utilities are slowly unlocking the data benefits and adapting them for a smooth and efficient work process. 

 

Role of Big Data Analytics in Industry 4.0

 

Undoubtedly, data is essential in creating a seamless network of machines and factory/process plants. Continuous digital data flow is required at all levels from field devices to ERP resource applications. IIoT is the face of I4.0 which includes a gamut of applications ranging from predictive maintenance, biometric identification for security, interconnected devices, smart factory, interconnected job site, asset tracking, and much more.

Various industrial communications standards like OPC lay down ground rules for IIoT, and other new-age technologies like automated robots, augmented reality which has paved the way for innovative services in an industrial ecosystem. All of these require data to process and execute. 

 

However, these data can be useful only when they are analyzed and sorted to meet the industrial requirements. This is where big data analytics becomes the forerunner of the industrial automation world. Big data analytics includes predictive analytics, prescriptive analytics, descriptive analytics, and predictive data analytics.

 

Big data analytics involves examining big data to understand certain patterns and other underlying insights attached to them. In industry 4.0, where accurate communication between devices remains top priority, data plays a key role. In short, data fuels the digitalization in I4.0.

 

Benefits and impact of data analytics in Industry 4.0

 

The data is collected from smart field devices via various mechanisms. It helps the stakeholders to understand patterns that can be used to improve their overall operational efficiency. Big data analytics aid in discovering the causes that lead to bottlenecks throughout the process and get insights into the source of a problem. It also helps in the obsolescence and hardware management using predictive analytics. By analyzing the data, manufacturers can understand the consumer trend to develop high-quality output. 

 

Manufacturers and businesses use these findings that help them in creating better strategies to reduce the operational and production cost and eliminate waste to a great extent. Big data analytics can help manufacturers and industry owners reduce the breakdown and unscheduled downtime by a significant percentage. 

How Data is Used in Industry 4.0

From interconnected IIoT technology to cloud computing to sensors to robotics everything is derived from data. These data are actionable data that can be used for data-driven intelligence and analytics that use artificial intelligence for a smart automated industrial workspace.

 

Devices such as sensors, actuators, translators, event generators, loggers, etc. interconnected in the real IIoT world are industry-specific components that continuously leverage and transform data to be able to work at par with the digital world. These devices facilitate components of the IIoT landscape through ICS capability of machine to machine, human to machine, and vice versa communication capability. 

 

From creating process performance strategies to prototyping and maintenance and production, these OT data play a crucial role in Industry4.0. Data is used by OEMs, discrete and process and power utility industries among others to leverage benefits like:

1. Efficient Predictive maintenance: 

Predictive maintenance is crucial in predicting any faults before they cause serious damages. With proper actionable data in hand, industries can create better predictive strategies and implement better fault management systems.

2. Automated Production Management

Industries use data from field devices like sensors to communicate with and operate PLCs and other production hardware. Automated production management greatly depends on the availability of these systems. 

3. Better Security

Security is one of the most crucial aspects of IIoT. Data holds a crucial place for all businesses irrespective of their verticals. It helps create a secure layer around the IIoT network through methods like data encryption, user access, authentication, authorization, user management, and much more.  Through effective incorporation of data security measures, business can control, monitor and manage their OT data.

 

Conclusion

 

That data is the driving force in the current automated and smart version of the industrial revolution is an undeniable fact. Therefore, if you want to stride ahead in this digitally smart era of I4.0 and digital transformation, you must be ready with the right tools and guidance to utilize the data in an optimum manner. 


Utthunga’s services consolidate the data-driven technologies to make your industrial and operational processes at par with the industry trends. We have a prolific history of serving industries and OEMs in discrete, process, power and building automation. Get in touch with our expert panel if you belong to these industries and are looking around for the perfect partner to catapult your business to achieve digital transformation. 

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