The 4th Industrial Revolution, or Industry 4.0, is a stage in the evolution of humanity’s production processes. There have been three previous Industrial Revolutions, which first occurred in the 18th century in Britain with mechanization. The Second Industrial Revolution occurred in the early twentieth century with improved manufacturing methods and assembly lines. With the introduction of digital technology in the 1960s, the Third Industrial Revolution began. As computers became more powerful and the internet became more interconnected than ever before, Industry 4.0 began to take shape in the 2010s.
Industry 4.0 has multiple pillars like Big Data, Autonomous Robots, Augmented Reality, Cybersecurity but the most common ones are AI, IoT & Cloud.
With Industry 4.0, IoT sensors are used along the AI to elevate predictive maintenance with continuous machine health monitoring. In addition, customized dashboards are helpful for remotely monitoring the machinery by an expert with minimum human interaction.
Globally distributed industries can be compared by sitting in one location in terms of production, maintenance downtime & cost of operations.
The data from the manufacturing facilities are also valuable in increasing production. These data can be gathered through sensors that redirect to IoT gateways’ analyzing tools.
There is a wide range of intelligent sensors. Yet, the most ordinarily utilized ones are level sensors, electric flow sensors, stickiness sensors, pressure sensors, temperature sensors, closeness sensors, heat sensors, stream sensors, liquid speed sensors, and infrared sensors.
Intelligent sensors are inseparable from Industry 4.0. There is a vital part of Industry 4.0 environments that incorporate the modern web of things (IIoT) and distributed computing stages first. Indeed, they are now and again likewise alluded to as IIoT sensors.
A basis for industry 4.0 is as follows: IoT Sensors continuously collect data from the machines store the data either on Cloud or on-premises storage. Further use the data to create dashboards for visual monitoring by domain expertise or predictive analytics.
2. Industry 4.0 With AWS (Amazon Web Services)
Industrial Sensors & SCADA/HDA (Supervisory control and data acquisition/Historical Data Access) collects the industrial data for monitoring purposes.
AWS IoT Greengrass, an extension of AWS IoT core, provides local commuting to the edge devices & helps to take decisions when it’s not connected to the Cloud.
AWS IoT core receives data from either Greengrass or the partner edge devices directly. So which data can we store in logs to S3 for future reference and further use with other AWS services.
AWS Machine learning services such as SageMaker can be used to train the model for Predictive Maintenance.
AWS Analytics Services like QuickSightcan be used to generate dashboards & publish reports on collected data.
Most important for any industry is to get alerts on time-related to safety or production; AWS SNS (Simple Notification Service) enables it through email or SMS services.
3. Industries Using AWS as industry 4.0 Partner
A case study shows how automobile giants collaborate with AWS to improve efficiency.
“Volkswagen Group uses AWS IoT, ML, and edge services to power its Industrial Cloud, connecting data from over 120 manufacturing plants to improve efficiency and uptime, production flexibility, and vehicle quality.”
Another case study emphasizes how Georgia-Pacific Optimizes Processes, Saves Millions of Dollars Yearly Using AWS.
“We are using AWS data-analysis technologies to predict … precisely how fast converting lines should run to avoid tearing. By reducing paper tears, we have increased profits by millions of dollars for one production line.”
As data from industries are expanding with the industries, we can run the setup without worrying about infrastructure management with Cloud, it provides a scalable foundation for deployment & management of resources.
Organization & management of real-time data becomes easy with Amazon, predictive maintenance & visualization over this structured data helps in Root Cause Analysis in case of production line face issue or goes fail, which helps to reduce overall Maintenance cost & downtime.
Business tools can be integrated with AWS which helps in maintaining inventory, product tracking & other business operations from a single platform.
Organizations will want to use the benefits of industry 4.0 to establish a competitive advantage, boost revenue and profits, as well as provide better customer experiences in need to survive in the competitive climate.
Due to the current quantity of solutions, cloud services such as AWS, Azure, GCP, and others will aid in the implementation of Industry 4.0 which leads to boost revenue.
6. About CloudThat
CloudThatprovides end-to-end support with all the AWS services. As a pioneer in the Cloud Computing consulting realm, we are AWS (Amazon Web Services) Advanced Consulting Partner, and Training partner. We are on a mission to build a robust cloud computing ecosystem by disseminating knowledge on technological intricacies within the cloud space. Read more about CloudThat’s Consulting and Expert Advisory.
7. Frequently Asked Questions
What is AWS IoT core?
AWS IoT Core is a managed cloud service that enables many connected devices to interact with the cloud applications and other devices without any hassles. It has the ability to support billions of devices and trillions of messages. Moreover, it has the ability to securely and reliably route messages to AWS endpoints.
What is AWS IoT Analytics?
AWS IoT Analytics is a fully managed service that helps you to analyze data from millions of IoT devices so that building fast and responsive IoT applications is feasible without any need to manage hardware or infrastructure.
What is IoT SiteWise?
Amazon IoT Sitewise is a managed service that enables you to collect, store, organize, and monitor data from industrial equipment with ease and further assists you to make precise data-driven decisions.
WRITTEN BY Shubham Dubey
Shubham Dubey works as a Sr. Research Associate at CloudThat. He has 3+ years of experience in AI/ML. He is highly passionate about learning new skills and technologies.