Cloud Computing 101: Industry 4.0
Industry 4.0 refers to a new phase in the Industrial Revolution that focuses heavily on interconnectivity, automation, machine learning, and real-time data. Industry 4.0, also sometimes referred to as IIoT or smart manufacturing, marries physical production and operations with smart digital technology, machine learning, and big data to create a more holistic and better connected ecosystem for companies that focus on manufacturing and supply chain management.
There are four design principles in Industry 4.0. These principles support companies in identifying and implementing Industry 4.0 scenarios.
Interconnection: The ability of machines, devices, sensors, and people to connect and communicate with each other via the Internet of Things (IoT) or the Internet of People (IoP).
Information transparency: The transparency afforded by Industry 4.0 technology provides operators with vast amounts of useful information needed to make appropriate decisions. Interconnectivity allows operators to collect immense amounts of data and information from all points in the manufacturing process, thus aiding functionality and identifying key areas that can benefit from innovation and improvement.
Technical assistance: First, the ability of assistance systems to support humans by aggregating and visualizing information comprehensively for making informed decisions and solving urgent problems on short notice. Second, the ability of cyber physical systems to physically support humans by conducting a range of tasks that are unpleasant, too exhausting, or unsafe for their human co-workers.
Decentralized decisions: The ability of cyber physical systems to make decisions on their own and to perform their tasks as autonomously as possible. Only in the case of exceptions, interferences, or conflicting goals, are tasks delegated to a higher level.
Smart Manufacturing: Powerful Production Smart manufacturing aims to increase throughput, improve quality, and maximize the utilization of equipment. Three productivity factors play the key roles:
Automation: Production steps and processes are automated as far as possible. Advanced robotics can make a big contribution here.
Operational data: With automation, the number of sensors in the machines, equipment, and logistics vehicles also increases. If units are connected, the data from all the sensors can be merged and made accessible to the entire network.
Advanced analytics: This comprises analysis methods that draw on AI and machine learning to glean deep insights from a company’s data.
In smart manufacturing, machinery, plants, and tools connect through a platform, enabling them to communicate with each other. The platform software can, for example, analyze the data collected and therefore predict malfunctions before they happen or adapt processes to changed production conditions.
If likely machine downtimes can be detected at an early stage, companies can dispense with regular maintenance slots and switch to predictive maintenance. Necessary maintenance can be scheduled in good time, while unnecessary maintenance can be avoided.
Smart tools increase quality and reduce the error rate. For example, smart cordless screwdrivers can autonomously adjust their torque to their current position within the manufacturing process, and thus adapt to the manufacturing context.
The goal is to boost productivity along the whole value chain by using prepared data to make the system transparent. The data is available to the right parties at the right time in the context of the relevant process and product, so employees can take the best action possible.
SAP and Industry 4.0
Digital, intelligent products: Companies can now develop intelligent, connected, and self-aware products capable of sharing information about their health; location; wear and tear; usage level, type, and timing; storage conditions; and more.
Emerging technologies: New technologies, such as cognitive computing, 3D printing, augmented reality, blockchain, machine learning, artificial intelligence, voice-controlled user interfaces, and robotics, are reaching a tipping point in terms of readiness and availability. These technologies are making it possible to translate futuristic digital business concepts into business realities for the very first time. Imagine using augmented reality on the factory floor to get job information, access raw material information, process instruction sheets, and even do physical inventory by detecting where workers look.
Shifts in customer attitudes, behaviors, and expectations: From wearables, connected cars, and networked running shoes to hyperconnected industrial products, the Internet of Things is positioned to give companies and organizations even more opportunities to collect data and use it to their advantage. Customers are clearly changing their attitudes about sharing previously “private” information with trusted companies they do business with – especially if they get something valuable in return – and this is enabling a heightened level of customer empathy that can transform product value chains.
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