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The Key Components of Industry 4.0 and Their Applications

Bhagyashri Kambale | February 11, 2022 | 1183 views
THE KEY COMPONENTS OF INDUSTRY 4.0
Industry 4.0 technologies, ranging from simulation to big data, have advanced significantly during the past few years. It is critical to gaining access to real-time outcomes and data that will propel the sector to new heights of lean success. Growing industry expertise and technological applications are making all cutting-edge technologies commercially available.

However, the notion of Industry 4.0 is not straightforward. It comprises a wide range of technologies and is applied across a variety of circumstances. This article will explore some of the key components of Industry 4.0 and their application scenarios. All of them are critical components for industry to work smoothly, accurately, and effortlessly. Each individual component plays a unique role in the overall efficacy of Industry 4.0 technologies.

Industry 4.0 Components


Big Data and Analytics & Use Case

Big data analytics is one of the core components of Industry 4.0. With big data analytics, businesses may identify important correlations, patterns, trends, and preferences to help them make better decisions. In Industry 4.0, big data analytics is used in smart factories to forecast when maintenance and repair procedures are required. Manufacturers benefit from increased production efficiency, real-time data analysis, predictive maintenance optimization, and production management automation.

“Data is the new science. Big data holds the answers.”

– Pat Gelsinger, CEO at VMware

The IoT and current production systems create a lot of data that must be acted upon. That's why big data organizes data and develops insights that help businesses enhance their operations.

Big Data Use Cases 

Optimizing Warehouse Operations: Businesses may increase operational efficiency by identifying human mistakes, running quality checks, and displaying ideal production or assembly routes using sensors and portable devices.

Eliminating Bottlenecks: Big data helps identify variables that may slow the operation’s performance and diagnose the issue at an early stage and eliminate bottlenecks.

Predicting Demand: More accurate and relevant forecasts are made possible by visualizing activities beyond historical data through internal analysis (consumer preferences) and external analysis (trends and external events). This enables the business to predict demand, adjust and optimize its product portfolio.

Proactive Upkeep: By recognizing breakdowns in patterns, data-fed sensors indicate potential problems in the operation of machinery before they become breakdowns. The system notifies the equipment in order for it to react appropriately. These are only a few of the applications of big data analysis in manufacturing systems; there are several others, including enhanced security, load optimization, supply chain meanagemnt, and non-conformity analysis. 

Industrial Internet of Things (IIoT) & Use Case

The next component in the industry 4.0 components list is IIoT. By virtue of its unique characteristics, the Industrial Internet of Things (IIoT) is creating massive changes in industrial applications. It greatly improves the operational efficiency and workflow of factories by monitoring assets and processes in real time. The IIoT presents several opportunities for entrepreneurs to improve their industry exponentially.

“The Internet of Things is the game-changer for an overall business ecosystem transformation.”

– Joerg Grafe, Senior Market Analyst, IBM

IIOT Use Cases

Predictive Maintenance: Maintenance schedules are established for machines and assets that run continually. Unplanned maintenance and failures often cost over $88 million a year. Predictive maintenance can help control these overhead costs.

Sensor and device data allows predictive analytics systems to swiftly analyze current conditions, identify danger indications, send alerts, and initiate maintenance activities. For example, a pumping station motor in an ideal IoT facility may schedule maintenance if it detects irregularities in sensor data. This method saves money on routine and frequent maintenance.

Asset Tracking: Asset tracking is designed to find and track valuable assets. Industries can track assets to improve logistics, maintain inventory, and identify inefficiencies or theft.

Real-time asset tracking is vital in manufacturing. It may be used in warehouse and inventory management to keep track of the goods. This helps in finding the lost or misplaced goods in the warehouse. Industries with scattered assets may use IoT to track, monitor, and control them.

Workplace analytics: More IIoT devices mean more workflow data for organizations. Data scientists can use analytics engines to find inefficiencies and offer improved operations. Location data analysis might also reveal warehouse inefficiencies.

Remote quality monitoring:  Sensors give faster and more cost-effective information about products or processes, leading to faster and more effective actions. Industry 4.0-enabled quality monitoring systems can also be obtained from the IIoT.

Manufacturing factories can utilize IoT devices to remotely check material or product quality. It increases efficiency by allowing staff to verify many processes quickly. Similarly, real-time alarms make it easier for people to respond quickly, which lowers the risk of a failed product if left unchecked.

Because remote quality monitoring is a novel concept, there aren't any ready-made solutions or services. Developing customized IoT technology to measure certain metrics can be costly and difficult.

Cyber security & Use Case

Industrial manufacturing has one of the highest data breach costs of any sector. The Ponemon Institute's 2019 Cost of a Data Breach Report estimates the average industrial breach at $5.2 million. In May 2017, the WannaCry ransomware assault crippled several manufacturing companies, forcing some to shut down plants for days. Overall losses were in the billions.

“Cyber-Security is much more than a matter of IT.”

― Stephane Nappo

Cyber security is vital for a safer digital zone on your factory floor or in your manufacturing business. It is one of the crucial 4.0 industry components. It's essential to be mindful of the weaknesses while modernizing manufacturing. The largest risk in an open factory environment with widely distributed partners and operations is an incident that disrupts operations. No manufacturing company, or any organization, for that matter, should pursue digital transformation without including cyber security in every step and decision.  

Cyber Security Use Cases
  • Analyzing network traffic to detect patterns indicative of a possible attack
  • Detect harmful activities or insider risks
  • Response to incidents and forensics
  • Manage the risk associated with third- and fourth-party vendors
  • Identify data intrusions and compromised accounts
  • Risk management, governance, and compliance
  • Threat hunting is a technique for identifying signs of attack

Additive Manufacturing & Use Case

Additive manufacturing is a set of manufacturing processes that create a final product by layering material. Additive manufacturing reduces production and supply chain costs by enabling the rapid creation of large quantities of parts. It eliminates stock and the requirement for molds. Initially, 3D printing was utilized for prototyping and is still the rule. However, 3D printing technology has advanced; it is now more inventive than ever before.

“3D printing is going to be way bigger than what the 3D printing companies are saying.”

– Credit Suisse

Additive Manufacturing Use Cases

Parts for New Products: Porsche is 3D printing aluminum pistons for the Porsche 911 G2 RS engine. The improved product was made feasible using generative design software, aluminum powder, and 3D printer improvements. General Atomics Aeronautical Systems has teamed up with GE Additive to print a NACA inlet. The component is made via laser powder bed fusion.

Parts for the Aftermarket: Aftermarket components are defined as non-OEM (original equipment manufacturer) replacement parts. Thyssenkrupp and Wilhelmsen Marine Products have teamed up to offer 3D printed replacement components. With aged ships, the maritime sector frequently needs hard-to-find, costly, and time-consuming spare components. 3D printing spare parts near to the source reduces lead times and shipping costs.

Jigs, Fixtures, Molds and Tools: Jigs, fixtures, molds, and tools are essential in manufacturing. When one of these fails, a plant's downtime is prolonged. Jabil, a manufacturing services firm, has adopted 3D printing. They no longer have to wait weeks for tools or components. They can now produce tooling, fixtures, and manufacturing aids in-house in days, speeding up new product launches and increasing customer satisfaction.

Simulation and Virtualization & Use Case

Simulation in manufacturing systems is the process of using software to create computer models of production systems for the purpose of analyzing them and obtaining valuable information. According to syndicated research, it is the second-most popular management discipline among industrial managers.

“Simulation is the situation created by any system of signs when it becomes sophisticated enough, autonomous enough, to abolish its own referent and to replace it with itself.”

- Jean Baudrillard

Simulator software lets businesses try out new technologies and principles in a risk-free, virtual setting so they can make sure they're making the right investments.

Simulation Use Cases

Interoperability: The simulation showed how downstream work stations may use extra location data to more efficiently choose and organize work batches to satisfy client demand.

Information Transparency: Using sensor data, we may construct a virtual replica of the physical world, such as a manufacturing plant or contact center. This technology allows an operator to visually evaluate and certify products.

Technical Assistance: Simulating the use of Automated Guided Vehicles (AGVs) to accelerate traditional production and manufacturing processes. Additionally to substitute physically hard jobs such as stock moving is becoming increasingly popular.

Due to simulation's ability to capture the process time variation, it is an effective tool for validating critical design parameters. For example, the number of AGVs to purchase, the overall benefits to throughput, maintenance planning, and track layout.

Decentralized Decisions: In a high-mix, high-volume production plant, a simulation is performed to examine the feasibility of increasing a palletizer's storage capacity in order to 'rack-up' a series of basic tasks for overnight processing while reserving more complex processes for staff hours.

The simulation lets you try out a large number of test scenarios, including worst-case scenarios in which the machine becomes stuck near the start of its overnight operation.

Final Word

Industry 4.0 is a solution bundle for manufacturers to improve their manufacturing, inventory, and supply chain management. The key components mentioned above are only a few from an extensive list. There are more industry 4.0 technologies to include in the list, including digital twins, cloud, virtualization, robots, augmented reality, artificial intelligence, and more. Many of these technologies are now accessible to make future forward smart factories a reality today. Know about the uses of each component and learn how to integrate it into your digital manufacturing.

FAQ


What is industry 4.0 also called?

Industry 4.0 is also known as IIoT or smart manufacturing. It combines physical manufacturing and operations with smart digital technologies such as machine learning, and big data to create a more holistic and linked environment for manufacturing and supply chain businesses.

Why is Industry 4.0 needed?

Industry 4.0 technologies help you control and optimize your production and supply chain operations. It provides real-time data and insights to help you make better business decisions, eventually increasing the productivity and profitability of your company.

What are the four core components of industry 4.0?

In an attempt to define Industry 4.0 concept, German researchers developed a list of industry-defining components. They are: cyber-physical systems, IoT, Internet of Things, and smart factories.

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