The Role Of IoT And Industry 4.0 in Creating Digital Factories of Tomorrow
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Like the previous three revolutions, Industry 4.0 aims to transform manufacturing using the latest technologies. This time around, it is the advanced information and communications tech that creates the Internet of Things. Industry 4.0 combines IoT with AI, ML, and robotics to digitize manufacturing.
IoT is a key part of the Industry 4.0 strategy which works to create flexible and connected digital factories where communication is facilitated between all parts of the system. The best aspect of today’s technologies like IoT, AI, and Big Data is their range of applications. These technologies work on the factory floor as well as in other areas such as planning and management. Manufacturers can even integrate customers and business partners in value and business processes.Â
'IIoT and Industry 4.0 are creating new opportunities and possibilities for the manufacturing industry. Digital factories are the future of manufacturing and early adopters will have an advantage.' -L2L
As you can see, Industrial IoT (IIoT) has great potential to improve manufacturing. Today, automation and robots are present in almost all factories. The use of AI is also speeding up which has improved automation and manufacturing efficiency. As manufacturers look for ways to embrace IoT and Industry 4.0, the smart manufacturing market is expected to be worth $228.2 billion by 2027. Here are some of the ways the industry is moving towards that goal.
Cyber-physical systems (CPS) are at the core of the smart factory that Industry 4.0 envisions. CPS are systems that use sensors and software in all parts of manufacturing. These parts may be machines, vehicles, routes, inventory, and the plant building itself. The sensors record and save the data which is then processed by the computer to make decisions. These decisions directly affect the physical system through actuators and human-machine interfaces (HMIs).
CPS improves upon the automated machines using Industrial IoT. Regular automated machines work in isolation using the software. In contrast, CPS collects and shares data from and with all the assets and areas of the plant. Cloud computing is used to analyze this data to make decisions that optimize the system. Businesses can also use AI and ML for smarter optimization based on previous results.
CPS and IoT complement each other to create smart factories. These competitive factories have reduced downtimes, improved efficiency, create better products, and account for higher productivity. Factories that implement IoT in manufacturing report cost reduction and an improvement in quality.
Maintenance is a major headache for any plant manager. The downtime and costs associated with it can prove expensive. The classic approach to regular maintenance is inefficient increases the risk of the breakage and wear of machine tools, which in turn increases costs.
Predictive maintenance systems use IoT to get real-time information about each in-service asset. Based on the information, the system predicts the time for asset maintenance. Manufacturing plants have interconnected systems where multiple factors are at work. Load, design, and process changes at one location can affect the entire plant. IoT-based predictive maintenance and cloud computing find great use in such cases. The system uses data from assets around the plant to predict maintenance requirements.Â
Smart maintenance management systems with IoT can use AI and ML as well. These can consider the effects of all the systems on manufacturing to make better accurate predictions with time.
Predictive maintenance gets special attention due to its effect on the bottom line. According to a McKinsey report, predictive maintenance can reduce cost by 10-40 percent and downtime by 50 percent. These improvements affect plant efficiency and even bring down indirect costs. The digital factories of tomorrow will find predictive maintenance inevitable in order to stay competitive.
During the Covid-19 pandemic, the manufacturing sector faced unprecedented conditions including social distancing requirements, worker shortages, and workforce size limits. All of these conditions greatly affected factories and warehouses.
Logistics is the lifeblood of any business. When logistics of multiple sectors faced challenges, the world suffered supply chain disruptions. These huge disruptions demanded a move towards smarter logistics management.
Factories can use IoT in many areas of logistics. Starting from inventory and material handling to internal transportation and shipping, IoT can help improve the accuracy and efficiency of logistics management. The primary way IoT helps in these areas is through real-time location and condition data of the assets. This helps with optimal use and inventory stocking, better asset tracking, and material handling systems, reducing accidents and asset losses. Information about production and shipping can be shared with partners and customers.
Amazon’s warehouses use IoT and robotics to optimize their systems, with humans working in tandem with interconnected robots. The approach of combining humans and tech has made Amazon the leader in warehousing. Other businesses are expected to follow their successful model moving forward. With the growth of Industry 4.0, the predictive maintenance market is expected to grow at a CAGR of 31 percent from 2021 to 2030.
Real-time data collection is a key benefit of digital factories and adopting IoT. The sensors on all factory assets collect large amounts of valuable data. This data can provide vital insights into factory performance.
Right now, only a small fraction of the data actually gets utilized in making decisions. These decisions may be related to changes in production, inventory levels, or forecasting. With cloud computing and big data, businesses can generate priceless insights from data.
IoT and CPS in a factory assure you have data from all the systems. Cloud computing converts that data into useful information. With visualization and correlation analysis, issues are identified and a hypothesis is created for causes. Solutions created to solve the issues are implemented to test the hypothesis. AI is used to calculate the impact of changes and an optimum range for parameters. This stream of data and advanced analytics helps decode complicated manufacturing processes and systems.
IIoT and Industry 4.0 are creating new opportunities and possibilities for the manufacturing industry. Digital factories are the future of manufacturing and early adopters will have an advantage. Still, it is important to remember that many of these technologies are constantly improving. Businesses have to plan in advance and strategize technology adoption to stay competitive.
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