Real-time monitoring helps you to optimize your products by eliminating overstock while stocking up with your most popular products. Real-time data can help you manage orders of mass customisation that are normally a slow and arduous process to fill. Customers want maximum customisation with maximum quality at maximum speed. Real-time data provides information about supplier inventory positions and order performance helping to make better decisions.
The combination of real-time data, predictive analytics, and machine learning can tell you ahead of time when a given machine will need maintenance or repair. This can seriously prolong the life of your equipment and machinery or just give you a bit more insight into how to improve them. Research conducted by LNS Research interviewed 400 manufacturing and industrial executives found that companies with real-time visibility of quality metrics in manufacturing outperform others by 6% in overall equipment effectiveness.
These manufacturers gained quicker insights and determined which areas of availability, performance, and quality were impacting performance most. The research found that manufacturers who rely on real-time data gain a significant competitive advantage over their peers.
Inaccurate and out-of-date schedules can cause delays and wreak havoc on production lines. This is completely avoidable with real-time data which can give you much more accurate fixed production times or machinery utilization rates.
Quick quotes win deals and manufacturers need real-time integration between pricing and quoting (CPQ) as well as selling and manufacturing systems to speed up pricing requests. Buyers are under immense pressure to make quick decisions and need fast quotes, competitive pricing, and up-to-date production. Real-time integration reduces the amount of time on manual data entry and improves accuracy.
Real-time data reduces the time it takes to do internal quality audits. This is particularly useful for manufacturers with stricter compliance requirements, like those involved in the production of medical devices. Faster, more frequent audits also let you know what you need to improve on to make your process better.
It can be daunting moving over to a new way of doing things but manufacturers have a lot to gain from moving to real-time analytics. Opportunities and relevant data are getting lost in a deluge of spreadsheets, reports, and schedules. In short, real-time data will save you time and money all while making you more competitive.
Depending on whether you are pursuing radical transformation as a matter of urgency or are looking at continuous improvement, you may want to build your data strategy to include real-time data, or pick a priority use case from the seven above and let it act as a demonstrator of the value real-time data can bring.
Industry 4.0 is characterised by a fusion of manufacturing and information technologies. This opens up whole new ways of organising and managing the entire value creation chain over the full product life cycle, and beyond. At its heart is a constant stream of relevant, real-time data. Once parties in the horizontal and vertical value creation chains are networked, the streams of real-time data mean they have instant access to the information they need to do their work effectively.
Knowledge is power, and this data can help production managers to powerfully increase the performance and productivity of their systems. Gradually, a company can start harnessing the power of big data, artificial intelligence and machine learning for approaches such as smart services and predictive maintenance to optimise their production lines.
At the heart of industry 4.0 are cyber-physical systems. A cyber-physical system can be a device, a piece of equipment or a production line. They contain intelligent sensors to monitor the conditions around them. This information is passed down the line, and the machines are able to self-optimise by adapting their performance to the specific job and operating conditions. When several previously standalone cyber-physical systems are integrated, this creates a cyber-physical production system (CPPS). All of this naturally requires access to a constant stream of real-time data. However, integrating the resources and systems involved in a CPPS can be done efficiently and safely with an integration platform such as the SEEBURGER BIS IoT solution.
However, for this to happen, the collected data must first flow into a data lake, where it is collated and prepared for big data analysis. One of the main challenges in the systematic use of real-time production data is how to merge disparate data sources into an optimal data stream to provide all your important systems with the right information at the right time.
In order to make decisions based on real-time data, production systems must be able to communicate with each other. Machine data can enter other systems in very different ways. Industrial integration, i.e. aggregating and logging industrial process data through integrating heterogeneous data streams, makes this possible. Modern machines have an OPC UA (Open Platform Communication) interface format. Many companies also use ODBC or radio via RFID to exchange data, depending on the application. Essentially, all the elements in a production process, from individual components to complete systems, are described using properties. The way these properties are described varies from language to language. These different descriptions need to be merged into a common format which obeys the communication standards in IoT and IIoT.
Manufacturers who continue to rely on manual reporting are missing out on the opportunities to improve their business and gain new customers that real-time data brings. The data gaps many see in their homegrown, legacy, and third-party systems create distrust. Shutting down all initiatives to improve using an enterprise production system is not the answer however. Configure, Price and Quote (CPQ) strategies are a great place to start with a real-time data pilot, as improvements in this selling process are measurable in improved revenue and reduced selling costs, and shortened sales cycles.
These smart factories are equipped with advanced sensors, embedded software and robotics that collect and analyze data and allow for better decision making. Even higher value is created when data from production operations is combined with operational data from ERP, supply chain, customer service and other enterprise systems to create whole new levels of visibility and insight from previously siloed information.
Developing smart factories provides an incredible opportunity for the manufacturing industry to enter the fourth industrial revolution. Analyzing the large amounts of big data collected from sensors on the factory floor ensures real-time visibility of manufacturing assets and can provide tools for performing predictive maintenance in order to minimize equipment downtime.
Using high-tech IoT devices in smart factories leads to higher productivity and improved quality. Replacing manual inspection business models with AI-powered visual insights reduces manufacturing errors and saves money and time. With minimal investment, quality control personnel can set up a smartphone connected to the cloud to monitor manufacturing processes from virtually anywhere. By applying machine learning algorithms, manufacturers can detect errors immediately, rather than at later stages when repair work is more expensive.
The third industrial revolution, which began in the middle of the 20th century, added computers, advanced telecommunications and data analysis to manufacturing processes. The digitization of factories began by embedding programmable logic controllers (PLCs) into machinery to help automate some processes and collect and share data.
The Internet of Things (IoT) is a key component of smart factories. Machines on the factory floor are equipped with sensors that feature an IP address that allows the machines to connect with other web-enabled devices. This mechanization and connectivity make it possible for large amounts of valuable data to be collected, analyzed and exchanged.
Cloud computing is a cornerstone of any Industry 4.0 strategy. Full realization of smart manufacturing demands connectivity and integration of engineering, supply chain, production, sales and distribution, and service. Cloud helps make that possible. In addition, the typically large amount of data being stored and analyzed can be processed more efficiently and cost-effectively with cloud. Cloud computing can also reduce startup costs for small- and medium-sized manufacturers who can right-size their needs and scale as their business grows.
AI and machine learning allow manufacturing companies to take full advantage of the volume of information generated not just on the factory floor, but across their business units, and even from partners and third-party sources. AI and machine learning can create insights providing visibility, predictability and automation of operations and business processes. For instance: Industrial machines are prone to breaking down during the production process. Using data collected from these assets can help businesses perform predictive maintenance based on machine learning algorithms, resulting in more uptime and higher efficiency.
The digital transformation offered by Industry 4.0 has allowed manufacturers to create digital twins that are virtual replicas of processes, production lines, factories and supply chains. A digital twin is created by pulling data from IoT sensors, devices, PLCs and other objects connected to the internet. Manufacturers can use digital twins to help increase productivity, improve workflows and design new products. By simulating a production process, for example, manufacturers can test changes to the process to find ways to minimize downtime or improve capacity.
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