Extensive development gives way to intensification of production – the use of more efficient technological processes and scientific and technical research. In the modern world, the Internet of things has become such a technology.
The Industrial Internet, or the more familiar name – the Industrial Internet of Things (IIoT), is an expanded understanding of the term “Internet of Things” (IoT) – a system of integrated computer networks and physical objects (“things”) equipped with built-in sensors, sensors, smart meters, other technologies that allow these objects to interact with each other, without human intervention.
Better planning and shorter lead times
Regulation of the production cycle depending on the demand for manufactured products is made possible thanks to machine learning methods and accumulated data. Such mathematical models make it possible to optimize production processes and avoid overproduction of products, overstocking of production warehouses, having established effective distribution between distribution channels.
For example, returns of unsold products from points of sale and, consequently, losses are an urgent problem for many manufacturers, especially if the goods have a short shelf life. By means of predictive analytics at one of the enterprises such returns were halved.
Increase equipment uptime and reduce downtime due to predictive maintenance
At the enterprises of the chemical industry there are a large number of various equipment. Using a continuous stream of data collected from sensors on production-critical equipment such as turbines, compressors, extruders, advanced analytics can determine patterns to predict the diagnosis of possible failures. Such “smart” equipment can send dispatchers messages about the required maintenance, possible failures, the need to order spare parts for proper operation, and even the schedule for their delivery. This allows you to switch from routine and incident repair to predictive maintenance of equipment.
For example, one of the major international chemical companies has repeatedly faced production downtime due to failure of the extruder. This happened more than 90 times a year and led to production losses, write-off of damaged raw materials and overtime work. Using real-time data collection from extruder sensors, as well as supplementing this information with unstructured data from maintenance records, test data, and other sources, the company developed a failure forecasting model that, evaluating cause-effect relationships, generated warnings and recommendations on extruder performance. The result was an 80 percent reduction in unplanned downtime and savings in operating costs — approximately $ 300,000 for each extruder.
Shorter production cycle
Manufacturing business process technologies have changed over the past decades. Operators do not go with logs and do not manually record the readings of analog controllers. Modern production facilities are equipped, as a rule, with dispatch systems for the collection and management of SCADA class. Modern technologies of real-time analytics and automation of process control actions allow you to combine physical and digital objects and generate forecasts, warnings, and instructions during the production cycle.
The technological process can vary depending on many factors: the quality and variation of the dosage of raw materials, temperature and atmospheric conditions, pollution, aging of the components. Predictive process analytics is similar to predictive maintenance of equipment and includes the collection of structured and unstructured data from process equipment sensors, from laboratory records, and alarm systems. The mathematical models trained on this data allow us to identify patterns and deviations in chemical processes before they occur, thereby reducing production risks or optimizing the production cycle.
Improving energy efficiency and lowering operating costs
The cost of electricity at any industrial enterprise is a significant amount of expenses in the income statement.
Although the chemical industry has a high degree of automation and most plants control such standard variables as temperature, heat fluxes, tank filling levels and pressure for optimal energy consumption, the Austrian company Borealis, a manufacturer of polyolefins, basic chemicals and fertilizers, uses machine methods training and builds a dynamic system of electricity consumption at the plant, taking into account external temperature, pollution of systems, aging catalyst etc.
Improving the quality of products and reducing deviations
As a rule, it takes a huge amount of time to post-process testing of samples, incurring losses due to processing of defective products or even batches.
Predictive analytics allows you to predict potential deviations during the production cycle and make minor adjustments to the process before the deviation affects the quality of the final product.
In world practice, there have already accumulated enough success stories of enterprises that have implemented IoT solutions and continue to develop them. Some of them have already allowed companies to feel a tangible effect from the implementation, some are at the implementation stage or pilot testing.
The industrial Internet is becoming a vehicle for changes that are being actively discussed in the framework of the term “industry 4.0,” introduced in Germany in 2011 and which laid the foundation for the development of the digital economy of this country. At the macro level, industry 4.0 implies the formation of cross-industrial ties, the openness of processes and the transition to the so-called shared economy – a shared economy. This will be possible due to the digitalization of industrial enterprises and the emergence of “smart” products with various built-in communication systems of such objects with each other or information systems. Machines with intelligent sensors that can inform digital production of the need for preventative repairs, or “smart” semi-finished products, which will convey the “knowledge” line to the production line about reconfiguring the technological cycle depending on the characteristics of a given semi-finished product are examples of the industrial Internet of things that are quite achievable in the near future. Of course, the stages of industry 4.0 will take place evolutionarily, but now enterprises are implementing innovative solutions from the field of the Internet of things.
A number of digital technologies
- Big data
- neuro-technologies and artificial intelligence;
- distributed registry systems;
- quantum technologies;
- new manufacturing technologies;
- Industrial Internet
- components of robotics and sensorics;
- wireless technology;
- virtual and augmented reality technologies.
industrial enterprises are taking the first steps towards the implementation of more complex IoT projects, namely:
- increase in the number of sensors for accumulating data from devices;
- increasing the capacity of servers to process more data;
- installation of network equipment to handle growing traffic;
- BI solutions, analytics using Big Data received from devices;
- remote management and service platforms;
- information security solutions for networks and data;
- data storage and archiving;
- integration solutions and consulting;
- compliance and implementation of regulatory requirements.
Making a decision about the need to invest in the automation of production and the technology of the Internet of things is a must for industry leaders. The early implementation of innovative business processes and practices will allow you to achieve a competitive advantage, future success and market conquest.