The benefits of implementing AI in industry are also reflected in market trends.
Meticulous Research in its new report underlines that each year the artificial intelligence market in the industry will grow by approximately 40% and, by 2027, it will reach the value of 27,000 billion dollars. In addition, Mordor Intelligence estimated the volume of the AI market in the oil and gas industry in 2019 at $ 2 billion. Forecasts for the year 2025 will amount to 3,810 billion dollars, while annual growth will be 11%.
To improve its production processes, the startup Arkansas steelmaker Big River Steel merged with an AI consulting firm. Together they selected as a priority task forecasting demand through the use of machine learning models. The models have been trained with data from historical steel demand, macroeconomic indicators and data on customer activity. When planning the activity, the system takes into account the previously anticipated demand. These circumstances create the conditions to improve supply management and reduce inventory in the warehouse. In addition, the volatility in this industry makes new analytical approaches gain more potential.
Automotive companies can boast more experience in forecasting demand. This is demonstrated, for example, by Volkswagen AG Data: Lab Munich, which pioneered more than a hundred demand forecasting projects by product and region. The consulting firm Capgemini calculated that large producers of complex automotive components could increase their operating profit by up to 16% thanks to the active implementation of AI.
Predictive Layer offers the solution for predicting electrical power consumption. The company is dedicated to supplying a dynamic price generation engine that includes the analysis of demand and the elasticity of supply of the client company. As the company assures, they have managed to forecast the demand quite accurately even for the next day. The national electricity market is expected to save about $ 45 billion a year.
Like classic models, oil companies also use innovative models for forecasting electrical power consumption. As the 2019 report shows, classical interpretation models are even more preferable for annual statewide power consumption forecasts. In this case, a person more easily understand the relationship between consumption data and influencing factors. Although deep learning models may be more efficient, they are still rarely used.
Risk definition and forecasting services
Data in the form of tables and texts are the two main sources for using machine learning methods. In risk management, the text data is the failure reports, while the table data is the one that reflects their frequency.
Nowadays, forecasting services are very fashionable, however, their development requires high quality data and the reorganization of the entire system of maintenance and repair processes. Anomaly detection systems and forecasting services are important machine learning applications in the oil and gas industry. The use of this method favors the circulation of sensors. Detection of defects, for example in pumps, can occur at an early stage, preventing future losses.
The BCG project explains in a simple way the benefits of applying these approaches to data analysis. In combating dynamic equipment downtime and evaluating its potential, operating data is of great help. Process control opens up new possibilities for working with the weak points of production.
Artificial intelligence and production processes
The great variability is typical of complex processes. To generate a standardized approach to the management of technological processes, expert systems based on data analysis are generated. The use of these systems is a priority, in the first place, for the process industries. The first projects in this area can be seen in the mining and steel industry.
The NLMK example is very illustrative and shows the prognosis of a chemical when certain materials are added to it. In 2019, the chemical company Accenture presented the financial model for a standard company in the chemical sector. The company demonstrated how a company with revenues of 11.3 billion dollars a year is capable of increasing its profit by 10%. Technology company bitrefine came up with a solution for a wide range of tasks. It was shown that revenues can be increased by digitizing and optimizing production processes in various spheres: gas treatment, plastics production, chemical industry, etc.
Artificial intelligence and quality control
Decision support systems also include quality control. These help to monitor the properties of the manufactured products and determine the logic for the elimination of anomalies. In this case, the core of the system is also an AI that detects defects better than man, thanks to deep learning methods.
As an example we can name the BMW factory in Dingolfing: the order data and the photos of the car are analyzed by artificial intelligence. A similar system has also been installed by the Audi company. The company’s press center in Ingolstadt ensures that they are able to eliminate all losses caused by poor quality inspections.
Visual control can be used to check assembly, stamping, weld quality, and product geometry. Image analysis is done thanks to automatic learning, while calculations are provided by the system in real time.
Detection of anomalies in the early stages of the technological process
No one doubts that one of the key priorities of the industry is the safety of people: the elimination of risks for human beings and the delegation of dangerous tasks to machines. Artificial intelligence systems have an important quality to detect anomalies. The development of this possibility is one of the objectives of the industry. Production processes can be interrupted by events that are difficult for humans to detect through manual review.
The Ukrainian company Sciforce offers the example of a customer who wants to speed up the regular algorithms of data processing and increase the stability of the system. The company has created a business anomaly detection process that includes both detecting current anomalies as well as forecasting future ones. As a model for this detection, automatic coders were used and, for prognosis, recurrent neural networks were used. The models have been able to provide an accurate forecast for up to 10 minutes.
Popularity of new approaches
In recent years, neural networks and other machine learning concepts have been on the rise. These are already applied in many areas of economic activity, but have not yet reached heavy industry. These types of industries still employ traditional process control methods. When a company’s business model from the outset is built around high-quality data, it can deliver significant value. Another situation is observed with companies in the heavy industry that deal with physical assets. Therefore, these companies took a long time to consider the new approaches.
Most projects are still in the experimental stages. However, some large companies are already on the road to digitization. Among them are General Electric and Volkswagen Group. The strategies of these companies include a multitude of projects related to artificial intelligence. These projects are focused on getting a quick result from AI implementation: In the years to come, most companies that started digitization will face the significant challenges it presents.