7 cases of using Big Data technologies in production
Habr, hello! To date, Big Data technologies have found their application in almost all industries: retail, banking, health care, and, in turn, the production sector is no exception. Optimizing the production chain, identifying defects and controlling product quality, improving the usability of a product based on consumer behavior is not a complete list of the results that can be achieved in the manufacturing sector thanks to Big Data. Consider several cases of foreign and domestic companies that have implemented big data technology in their activities.
The purpose of the implementation of Big Data: the use of data on consumer behavior to improve the design and usability of the product.
Apple has always been at the pinnacle of technological progress, so it is not surprising that it uses Big Data technology everywhere. Moreover, the company was initially in an advantageous position, not only due to the huge popularity of its products, but also because all Apple devices were literally created in order to collect valuable information. Now Apple has a huge amount of data on how we use the Iphone, Ipad and Macbook, and can draw conclusions about what should be the design and characteristics of the latest version of the device.
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Moreover, the approach to the design of applications is fundamentally changing: now the application does not dictate to the user the conditions of its use, but consumers let the company know what needs to be corrected in the new version.
The culmination of the successful use of Big Data by Apple is the distribution of Apple Watch, because now the possibilities of collecting data about the user are almost limitless: the company knows that the watch carrier ate, how many steps it took, what temperature it has. At the moment, these data are mainly used to improve the health of consumers.
IBM has also expressed interest in this technology and has entered into a partnership agreement with Apple. Together, companies are going to use Big Data to prevent the spread of disease, their treatment and prevention.
The result: increased loyalty and customer satisfaction.
The goal of implementing Big Data: minimizing production downtime.
General Electric Oil & Gas is one of the divisions of the diversified corporation GE, which manufactures high-tech equipment for the oil and gas sector. In conditions when energy prices are falling, and a day of “idleness” can cost as much as $ 7 million, it is vital to minimize the time for an unplanned production shutdown, it is necessary to increase the efficiency of oil production.
Thanks to sensors installed on the sold equipment, analysts of the company receive operational information about the state of oil production, and for analyzing the data in 2012, the cloud platform Predix was developed, which, using machine learning algorithms, allowed engineers to schedule diagnostic checks, improve the efficiency of equipment and equipment use. reduce downtime by identifying possible faults before they occur.
The result: an increase in annual energy production and reduced losses from inefficient use of equipment.
The purpose of the implementation of Big Data: optimization of the production chain.
Nestlé is a Swiss company, the world's largest food producer. For Nestlé factories it is very important to maintain accuracy in supply planning, since any discrepancy in time or quantity of raw materials purchased may lead to a delay in production and unmet demand. Davis Wu, a sales manager, says: “We need to plan deliveries more precisely, so that our products are as fresh as possible when they hit the stores”.
That is why the company turned to SAS for the joint implementation of SAS Forecast Server , which, using sales data for previous periods and optimization algorithms, automatically determines the demand for materials and forms logistics supply chains.
The result: a decrease in error in forecasting the demand for materials by half, a reduction in losses from the storage of excess working capital, losses from delays in production, etc.
The purpose of implementing Big Data: reducing production costs.
Intel is engaged in the production of computer components, in particular, microprocessors, each of which, before entering the market, must pass about 19,000 tests. By analyzing the data for the entire production process, the analytical platform is able to identify which tests will not be required, leaving only a fraction of the necessary checks. Thus, the time of testing microprocessors, as well as the cost of testing, has significantly decreased.
The result: savings of $ 3 million on a single line of Intel Core processors. By increasing the use of Big Data technology in production, the company expects to save another $ 30 million.
The purpose of the implementation of Big Data: optimization of materials costs in the production of steel.
OJSC Magnitogorsk Iron and Steel Works is one of the world's largest steel producers and occupies a leading position among the enterprises of ferrous metallurgy in Russia. At the end of June 2016, a reference service from Yandex Data Factory, the Sniper , was introduced into pilot production, which is intended to optimize the consumption of ferroalloys and additional materials in steel production. The analytical platform processes the smelting parameters: data on the initial composition and mass of the charge, the requirements for the content of chemical elements in the finished steel and others, and then issues the appropriate recommendations.
Result: preliminary testing of the service showed that the savings in its use averaged 5% or 275 million rubles a year.
The purpose of implementing Big Data: identifying the causes of equipment failure.
Gazprom Neft together with Teradata (an American company specializes in software and hardware systems for data processing and analysis) implemented a project for introducing predictive analytics into the processes of electric centrifugal pumps. The goal of the project, the integration of which was completed in August 2015, was to identify the causes of the failure of the automatic restart of the pumps after an emergency power outage. In the course of the analysis, more than 200 million records were used from controllers of control systems at 1649 wells and, as a result, visualized models of event chains were created that affect the self-starting of pumps and the maps of probabilistic distribution of cause-effect relationships.
Result: obtaining information about previously unknown relationships in the operation of pumping equipment and troubleshooting.
The purpose of the implementation of Big Data: optimization of business processes, reducing the time to prepare reports and data processing.
Surgutneftegaz is one of the largest enterprises in the Russian oil and gas industry, the first of the Russian companies in 2012 to switch to SAP HANA , the in-memory data and application platform for doing business in real time. As a result, the introduction of this platform has led to large-scale changes in the business processes of the campaign. The developers were able to automate production accounting, calculating sliding prices online, providing specialists with the most up-to-date information, while SAP HANA performs requests that were previously processed for several hours in a few seconds. Also, there is a significant saving in hardware resources due to the above in-memory computing, in which the main data storage is the central memory of the server, providing significantly higher speed of operations than individual disks, as well as linear scalability, allowing parallel processing of user requests in the RAM of all servers.
Result: A significant increase in the efficiency of business processes in the company.
On September 21, the Big Data Specialist program starts; if you prepay before May 21, you will receive a 15% discount on tuition.
Foreign companies
1. Apple
The purpose of the implementation of Big Data: the use of data on consumer behavior to improve the design and usability of the product.
Apple has always been at the pinnacle of technological progress, so it is not surprising that it uses Big Data technology everywhere. Moreover, the company was initially in an advantageous position, not only due to the huge popularity of its products, but also because all Apple devices were literally created in order to collect valuable information. Now Apple has a huge amount of data on how we use the Iphone, Ipad and Macbook, and can draw conclusions about what should be the design and characteristics of the latest version of the device.
')
Moreover, the approach to the design of applications is fundamentally changing: now the application does not dictate to the user the conditions of its use, but consumers let the company know what needs to be corrected in the new version.
The culmination of the successful use of Big Data by Apple is the distribution of Apple Watch, because now the possibilities of collecting data about the user are almost limitless: the company knows that the watch carrier ate, how many steps it took, what temperature it has. At the moment, these data are mainly used to improve the health of consumers.
IBM has also expressed interest in this technology and has entered into a partnership agreement with Apple. Together, companies are going to use Big Data to prevent the spread of disease, their treatment and prevention.
The result: increased loyalty and customer satisfaction.
2. GE Oil & Gas
The goal of implementing Big Data: minimizing production downtime.
General Electric Oil & Gas is one of the divisions of the diversified corporation GE, which manufactures high-tech equipment for the oil and gas sector. In conditions when energy prices are falling, and a day of “idleness” can cost as much as $ 7 million, it is vital to minimize the time for an unplanned production shutdown, it is necessary to increase the efficiency of oil production.
Thanks to sensors installed on the sold equipment, analysts of the company receive operational information about the state of oil production, and for analyzing the data in 2012, the cloud platform Predix was developed, which, using machine learning algorithms, allowed engineers to schedule diagnostic checks, improve the efficiency of equipment and equipment use. reduce downtime by identifying possible faults before they occur.
The result: an increase in annual energy production and reduced losses from inefficient use of equipment.
3. Nestlé
The purpose of the implementation of Big Data: optimization of the production chain.
Nestlé is a Swiss company, the world's largest food producer. For Nestlé factories it is very important to maintain accuracy in supply planning, since any discrepancy in time or quantity of raw materials purchased may lead to a delay in production and unmet demand. Davis Wu, a sales manager, says: “We need to plan deliveries more precisely, so that our products are as fresh as possible when they hit the stores”.
That is why the company turned to SAS for the joint implementation of SAS Forecast Server , which, using sales data for previous periods and optimization algorithms, automatically determines the demand for materials and forms logistics supply chains.
The result: a decrease in error in forecasting the demand for materials by half, a reduction in losses from the storage of excess working capital, losses from delays in production, etc.
4. Intel
The purpose of implementing Big Data: reducing production costs.
Intel is engaged in the production of computer components, in particular, microprocessors, each of which, before entering the market, must pass about 19,000 tests. By analyzing the data for the entire production process, the analytical platform is able to identify which tests will not be required, leaving only a fraction of the necessary checks. Thus, the time of testing microprocessors, as well as the cost of testing, has significantly decreased.
The result: savings of $ 3 million on a single line of Intel Core processors. By increasing the use of Big Data technology in production, the company expects to save another $ 30 million.
Domestic companies
1. Magnitogorsk Iron and Steel Works (MMK)
The purpose of the implementation of Big Data: optimization of materials costs in the production of steel.
OJSC Magnitogorsk Iron and Steel Works is one of the world's largest steel producers and occupies a leading position among the enterprises of ferrous metallurgy in Russia. At the end of June 2016, a reference service from Yandex Data Factory, the Sniper , was introduced into pilot production, which is intended to optimize the consumption of ferroalloys and additional materials in steel production. The analytical platform processes the smelting parameters: data on the initial composition and mass of the charge, the requirements for the content of chemical elements in the finished steel and others, and then issues the appropriate recommendations.
Result: preliminary testing of the service showed that the savings in its use averaged 5% or 275 million rubles a year.
2. Gazprom Neft
The purpose of implementing Big Data: identifying the causes of equipment failure.
Gazprom Neft together with Teradata (an American company specializes in software and hardware systems for data processing and analysis) implemented a project for introducing predictive analytics into the processes of electric centrifugal pumps. The goal of the project, the integration of which was completed in August 2015, was to identify the causes of the failure of the automatic restart of the pumps after an emergency power outage. In the course of the analysis, more than 200 million records were used from controllers of control systems at 1649 wells and, as a result, visualized models of event chains were created that affect the self-starting of pumps and the maps of probabilistic distribution of cause-effect relationships.
Result: obtaining information about previously unknown relationships in the operation of pumping equipment and troubleshooting.
3. Surgutneftegaz
The purpose of the implementation of Big Data: optimization of business processes, reducing the time to prepare reports and data processing.
Surgutneftegaz is one of the largest enterprises in the Russian oil and gas industry, the first of the Russian companies in 2012 to switch to SAP HANA , the in-memory data and application platform for doing business in real time. As a result, the introduction of this platform has led to large-scale changes in the business processes of the campaign. The developers were able to automate production accounting, calculating sliding prices online, providing specialists with the most up-to-date information, while SAP HANA performs requests that were previously processed for several hours in a few seconds. Also, there is a significant saving in hardware resources due to the above in-memory computing, in which the main data storage is the central memory of the server, providing significantly higher speed of operations than individual disks, as well as linear scalability, allowing parallel processing of user requests in the RAM of all servers.
Result: A significant increase in the efficiency of business processes in the company.
On September 21, the Big Data Specialist program starts; if you prepay before May 21, you will receive a 15% discount on tuition.
Source: https://habr.com/ru/post/325550/
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