Enabling real time big data solutions for manufacturing at scale
Abstract Today we create and collect more data than we have in the past. All this data comes from different sources, including social media platforms, our phones and computers, healthcare gadgets and wearable technology, scientific instruments, financial institutions, the manufacturing industry, new...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-12-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-022-00672-6 |
| _version_ | 1811291932025946112 |
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| author | Altan Cakir Özgün Akın Halil Faruk Deniz Ali Yılmaz |
| author_facet | Altan Cakir Özgün Akın Halil Faruk Deniz Ali Yılmaz |
| author_sort | Altan Cakir |
| collection | DOAJ |
| description | Abstract Today we create and collect more data than we have in the past. All this data comes from different sources, including social media platforms, our phones and computers, healthcare gadgets and wearable technology, scientific instruments, financial institutions, the manufacturing industry, news channels, and more. When these data are analyzed in a real-time nature, it offers businesses the opportunity to take quick action in business-development processes (B2B, B2C), gain a different perspective, and better understand applications, creating new opportunities. While changing their sales and marketing strategies, businesses are now able to manage the data they collect in real-time to transform themselves, to record them in a healthy way, to analyze and evaluate data-based processes, and to determine their digital transformation roadmaps, their interactions with their customers, sectoral diffraction, application, and analysis. They want to accelerate the transformation processes within the technology triangle. Thus, big data, recently called as small and wide data, is at the center of everything and becomes an important application for digital transformation. Digital transformation helps companies embrace change and stay competitive in an increasingly digital world. The value of big data in manufacturing, independent from sectoral variations, comes from its ability to combine both in an organization’s efforts to both digitize and automate its end-to-end business operations. In this study, the current digitalization and automation applications of one of the plastic injection-based manufacturing companies at scale will be discussed. Presented open-source-based big data analytics platform, DataCone, that increases data processing efficiency, storage optimization, encourages innovation for real time monitorization and analytics, and support new business models in different industry segments will be demonstrated and discussed. Thus, development and applied ML solutions will be discussed providing important prospects for the future. |
| first_indexed | 2024-04-13T04:37:40Z |
| format | Article |
| id | doaj.art-490c8c81e0cf4bce966f7dcaeaaefcd6 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-04-13T04:37:40Z |
| publishDate | 2022-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-490c8c81e0cf4bce966f7dcaeaaefcd62022-12-22T03:02:07ZengSpringerOpenJournal of Big Data2196-11152022-12-019112410.1186/s40537-022-00672-6Enabling real time big data solutions for manufacturing at scaleAltan Cakir0Özgün Akın1Halil Faruk Deniz2Ali Yılmaz3Istanbul Technical University Artificial Intelligence, Data Science Research and Application Center, Istanbul Technical UniversityParton Big Data Analytics and ConsultingParton Big Data Analytics and ConsultingBig Data and Business Analytics, Department of Data Engineering and Business Analytics, Istanbul Technical UniversityAbstract Today we create and collect more data than we have in the past. All this data comes from different sources, including social media platforms, our phones and computers, healthcare gadgets and wearable technology, scientific instruments, financial institutions, the manufacturing industry, news channels, and more. When these data are analyzed in a real-time nature, it offers businesses the opportunity to take quick action in business-development processes (B2B, B2C), gain a different perspective, and better understand applications, creating new opportunities. While changing their sales and marketing strategies, businesses are now able to manage the data they collect in real-time to transform themselves, to record them in a healthy way, to analyze and evaluate data-based processes, and to determine their digital transformation roadmaps, their interactions with their customers, sectoral diffraction, application, and analysis. They want to accelerate the transformation processes within the technology triangle. Thus, big data, recently called as small and wide data, is at the center of everything and becomes an important application for digital transformation. Digital transformation helps companies embrace change and stay competitive in an increasingly digital world. The value of big data in manufacturing, independent from sectoral variations, comes from its ability to combine both in an organization’s efforts to both digitize and automate its end-to-end business operations. In this study, the current digitalization and automation applications of one of the plastic injection-based manufacturing companies at scale will be discussed. Presented open-source-based big data analytics platform, DataCone, that increases data processing efficiency, storage optimization, encourages innovation for real time monitorization and analytics, and support new business models in different industry segments will be demonstrated and discussed. Thus, development and applied ML solutions will be discussed providing important prospects for the future.https://doi.org/10.1186/s40537-022-00672-6Big dataDigital transformationReal-time learningMachine learningManufacturingOpen source |
| spellingShingle | Altan Cakir Özgün Akın Halil Faruk Deniz Ali Yılmaz Enabling real time big data solutions for manufacturing at scale Journal of Big Data Big data Digital transformation Real-time learning Machine learning Manufacturing Open source |
| title | Enabling real time big data solutions for manufacturing at scale |
| title_full | Enabling real time big data solutions for manufacturing at scale |
| title_fullStr | Enabling real time big data solutions for manufacturing at scale |
| title_full_unstemmed | Enabling real time big data solutions for manufacturing at scale |
| title_short | Enabling real time big data solutions for manufacturing at scale |
| title_sort | enabling real time big data solutions for manufacturing at scale |
| topic | Big data Digital transformation Real-time learning Machine learning Manufacturing Open source |
| url | https://doi.org/10.1186/s40537-022-00672-6 |
| work_keys_str_mv | AT altancakir enablingrealtimebigdatasolutionsformanufacturingatscale AT ozgunakın enablingrealtimebigdatasolutionsformanufacturingatscale AT halilfarukdeniz enablingrealtimebigdatasolutionsformanufacturingatscale AT aliyılmaz enablingrealtimebigdatasolutionsformanufacturingatscale |