Please use this identifier to cite or link to this item:
https://repository.iimb.ac.in/handle/2074/19894
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | De, Rahul | |
dc.contributor.author | Dixit, Ankur | |
dc.contributor.author | Pathak, Harshita | |
dc.date.accessioned | 2021-06-18T14:19:24Z | - |
dc.date.available | 2021-06-18T14:19:24Z | - |
dc.date.issued | 2019 | |
dc.identifier.uri | https://repository.iimb.ac.in/handle/2074/19894 | - |
dc.description.abstract | A digital twin combines the power of Analytics, human expertise to build models that aid humans in decision making. The digital twin has been adopted in industries to bring more predictability in the system and thereby making them more reliable and efficient. Further, digital twins have been used to reduce risks and costs associated with downtimes. In a recent article by Deloitte1 , it states that Digital Twin may enable companies to enhance the rate of product and process development. The market for digital twin is expected to reach $16 Billion by 2023, growing at an annual rate of 38%. The drivers for this growth are predominantly cloud computing, advanced machine learning techniques and growing analytics and simulation software. | |
dc.publisher | Indian Institute of Management Bangalore | |
dc.relation.ispartofseries | PGP_CCS_P19_025 | |
dc.subject | Digital twin | |
dc.subject | Healthcare industry | |
dc.subject | Decision making | |
dc.title | Digital twin in healthcare | |
dc.type | CCS Project Report-PGP | |
dc.pages | 18p. | |
Appears in Collections: | 2019 |
Files in This Item:
File | Size | Format | |
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PGP_CCS_P19_025.pdf | 822.62 kB | Adobe PDF | View/Open Request a copy |
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