| dc.contributor.author | Mišić, Velibor V | |
| dc.contributor.author | Perakis, Georgia | |
| dc.date.accessioned | 2021-10-27T20:34:39Z | |
| dc.date.available | 2021-10-27T20:34:39Z | |
| dc.date.issued | 2020 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/136275 | |
| dc.description.abstract | © 2019 INFORMS Research in operations management has traditionally focused on models for understanding, mostly at a strategic level, how firms should operate. Spurred by the growing availability of data and recent advances in machine learning and optimization methodologies, there has been an increasing application of data analytics to problems in operations management. In this paper, we review recent applications of data analytics to operations management in three major areas—supply chain management, revenue management, and healthcare operations—and highlight some exciting directions for the future. | |
| dc.language.iso | en | |
| dc.publisher | Institute for Operations Research and the Management Sciences (INFORMS) | |
| dc.relation.isversionof | 10.1287/MSOM.2019.0805 | |
| dc.rights | Creative Commons Attribution-Noncommercial-Share Alike | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | |
| dc.source | SSRN | |
| dc.title | Data Analytics in Operations Management: A Review | |
| dc.type | Article | |
| dc.contributor.department | Sloan School of Management | |
| dc.relation.journal | Manufacturing and Service Operations Management | |
| dc.eprint.version | Original manuscript | |
| dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
| eprint.status | http://purl.org/eprint/status/NonPeerReviewed | |
| dc.date.updated | 2021-04-09T16:26:11Z | |
| dspace.orderedauthors | Mišić, VV; Perakis, G | |
| dspace.date.submission | 2021-04-09T16:26:12Z | |
| mit.journal.volume | 22 | |
| mit.journal.issue | 1 | |
| mit.license | OPEN_ACCESS_POLICY | |
| mit.metadata.status | Authority Work and Publication Information Needed | |
